Decision Support for Infrastructure Management of Public Institutions

Abstract

The management of public institutions is focused not only on providing and improving public services but also on managing the physical infrastructure that these institutions use—buildings for provision of such services. The focus of this paper is on decision support to the management of individual buildings and the set of such buildings (portfolio) during the planning phase. More precisely, it is directed towards support towards both the decision-maker (DM) and decision-making process (DMP) when planning construction activities/projects such as maintenance, renovation, reconstruction, extension, construction, design/preparation of project-technical documentation, etc. The aforementioned DMP includes the processing of a large amount of diverse data (technical, economic, social, etc.) expressed differently—numerically or descriptively, as well as in different units of measurement, simultaneously taking into account the different wishes and attitudes of stakeholders (consequently meeting their often conflicting goals and criteria). The above indicates that it is a complex and ill-defined multi-criteria problem faced by the DM/planner. On top of that, and knowing that the DM usually does not have all the necessary knowledge and skills, this paper proposes how to overcome these issues by supporting the DM within the DMP during such a planning process. The proposed concept promotes an integral (considering relevant aspects of this management problem) and inclusive (taking into account the views of relevant stakeholders) approach to managing complex construction projects and their portfolios. It is methodologically based on the logic of decision support systems and multi-criteria analysis. The multi-criteria methods used include the Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE) for the evaluation and comparison of alternatives in an integral manner, as well as the Analytic Hierarchy Process (AHP) for determining the weights of criteria and achieving an inclusive and consistent approach to relevant stakeholders (based on the goal tree approach). The concept was tested on the planning of infrastructure management at a university in the Republic of Croatia, and it was proven to be useful because it provided the DM with a basis for decision making. The usefulness of the concept was confirmed by the concordance of the plan obtained using the concept and the activities/projects actually realized.

1. Introduction

Management of public institution infrastructure (MPII) is an important and complex topic encompassing a wide range of activities. Confirmation of the importance of the chosen research topic was found in an extreme example from paper [1], in which the poor management of public infrastructure in Nigeria is set as the key problem of MPII side by side with the problems of lack of political and institutional frameworks, low transparency, and high corruption. More will be said about complexity later. It must be emphasized that this research focuses only on building management and related construction project management within it. Viewed exclusively from the perspective of management theory, specifically managerial functions, the research pertains to one out of five (planning, organizing, stuffing, directing, and controlling) [2] managerial functions. Namely, the part of management in focus in this paper is the planning function and, within it, the decision-making process, which is crucial for planning. If we move the focus of research from general management definitions to the specifics of infrastructure management, the subject of this research is the civil engineering part of the infrastructure, specifically the management of the buildings with all their constituent parts. The subject of the research can then be defined more precisely: management of various individual construction projects as well as management of a set of construction projects. Herein, management refers to all phases of the lifecycle of a construction project—from the inception of the idea, through to designing and construction, the useful utilization and maintenance of the building, all the way to the end of its existence—removal of the building (either partially or completely). For the purposes of this research, a building is defined as a physical immovable creation of human labor, produced using equipment/machinery and materials, which, besides the construction part (both load-bearing and non-load-bearing parts), consists of mechanical, electrical, and other installation systems and devices permanently connected to the building. Herein, construction projects should be understood as the undertaking of multiple activities (related to these buildings and construction activities) that lead to desired changes and achieving desired goals related to these buildings—like their improvement or sustainable usage of those buildings, etc. Construction projects and their realization should also be considered in the context of limited available resources—from financial to material, but also taking into account intellectual resources of DM and users as well.

Confirmation that infrastructure contributes to the realization of the functions of public institutions (especially those related to education) is presented in the paper [3], which focuses on studying the balance between the asset and maintenance management strategies and the funding model through conducting a state-of-the-art literature review and qualitative analysis in the context of public schools in Australia and other developed countries around the world. Although different maintenance strategies were used in school infrastructure management, adequate robust asset management plans (AMPs) were identified within paper [3] as the most important/key factor for success besides adequate funding. So, it is easy to conclude from all the abovementioned that within the MPII, special attention should be directed to the management plan and its design. Therefore, the design of an Infrastructure Management Plan for public institutions and the support to decision-makers during the establishment of such a plan is set as the main research problem in this research paper.

In the continuation of the Introduction, the origin of the research presented in this paper as well as relevant literature references are presented.

1.1. Introduction of Relevant MPII Contexts

For a better understanding of the research presented in this paper, its fundamental determinants are identified and explained below, namely, determination of public institution infrastructure (PII) in general and within this research (as a system composed of several elements with numerous and varied characteristics), identification of stakeholders, and their influence on managerial decision making within MPII.

1.1.1. Public Institutions

Defining the specific area of research presented within this paper in terms of narrowing down the research subject, focusing it on public institutions, requires a basic explanation of what these institutions are. For this reason, it is necessary to start by noting that the concept of institutiones (Latin) according to the Croatian Encyclopedia [4] first appeared in Roman times as a term for an introductory textbook in a certain discipline. In the field of law, the most famous examples are Gaius’s and Justinian’s Institutes [5]. In sociology, an institution represents a system of norms that determines the ways of solving key problems encountered by members of society and ensures that fundamental social activities are carried out in a continuous, standardized, and predictable manner. The two previously mentioned definitions point to the etymology of the term “institution” and provide insight into its broader scope, thereby complementing the third definition, also from the same source, which refers to the concept of an institution in the sense in which it is used in this research: “An institution as a legal entity intended for the permanent conduct of non-profit activities (e.g., healthcare, social care, culture, education, science)”.

If the adjective “public” is added to this concept, the meaning of this “compound” (public institutions) even more precisely defines the scope of this research, as it refers to an institution open to all users who have needs for its services or products. To be more precise, a public institution is an organization that is established, funded, and operated by the government to serve the public interest. These institutions typically provide essential services or functions that are considered important for the well-being of society, such as education, healthcare, social services, law enforcement, and public administration. Public institutions are generally non-profit entities and are accessible to all members of the public. Their operations and activities are often regulated by law to ensure accountability, transparency, and the equitable distribution of resources. Examples of public institutions include public schools, universities, hospitals, government agencies, and libraries. At this point, it is necessary to define more precisely that this work encompasses the management processes of these institutions, specifically one part of them—infrastructure management. An important aspect of infrastructure management is finance, and therefore, it is necessary to emphasize that public institutions can be financed by public and/or private funds, which significantly influences the way they are managed and the associated planning and decision-making processes as stated before. In institutions established by the state and financed with public money, managerial freedoms are significantly restricted/regulated. One of the reasons for this, in the author’s opinion and according to his experience, is the need to protect them from greater risks that private institutions may take on, as well as the need to ensure and to maintain the continuous provision of services or products that public institutions offer.

1.1.2. PII—System of Elements with Various Characteristics

Another important aspect of defining management is the selection of the scope of elements to which management pertains—their quantity and interrelationships. Infrastructure of public institutions is proposed here to be viewed as a system composed of multiple elements, each of which is a functional system in itself—this approach is based on General Systems Theory [6]. Applying this approach to the construction part of public institutions’ infrastructure, i.e., to the part that relates to the buildings, it can be said that the infrastructural system of public institutions is made up of one or more buildings, each of which is a unique new system composed of multiple parts—functional and structural units that together form this new system—the building. Such parts of the new system are, for example, structure or its parts defined by expansion joints, floors, rooms, ventilation, heating and cooling systems, electrical and water supply systems of the building, and so on, depending on the focus of management. Knowing this, the complexity and diversity of possible divisions and approaches to systematizing these system elements and systems themselves are easily noticeable. Moreover, the quantity of diverse characteristics that need to be considered when analyzing the system itself and its elements with all of its attributes, as well as the related decision-making processes during infrastructure management planning, is apparent. These characteristics/attributes can be economic, technical, social, ecological, cultural, etc. The characteristics/attributes are also expressed in various ways, such as numerical or descriptive, or can be expressed in different measurement units or scales. For example, some of them can be expressed as monetary values (e.g., Euros, Dollars, etc.), some as areas (square meters) or length (meters), while others can be expressed as expert assessment (e.g., rating from 1 to 10 or rating as bad, neutral, good, etc.) or can be expressed with a simple yes or no answer (binary approach 1 or 0). This further leads to the complexity of describing the state of the entire system or any of its individual elements, as management requires both such approaches. Namely, without understanding the state of individual elements according to a set of relevant characteristics, the state of the entire system (of a set of elements) according to the same set of criteria cannot be understood and finally cannot be assessed in a proper manner.

1.1.3. Stakeholders and Decision Making Within MPII

All the aforementioned, although representing a very complex set of characteristics that need to be considered during such management, can be adequately described given the sufficient information, effort, patience, knowledge, and systemacity of the infrastructure manager. This part of the planning problem can be reliably described sufficiently well, but that is not all. There is also another managerial aspect, which is the most complex due to its uncertainty and variability. This aspect arises from stakeholders’ views on the planning problem because, without stakeholders, planning has no meaning. How many stakeholders are sufficient and necessary is another question. Ideally, the selected stakeholders would cover all relevant aspects of the problem. Thus, we can talk about stakeholders of different levels of responsibility and permitted scope of decision making, but also of different reasons of stakeholders’ interest about the problem (e.g., users or employees—professors, assistants, other staff, etc.) and their expertise in certain aspects of the selected problem (e.g., civil engineers, etc.). Special attention should be paid to the main decision-maker regarding the planning decisions—usually it is the legal representative of the public institution. Their level of knowledge and ability to grasp complex issues is one of the limitations that is entirely justified and expected, so the problem needs to be presented to them in a way that is understandable to them but in the same time necessary and sufficient for quality decision making.

Therefore, it is necessary to prepare a decision-making basis by a relevant group of stakeholders, not just for experts in construction and/or economic aspects, which is common practice in this field. Additionally, it is important to define the process of preparing the decision-making basis, making it understandable and acceptable not only to the decision-maker but also to all other stakeholders. It is important to maintain the possibility of final decision making by the actual and formally responsible person or body, as well as to always remember that the process, methodology, or concept used supports decision making but does not replaces the decision-maker. Hence, it is about preparing the basis for decision making, not making the decision itself or deciding. It is also important to mention other stakeholders who should be selected in a representative manner concerning the analyzed planning problem. Different views of the same planning problem due to different stakeholder interests are further complicated when the need to ensure consistency in evaluation is considered, even at the individual stakeholder level, not to mention the consistency of group decision making. Therefore, it is essential to find an approach that minimizes the impact of inconsistency, which is characteristic of complex decision-making processes, especially in group decision making, to an acceptable level while ensuring objectivity and consideration of all relevant stakeholders’ views and opinions. More on the role of stakeholders in infrastructure project management and construction projects in general can be found in [7,8,9,10,11], and the selected approach and methods will be explained later in the paper.

All of this leads to the conclusion that the complexity of infrastructure projects results from their large scope and complex structure, a large amount of required material and human resources, and a large number of stakeholders with different interests and goals, which is why the management of infrastructure construction or maintenance projects often represents a great challenge [12].

1.2. Research Context

The focus of this paper is precisely on public institutions that are entirely financed by public funds. Such an institution is a public university because it is owned and operated by the state government. It provides education to the public and is funded by taxpayers’ money. Management of their infrastructure, that is, research infrastructure (RI) and core facilities (CF), is very important because they are powerful drivers for generating research results and thus knowledge at universities [13]. In their work, Jurgens, A. et al. [13] provide insight into the various features of RI and CF, their organizational structure and management, funding mechanisms, key success factors, and challenges and opportunities associated with the implementation and operation of these research support structures. Bearing in mind what was stated in that paper [13] and using the author’s personal experience, it can be concluded that the organizational structure of universities can vary, which also impacts the management of infrastructure; they can be organizationally centralized or decentralized or mixed, i.e., non-integrated (composed of other legal entities such as faculties, through which they conduct their activities) or integrated (where the university is a single legal entity that carries out its activities through organizational units—departments). The mixed organizational structures of universities are those in which part of the activity is carried out through organizational units that are centrally managed (e.g., departments) while the remaining part is carried out through organizational units that are legal entities separate from the university. In Croatia, most of universities are decentralized (with faculties as separate legal entities); integrated universities do exist, but they are less common (it is regulated by the Law on Higher Education and Scientific Activity [14]). Decentralized management involves managing multiple individual components (e.g., faculties, centers, etc.) that have legal personality, ownership, and rights and obligations, which makes managerial processes more complex as they are based on the principle of a divisional organizational structure. Public universities in Croatia (integrated and non-integrated) are governed by a Senate, while executive functions are entrusted to the rector. Bodies such as the University Council or the Ministry oversee their work [14]. Usually, in Croatia but also all over the world, the rector is not necessarily a person with formal and practical management knowledge and skills as well as with, i.e., technical/engineering knowledge related to infrastructure (construction, electrical engineering, mechanical engineering, etc.). Rather, it is a university professor with high-quality expertise in a specific scientific field together with high levels of both local and international recognition. Usually, the rector’s management abilities generally stem from past work—informal learning and a set of personal knowledge, skills, and talents, except in cases where they also have formal education in management, which can enhance and systematize the aforementioned qualities. Therefore, it is clear that persons in such management positions (such as the rector) or governing bodies (such as the University Council) need help/support when dealing with infrastructure management problems. This paper aims to design support for managers with such specific management issues (especially in planning and decision making related to infrastructure management) who do not have formally and/or informally acquired management and/or construction knowledge and skills, as well as for those who do, because proposed concept can improve both their decision making, planning, entire work performances.

For the previous research thematically related to this study, it can be said that it consists of research/papers dealing with the subject matter of infrastructure management (during the planning or implementation of building infrastructure projects) but using different approaches from those previously presented and generally view the problem exclusively from a technical, economic, or social perspective. Alternatively, there are papers that use similar approaches to the one proposed but focus on a different subject matter—the subject of these papers is different from public institution infrastructure management. The most significant of these works are listed below. So, paper [15] illustrates how to empirically prioritize a set of projects by using a five-level project selection model. Conversely, the research presented within paper [16] is dedicated to the modelling of decision processes occurring during the implementation of construction projects. It is focused on the assessment of the robustness of the decisions regarding the possible changes during construction project implementation. In the context of addressing aspects beyond the technical and economic, paper [17] authored by Rosasco, P and Sdino, L. is interesting. Within it, the authors are dealing with the social sustainability of the infrastructures. According to their paper, the presence of infrastructure can have strong impacts on the environment and the living conditions of the population, but also it can be subject to actions related to contrast and opposition. Therefore, in parallel with the economic and environmental sustainability assessment, it is essential to decide whether or not to build new infrastructure. In addition, social sustainability is also pursued on the basis of an assessment that takes into account various aspects that relate the work to the population, also in order to identify the most satisfactory design solution. The assessment must identify suitable criteria that are capable of taking into account the various impacts generated by the infrastructure, not only of an economic and environmental type but also social. Their contribution deals with the identification of criteria for assessing the social sustainability of infrastructure projects. The goal of the paper [17] is to identify the useful criteria for assessing social sustainability and the weights attributed by the various parties involved in the decision-making process by citizens directly or indirectly affected by the infrastructure. Paper [18] deals with public sports infrastructure. It presents a study with the purpose of establishing user-centered local governance by improving the management structure for the advancement of public sports facilities using the Delphi method and hierarchical structure analysis method. In paper [19], the authors provided a new approach for the planning of an investment project in the field of building construction. The focus is in the shaping of the concept that serves to facilitate decision making about investments through providing support to investors when they are dealing with a problem selection of a solution for an investment project. The concept is based on the combined use of several different criteria, conventional methods for the evaluation of investment projects, and multicriteria methods (Analytic Hierarchy Process, AHP [20], and the Preference Ranking Organization METhod for Enrichment Evaluation, PROMETHEE [21]), but it does not deal with infrastructure facilities or projects. Unlike the previous paper, the following one [20] deals with infrastructure facilities and uses a similar approach to the one proposed in this study, but it focuses on proposing a methodology that supports the design process, specifically dealing with the selection of an appropriate technical solution for an infrastructure project or facility. Some other authors use multi-criteria methods such as AHP to support decision making on infrastructure management in construction, specifically in bridge maintenance management, which is evident in paper [22]. It deals with systematic research on quantitative assessment approaches for evaluating the overall technical status and selecting optimal replacement methods for existing BECIs (bridge expansion and contraction installation).

In addition to dealing with the selection of design-technical solutions as in paper [22], some authors also focus on the selection of policies to be applied to infrastructure management. For example is paper [23], which deals with the management of urban transport projects, with the planning phase within the urban-transport project management, which is a complex process from both the management and techno-economic aspects. The focus of research presented within paper [24] is on the decision-making processes related to the planning phase during the management of urban road infrastructure projects. The proposed concept is based on multicriteria methods and artificial neural networks.

1.3. Literature Review Findings and Research Contributions

From all the above, a clear and unequivocal conclusion emerges: it is necessary and important to establish a concept that supports decision making in the process of managing public institution buildings/facilities with a focus on planning. Except this, the proposed concept needs to address this poorly structured and complex problem described above and fill this research gap.

This is why it is herein proposed a way to establish an approach that can analyze the problem from multiple aspects, consider the views of relevant stakeholders who are often opposed, and process diverse data. It is a new integral and inclusive approach to this multi-criteria problem through a decision support concept (DSC) based on the multi-criteria methods and logic of decision support systems [25]. The contribution consists in shaping and connecting in unique process methodologies for ensuring both inclusiveness and integrality. Inclusiveness is ensured by the systematic inclusion of stakeholders and their views/opinions on the problem in a consistent manner using the simplified AHP method (on previously defined criteria and their relative importance/weights). Integrality is ensured by identifying and including relevant data in processes of evaluation and comparing alternatives according to defined adequate criteria using the PROMETHEE method. In doing so, the data are organized in a database and are processed by models from the model database. The models in the database are actually methods and approaches such as the aforementioned AHP and PROMETHEE or, e.g., the decision tree approach. The concept is presented in the following chapters below.

2. Methodology—The Decision Support Concept for Planning of Infrastructure Management of Public Institutions (DSC PIMPI)

In this chapter, the architecture of the Decision Support Concept for Planning of Infrastructure Management of Public Institutions (DSC PIMPI), its functioning, and the stakeholders involved will be presented. Its first part provides the architecture of the concept by describing all of its elements, the interrelationships of these elements, and the process of DSC PIMPI realization (application of the concept at the theoretical level). Its functioning is presented too. The employed methods and their purpose and results as well as the inputs required for their application are also described in its first part. In the second part, the stakeholders and their participation in the process of realization of DSC PIMPI are presented. Bearing in mind the basic scientific contribution of this paper—the proposal of a new methodology—it is necessary to clearly state to the reader that the focus in this chapter is on the description of the proposed methodology of the unique DSC in a realistic planning context, while the technical context of the known methods from which it is partly built is presented through references in this chapter as well as in the Introduction.

2.1. Architecture and Functioning of DSC PIMPI

The following Figure 1 shows the architecture of DSC PIMPI through the identification and definition of its main elements, while Figure 2 and the following text of this subchapter provides a description of DSC PIMPI functioning.

Figure 1 shows the architecture of the proposed DSC, which is based on the decision support system logic and MCDM application. It consists of tree modules (which are interconnected as shown in Figure 1), a data base module (DB), model base module (MB), and a central–implementation module. The interaction between these three basic elements is realized during the implementation of all parts of the DSC PIMPI, providing that various data related to infrastructure management are used from the database and stored in it (e.g., data on construction, materials, installations, plans, stakeholder attitudes, management goals, priority ranking, interrelationships between criteria and criteria weights, etc.); as well as that, the methods Goal Tree Method, PROMETHEE, and AHP are used for the transformation of those data. The realization of the DSC PIMPI is carried out in successive realization of three parts (first—Preparatory, second—Transformational, third—Fine Tuning and Resulting) to finally result in an infrastructure management plan.

The concept presented in the previous Figure 2 is based on the successive realization of multiple phases (several phases together form each one of previously mentioned three parts of the DSC PIMPI realization) where commonly the results of the previous phase(s) serve as input data necessary for the execution of the subsequent phase, but some phases (second and third) are conducted as two parallel activities. It all begins with the initial decision on the subject of management and continues with two parallel activities of the second phase: the analysis of the subject whose management planning is to be supported and the identification of relevant stakeholders, along with their grouping into several groups. The next, third phase involves generating planning objectives, which then guide the identification and definition of relevant evaluation criteria using the goal tree approach. Creating a goal tree streamlines the process of establishing supportive objectives for the main goal—sustainable management—and ultimately defining the criteria. While adding more hierarchical levels to the goal tree can lead to a more thorough analysis of the planning problem, it is important to avoid overelaboration, which could result in overlapping criteria that do not provide additional insights into the differences between alternative solutions. This approach allows the criteria to be adapted to each specific planning problem, but it is crucial to retain the main goal and the subgoals at the first hierarchical level of the goal tree. These elements ensure that all relevant aspects are considered, while further elaboration adapts the process to the specific planning problem at hand. Following this, the process moves on to activity devoted to generating a set of alternative solutions that will be evaluated based on these criteria. It is important to define these alternatives so that each one can be assessed against all the criteria. If needed, the criteria can be revisited and expanded during the generation of alternatives, creating a loop in the process where both activities of the third phase are repeated until a relevant set of alternatives (that can be fully evaluated according to all the criteria) is produced. Next, the fourth phase is devoted to determination of importance for each criterion. The relative importance of the criteria is determined using a simplified method of the Analytic Hierarchy Process (AHP) [20]. This method ensures that all relevant stakeholders are included and that decision making remains consistent. The AHP method in general involves pairwise comparisons of subgoals based on their importance (relative to the other subgoals at its own hierarchical level) in achieving a mutually shared (higher-level) goal, as shown in Figure 3 (e.g., MG—the mail goal is higher-level shared goal for MSG1—the mail subgoal 1, MSG2—the main subgoal 2, and MSG3—the mail subgoal 3, which are all its three subgoals).

The simplification of the AHP method here refers to a two-step comparison process. It is also necessary to refer to the nomenclature used. Namely, the names for the elements in the presented goal hierarchical structure correspond to the purpose they have in the proposed concept and are not the same as the usual names when applying the AHP method (e.g., such as those used in paper [22]). For ease of understanding, each term used here is accompanied by its common name in the AHP literature in parentheses. The first step is the common application of AHP, while in the second step, the AHP method is applied to a limited part of the goal hierarchy and by a precisely defined group (or groups) of stakeholders as explained below, meaning different groups of stakeholders evaluate individual parts of the hierarchical structure, whereby all parts of the hierarchical structure must be evaluated by at least one group of stakeholders or more. To be even more precise, the AHP method is applied to that part of the goal hierarchical structure only by one or more groups of stakeholders relevant to that part of the hierarchy—groups relevant to those goals and criteria. Sometimes it is just one group, and sometimes it is two or more. The aforementioned achieves a clearer influence of the expert group on the criteria (and their weights) relevant to it avoiding their influence on other criteria in the hierarchy. Furthermore, the process of comparing elements in the hierarchy is simplified and accelerated, thereby increasing consistency because there are fewer elements (SG) that need to be compared with each other in pairs, and this is only based on their influence on the realization of one directly superior element—MSG. First, only the subgoals at the second hierarchical level (main subgoals—MSG, which are commonly called criteria in the AHP literature) are compared with respect to the main goal (MG, which is commonly called the goal/subject in the AHP literature) by representatives of the main decision-maker or the responsible group. In the second step, subgoals at the third level (SG—which are commonly called indicator/index in the AHP literature) are compared with each other based on the second-level goals (e.g., SG1.1, SG1.2, SG1.3, and SG1.4 are compared with each other according to their contribution to achieving MSG1), with each stakeholder group conducting this process separately. Representatives of each stakeholder group compare only the third-level goals that support the second-level goal within their area of interest. Once all groups of representatives have completed their comparisons and assigned individual weights to the third-level goals, the compromised weight (mean weight) for each goal is calculated and used as follows. While the geometric mean might be more mathematically accurate, the arithmetic mean is used here for its greater ease of understanding/user-friendliness to stakeholders, and this choice will be explained in more detail in the following section. This approach based on the arithmetic mean is based on the author’s research experience, and its justified use has been demonstrated and confirmed through several papers [23,24]. It should also be pointed out how stakeholder biases are mitigated during the decision-making process. It is obvious that the biases are handled for two different cases. The first case is the one that appears at the level of a group of stakeholders, and the second is appearing between several groups of stakeholders who act in the application of AHP on the same part of the hierarchical structure. Bias within the group (the first case) was overcome by making unanimous decisions during the comparison, i.e., by reaching a common position—which is acceptable to everyone in the group through a defined reconciliation process or without it. Bias between stakeholder groups (the second case) is overcome by an approach that equally respects the opinion of each participating group. Equal respect was achieved in such a way that each group expressed its views individually, and from the thus-obtained values of importance for each element (SG), the final value of the importance of that SG was determined by arithmetic mean, which will later become and used be as the compromise weight of the criterion (C). In addition to determining the importance of the criteria, evaluation techniques for each criterion are established. The next step is to evaluate the alternative solutions according to all criteria, followed by priority ranking using the PROMETHEE method. To implement this method, it is necessary to determine how preferences are formed for each criterion—typically by selecting one of six basic preference functions [21]. After establishing the priority ranking of alternative solutions in the form of a goal function, constraints are applied using integer linear programming—the PROMETHEE V method [26]. These constraints usually incorporate provisions from higher-order plans and decisions or specific management decisions. The result is a set of alternative solutions proposed for implementation, which is then presented to the decision-maker as a higher quality basis for decision making. The outcome of this process is an infrastructure management plan proposal for a specific planning period. Once the concept is established for a particular management problem (for some particular public institution), it can also serve as a simulator for the decision-maker, allowing for fine-tuning or re-evaluation of their decisions. In the next planning phase, the process is repeated for alternative solutions that were not implemented in the previous plan and for new ones that have been generated or proposed in the meantime.

2.2. Stakeholders and Functioning of DSC PIMPI

The most crucial aspect of implementing any management process, including the planning and management of public institution infrastructure (buildings/facilities), is the proper and timely organization and involvement of stakeholders, along with monitoring their work and controlling the achievement of results in the required form and quality. Figure 1 not only illustrates the flow diagram of the DCS PIMPI implementation but also shows the participation of stakeholders in this process. Special attention should be paid now to the shading or outlining of elements in the flow diagram, as these indicate the specific phases of the DCS PIMPI process where particular groups of stakeholders are active/involved. Therefore, the following sections describe the stakeholders, their grouping, and their roles in the implementation of DCS PIMPI. In this case, decision support to infrastructure management of public institutions, the relevant stakeholders should be divided into four groups:

• Decision-Maker Representatives (DM): This group includes members of the administration responsible for infrastructure.
• Infrastructure Management Experts (E): This group consists of engineers and project managers in the construction sector, as well as economic experts within the construction industry.
• Users (U): This group includes both recipients and providers of public services who possess knowledge related to improving the quality of service delivery, particularly in enhancing the fulfilment of needs.
• DCS PIMPI Implementation Experts (EDSC): This group comprises experts in decision support, specifically those skilled in multi-criteria analysis. Notably, this group does not participate in determining the weights of the criteria.

The initial decision on the management subject (all or only some elements of the infrastructure used by the public institution) is made by the organization’s top management, indicated by the black-colored element in the block diagram. In public institutions, these decisions are usually made by a governing body, such as a management board or a similar administrative entity, often based on a proposal from the institution’s head. However, proposals can also originate from other bodies within or outside the institution, in line with the institution’s regulations, legal acts, decrees, or legal provisions from higher authorities, such as responsible national bodies or the ministry.

The next phase, the second, consists of two parallel activities (labelled as Activity 2.1 and Activity 2.2 in Figure 2), represented by the grey elements with a black solid border line in the flow diagram (Figure 2). Both activities involve the same groups of stakeholders, namely, the DM and EDSC. These are the initial stakeholder groups, defined by the roles their members perform. They are predefined and not specially selected for this process/purpose. The activity of identifying and grouping additional stakeholders involves identifying specific representatives for the previously defined remaining stakeholder groups and organizing them accordingly. In other words, this activity is about “staffing” the stakeholders and organizing them. This means selecting and organizing the remaining relevant stakeholders, thereby shaping the decision-making process to be inclusive. The second activity involves analyzing the selected subject—projects related to public institution infrastructure/facilities—primarily by the DM, while the EDSC’s role is more to guide the DM in the analysis in a way that allows for the generation of mutually comparable alternative solutions (comparable across all criteria). In this activity, the framework for generating alternative solutions is established, but the solutions themselves are not yet generated. For example, this might involve defining the types of projects to be managed and the geographic area to be covered (e.g., only buildings older than 30 years or only buildings in the northern part of the institution’s headquarters, etc.). It is important to note that these activities occur in parallel but do not influence each other; their execution is not interactive, but both DM and EDSC are involved in both activities.

The third phase also consists of two parallel activities (labelled as Activity 3.1 and Activity 3.2, and represented by the grey elements with a black dashed border line in Figure 2), but this time, these activities interact with each other. All stakeholder groups (DM, EDSC, E, U) are simultaneously involved in both activities—these are represented in the flow diagram with grey elements with dashed borders. Interaction is particularly important here because it contributes to a better approach to the subject and a clearer understanding of how to achieve it. This leads to the generation of the main goal (labelled as MG in Figure 3): Sustainable Management of Public Institution Infrastructure (buildings). Activity 3.1 is dedicated to generate alternative solutions. These solutions can be categorized into the following types: project documentation preparation—technical and study-based, obtaining permits—such as building permits or amendments to spatial planning documents, undertaking activities to modernize existing buildings, reconstructing and renewing existing buildings, constructing new buildings, or expanding existing ones, etc. Routine maintenance activities are outside the scope of this research and are considered through an assessment of the building’s condition—an expert evaluation. In this activity, the E group plays a dominant role, while the other groups are more supportive in terms of generating desires and needs (mainly indirectly, through other forms of institutional involvement—e.g., through strategic planning documents). This ensures alignment with other institutional acts and plans. It is important to note that as many alternative solutions/projects as possible are generated in this activity, with no pre-selection or similar process; all alternative solutions found in, for example, the institution’s strategic documents must be and are included. This activity is characterized by openness and tolerance, accepting even conflicting alternative solutions for evaluation or inclusion in further procedures. This approach generally bypasses key and usual points of irreconcilable differences among opposing stakeholder groups in the planning and realization of goals and projects. Sometimes deliberately conflicting alternatives, such as “do nothing” or “implement all projects at once”, may be included. The purpose of such alternative solutions is to set a reference framework or assess the realism of outcomes—the relationships among alternative solutions, knowing that these solutions will not be realized. Additionally, some alternative solutions that do not come from the previously mentioned documents, such as projects that are slightly more developed than ideas, may also be included. Their inclusion aims to compare them with approved projects to determine their relevance and decide whether they should be further developed or considered irrelevant compared to existing projects—essentially, preliminary check. This preliminary check is suggested as a second iteration only for the part of the concept that involves comparing, ranking projects, and forming a basis for decision making. The second activity (Activity 3.2) of this phase involves breaking down the previously defined MG into supporting goals (the main subgoals—MSG), which are further broken down into more specific subgoals (SG) until they reach a level where these goals become measurable or can be assessed relatively easily. Then, SG can be used as criteria—C. All stakeholder groups participate in generating goals at all levels, considering the “higher-level” knowledge among specific groups for breaking down certain goals. For example, the E group is expected to provide the most proposals for generating goals that maximize technical and economic effects, while the U group is expected to contribute most to generating goals that maximize social impacts. The fulfilment of sub-goals at the lowest level in the hierarchy by each of the generated alternative solutions is determined through measurement or expert assessment, turning these goals into criteria. Therefore, the most knowledgeable stakeholder group for a particular subgoal should guide and correct the process of generating supporting goals to ensure that measurability is adequately addressed. Graphically, when subgoals and their supporting objectives are arranged and connected with arrows, an inverted tree is formed, and hence the method is called the goal tree method. Figuratively speaking, the main goal is the trunk, the main supporting goals are the branches, and the supporting goals are the twigs and leaves depending on number of levels in hierarchy. Therefore, the criteria can be either twigs or leaves. More hierarchical levels indicate a finer elaboration of the managerial problem, but it does not necessarily ensure better criteria—an overlapping problem. All groups participate in developing all elements of this tree. Establishing this tree creates a hierarchical structure of goals, ensuring consistent decision making—everyone views the problem through the same lens by examining the agreed-upon goals set in a unique and clearly defined relationship. Most importantly, all stakeholders have participated in generating goals and forming the desired shape of the tree. The aforementioned contributes to the implementation of such concepts in the same way as the method of choosing the compromise weight of the criteria contributes but in an even more direct manner.

To expedite the goal-setting process, it is important to emphasize the usefulness of preparing a broader set of goals based on higher-level acts, from which stakeholders can then select the goals to be included (by DM and E groups). For each generated subgoal that is accepted, a criterion evaluation technique must be defined too. Therefore, special attention should be given to the overlapping of goals that will become future criteria. The accumulation of goals that will become criteria, and that do not affect the evaluation-relative project comparisons, does not contribute to the process. For example, if the criterion is the total cost of project implementation and it is calculated as the product of unit price and the number of square meters of the project, then introducing a criterion for the surface area of the objects for each project is redundant, as the comparison will yield the same results. On the other hand, sometimes, there are cases where alternative solutions are rated/ranked the same for criteria that are unrelated in terms of evaluation techniques. For instance, if the evaluation technique uses a scale from 1 to 5, it may happen that design and utility criteria (even though seemingly opposed) give the same ratings for a larger number of alternative solutions, or even all of them (in cases with a small number of alternatives).

The fourth phase involves determining the importance of the criteria, i.e., the weight of the criteria. All stakeholder groups participate in this phase as well, and the element is marked in the same way as the elements from the previous phase—shaded gray with a dashed border in Figure 2. The AHP (Analytic Hierarchy Process) method is used to determine the importance weights of criteria, as previously described. The result is the establishment of the relative importance of each criterion, which constitutes a portion of the total importance (100%) for evaluating the achievement of the main goal. In the allocation of 100% total importance, all stakeholder groups participate, each contributing to their part of the “tree”, as previously outlined. Also, as previously described, a smaller error would occur if the final weight of the criteria were determined by taking the geometric mean instead of arithmetic mean of the individually established weights (by the stakeholder group members). However, based on the author’s experience with similar problems, the arithmetic mean will be used here. The reason for this choice is to emphasize the stakeholders’ understanding of how their opinions are considered, rather than minimizing the level of approximation errors. Using the arithmetic mean, which is generally understood by all stakeholders (a method of calculation familiar to most people), provides a completely “transparent” and acceptable way to determine the compromise values of the criteria weights. It is acceptable because it assumes a clear, equitable consideration of all stakeholders/members of the stakeholder group (unlike, for example, the weighted average, which can also be applied but requires more advanced negotiation skills in group decision making—this is a possible but more complex and sensitive approach in developed democratic societies and their processes, particularly a characteristic and quality of public institutions. The level of such an “error” is acceptable since it involves relative comparison of alternative solutions based on estimation. Stakeholders’ trust in the objectivity of the methodology is crucial, as it will also increase their trust in the results achieved by this methodology. The outcome of this phase is the established compromise values of the criteria weights—values that are understood and accepted by all stakeholder groups.

With the completion of the fourth phase, the preparatory part of the DSC PIMPI implementation is finished, and the transformational part begins, consisting of three phases (from Phase 5 to Phase 7). In this group of phases, only the fourth stakeholder group, EDSC, participates. Therefore, the relevant elements of the flow diagram for these phases are white filled with a solid border. The task of the EDSC group here is to evaluate all alternative solutions (alternatives) according to all criteria using the defined evaluation techniques for each criterion. The evaluated alternatives are then compared against each other on all criteria, taking into account the weights of the criteria as well as the previously defined preferences for one alternative over another according to each criterion. Additionally, other aspects of the PROMETHEE method like the previously stated preference functions and criteria weights need to be considered as well. It is important to emphasize that EDSC does not express its own opinions or influence the implementation of this group of phases in the DSC PIMPI; rather, it uses its expert knowledge to conduct the appropriate processes that fully respect the results (stakeholder opinions) from the previous phases of DSC PIMPI. The final Phase 7 of the transformational part of the DSC PIMPI implementation is the presentation of the priorities of the alternatives for implementation in the form diagram and tables representing a goal function.

The determined priority of the alternatives should align with higher-level policies and plans, including strategic documents of the institution. For example, this could involve uniform spatial development based on the territorial scope of the institution’s activities or any other approach like functional or financial. Financial constraints arising from approved budgets or similar financial documents that regulate the institution’s operations must also be respected. Therefore, it is necessary to incorporate the impact of higher-level plans and decisions, specifically from the stakeholder groups responsible for adhering to and implementing such plans and decisions. Consequently, in this Phase 8 of the DSC PIMPI implementation, which consists of two activities, the same stakeholders are involved as in the first phase, meaning the process is initiated and completed by the same stakeholders, and the result of the process is a proposal for a smaller set of alternative solutions for the implementation or a basis for decision making.

Such a set of alternatives represents a subset of all generated alternative solutions and is not a infrastructure management plan but a basis for establishing that plan. The process of establishing the infrastructure management plan occurs in the Phase 9—the element in the flowchart is black filled with a solid border. The reason for this is that no concept, including the proposed DSC PIMPI, makes the final decision on inclusion in the plan; rather, it provides a basis for decision making to the head of the institution. In Phase 10, the head must make a final decision, which will be of higher quality since all relevant aspects of the problem and all relevant solutions have been considered, and it will be easier to implement because all relevant stakeholder groups have been appropriately involved in its formulation. The decision of the head and the selection represent the final infrastructure management plan, which is the result of Phase 10 (the element in the flowchart is white filled with a dashed border). This plan should not include other alternatives besides the proposed set, although an even smaller subset may be chosen for implementation, as only the proposed subset satisfies the views of all stakeholders as well as its narrower versions. In any other case, stakeholder views would not be fully respected. Phases 8, 9, and 10 represent the fine tuning and resulting part (final part) of the DSC PIMPI implementation, as they define the basis for decision making, which is the product of all stakeholders, and the final infrastructure management plan, which is the product of the head of the institution based on inclusion and integral approach. But since it is a public institution, the infrastructure management plan obtained in this way is only a proposal and it should be adopted by the governing body of the institution through one or more procedures. It is to be assumed that it will be easily adopted after its presentation, especially if the same governing body approved its preparation in the proposed manner (using DSC PIMPI) or if some of its members participated in its preparation.

3. Validation of DSC PIMPI

This chapter presents the validation of the DSC PIMPI in a real example. The usefulness of the DSC PIMPI is described through a comparison of the validation process of DSC PIMPI and gained results with the results obtained from real-life results provided with the current management approach. The validation is based on the author’s practical experience and interaction with other stakeholders over the past two years in the position of assistant to the head of a public institution, with tasks specifically related to the subject of this research. The data were collected informally during meetings and personal interactions with other stakeholders to ensure objectivity and the ability to compare validation process with current practices, without influencing them in any way. One of the research intentions is to provide recommendations for improving the management process, primarily to the top management, after the research was completed. Such recommendations represent a practical contribution and will be explained later, and they mainly relate to modelling the decision-making process of the decision-maker with the involvement of relevant stakeholders and a relatively small set of projects. The example is infrastructure management within a higher education institution in the Republic of Croatia, which uses several facilities. Because in this case, the higher education institution is a university, its head is the rector, who, along with the highest university body, the senate, makes management decisions and is the decision-maker in accordance with the positive regulations of the Republic of Croatia. It is particularly important to emphasize here once again that the implementation of the DSC PIMPI approach does not replace the decision-maker, nor does it make any decisions, but it exclusively facilitates decision making for the decision-maker by preparing the groundwork for it. It should also be said that after the use of the DSC PIMPI receives the prepared basis for decision making, the decision-maker adopts an infrastructure management plan, and this and other activities (adoption by the highest governing body, etc.) take place as usual. Preparing the groundwork for decision making is the final result of this research, while the infrastructure management plan should be the result of the final decision of the head and the appropriate management body as stated and is not part of this research. It should also be emphasized that the results obtained from this research were not used in practice. Furthermore, for the purposes of this research, the member of the administration—the vice-rector responsible for infrastructure—represents the DM group. It is assumed that the decision-maker proposes projects themselves in Phase 1 of the DSC PIMPI and makes the final decision in Phase 9 of the realization of DSC PIMPI, and, for the purposes of this paper, project proposals and decision are based on actual data—realized and proposed projects by the decision-maker independently of this research. These practical results served to analyze the results of the proposed concept. The head of the organizational unit responsible for infrastructure, together with employees and external experts in construction (designers, project managers, supervisory engineers, etc.), represents the E group, while the U group consists of representatives of the university’s employees and students—representatives of the existing organizational elements of the university (faculties, departments, etc.), such as deans, heads of departments and other university components, vice-deans, and student representatives. The EDSC group in this case is represented by the author of this work—typically, this always involves one person who may have a professional/administrative assistant or an assistant for simpler tasks, who does not necessarily have to be an expert in this field. It is beneficial to include such supporting individuals in other stakeholder groups as well to facilitate and speed up EDSC’s management of the implementation/execution of DSC PIMPI. The previously mentioned simulation involves the realization of the first two phases of DSC PIMPI for the case of managing projects related to the university’s construction infrastructure.

During the third phase, the stakeholders agreed on the main goal (designated as MG) of sustainable management of university projects related to buildings—current and future. This goal was broken down using the goal tree method into sub-goals and criteria as shown in Figure 4, which represents the established hierarchical structure of goals. The lowest level of goals (filled grey and marked with C) is adopted as criteria. All these objectives (main objective, main sub-objectives, and objectives that have become criteria) which are shown and explained below were identified through informal discussions/interviews during regular and informal meetings with stakeholders of all groups. The meetings took place during the preparation of project technical documentation or during the execution of construction works, but also with students during classes in study programs dealing with construction investment planning, project management, etc. The process was conducted completely informally so as not to affect the objectivity in identifying the criteria as well as determining their weights, which could occur if the proposed process were fully presented to the stakeholders.

Figure 4 shows that the main goal (MG) is broken down into three supporting sub-goals, labelled as MSG (filled white). Sub-goal MSG1 (maximization of technical effects) is supported by four third-level sub-goals, MSG 2 (maximization of economic effects) is supported by three third-level sub-goals, and MSG 3 (maximization of social effects) is supported by four third-level sub-goals. The third-level sub-goals represent criteria for further procedures and are all marked in grey (from the group of four criteria C1.1–C1.4, through the group of three criteria C2.1–C2.3, to the group of four criteria C3.1–C3.4). The

Table 1. Criteria.

Criterion Label	Criterion Name	Evaluation/Assessment Technique
C1.1	Constructability	Expert assessment—a scale of 0–10 (10—easy; 0—difficult)
C1.2	Duration	The time of execution expressed in months
C1.3	Improvement degree	Expert assessment of the degree of improvement achieved by this project—a scale of 0–100% (0—no change, 100—complete change)
C1.4	Urgency	Expert assessment of the urgency of undertaking the project in terms of meeting the needs or removing the threat—a scale of 0–100 (0—long-term; 100—immediate)
C2.1	Cost	The monetary value of the activity/project expressed in EUR 1000
C2.2	Financing	Is there a fully secured source of financing—YES or NO ratings expressed as 1 or 0
C2.3	Influence horizon	Durability of the executed project before removal or time period before which it is not necessary to undertake these or similar activities/projects again, expressed in years
C3.1	Users	The number of current and future users/people affected
C3.2	Ecology	Significant reduction of carbon footprint (use of sustainable technologies and materials)—YES or NO (1 or 0)
C3.3	Culture	Preservation or creation of new cultural/art values—YES or NO (1 or 0)
C3.4	Contribution (to institution)	Contribution to the development of study programs (YES or NO) and research/art (YES or NO)—a sum of points (0—NO and 1—YES)

below provides the full names of the criteria from the hierarchical structure shown in the previous figure, where they are also indicated by the previously presented labels. It should be noted that two criteria, specifically C1.2 and C2.1, represent a minimization problem, while the others belong to the maximization problem because it also needs to be determined when applying the PROMETHEE method.

Preference functions (used for forming preferences between alternatives according to each criterion) were also selected by EDSC in accordance with the settings of the PROMETHEE method. For criteria C2.2, C3.2, and C3.3, the usual preference function (U-shape) was selected, while for all other criteria, a linear preference function (V-shape) was chosen. The mentioned forms of the preference functions are the most commonly used forms and the only forms presented by the authors of the method. Determining the form is important because it determines the value of preference for the determined difference in the ratings of two alternative solutions according to the same criterion.

The alternative solutions, a portfolio of 12 projects, were generated by the institution’s head and were based on the ideas and proposed projects presented during last two years by himself independently of this research Meaning that for some of them there are decisions (they have been implemented in the last two years or are still in implementation phase), and some of them are for now only expressed needs. This portfolio is also grounded in the analysis of available resources—higher-order plans and decisions, discussions with the institution’s head, and meetings with the other administration, as well as meetings with the heads of the constituent units, etc. All the projects in the portfolio are listed in

Table 2. Alternatives—project portfolio.

Label of Alternative/Project	Name of the Alternative/Project
P1	Project of single historic building reconstruction for conduction of a study program in town divers of the town where the university headquarters are situated
P2	Single construction rehabilitation project for one faculty in the building situated in the same town as the university headquarters but outside the university campus
P3	Renovation and adaptation of the space for the use of two faculties in the building situated in the same town as the university headquarters but outside the university campus
P4	Preparation of project-technical documentation, obtaining a building permit and preparation of a feasibility study for the new student dormitory on the university campus and payment of utility and other related taxes
P5	Preparation of project-technical documentation, obtaining a building permit and preparation of a feasibility study for the University Science & Innovation Centre on the university campus and payment of utility and other related taxes
P6	Preparation of project-technical documentation and adaptation of the space for the use of two study programs in the building situated in the same town as the university headquarters but outside the university campus
P7	University Science & Innovation Centre construction project
P8	Project of construction of a student dormitory on the university campus
P9	Project of landscaping on the campus
P10	Preparation of the conceptual project for the regulation and construction of temporary areas/facilities for parking on the university campus
P11	The project of technical and study documentation preparation and applying for a tender for the energy renovation for several studies in the same town as the university headquarters but outside the university campus
P12	The project of technical and study documentation preparation for adaptation of one building into a new student dormitory outside of the university campus but in the same town as the university headquarters

below.

Table 3. Criteria weights.

C1.1	C1.2	C1.3	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4	Sum
7.0	7.0	12.3	7.0	13.7	15.6	4.0	19.2	3.6	2.9	7.8	100

below shows the compromise weights of the criteria obtained through the simplified AHP method. First of all, for the purposes of validation, the DM group of stakeholders defined that all sub-goals at the second hierarchical level were of equal importance, meaning MSG1 weight is 33.33, MSG2 weight is 33.33, and MSG3 weight is also 33.33. The value of weight (33.33) for each of the MSGs is then distributed to correspond to the importance of the criteria obtained by the AHP method (gained only by the comparison of criteria under single MSG according to their importance for the realization of only that MSG) as shown in Table 3 (e.g., if one MSGn had the weight 33.33 and three equally important sub-goals C, then each of them, after comparing them with each other and taking into account the weight value of MSGn, would have the same weight value, namely, 11.11). Thus, three sets of criteria weights were obtained: set C1.n (C1.1, C1.2, C1.3, and C1.4), set C2.m (C2.1, C2.2, and C2.3), and set C3.n (c3.1, C3.2, C3.3, and C3.4). Weights of criteria set C2.m (C2.1 = 13.7, C2.2 = 15.6, and C2.3 = 4.00) were defined as arithmetic mean of criteria weights (for each criterion separately) obtained by stakeholder groups E and U because both of these stakeholder groups participated in determining the importance of the criteria under MSG2 with equal importance. The criterion weights for set C1.n were determined by stakeholders in group E, and for set C3.n by stakeholders in group U.

Due to the need for data protection and to maintain objectivity (avoiding influence on the actual execution of infrastructure management), for the purposes of this research, all alternatives (all projects in the portfolio) were evaluated based on available data (with sufficient precision to reflect the actual relationships between the alternatives according to each criterion) and on objective assessments across all criteria, resulting in the decision matrix. This does not represent a significant deviation from actual practice, as in reality, here it is also impossible to have all precise data available when conducting such analyses (some data are just planned values—predictions). This matrix, together with the criterion weights and the selected preference functions, represents the basic parameters needed for the application of the PROMETHEE method for multi-criteria comparison and ranking using the software The Visual PROMETHEE- version 1.4.0.0 © Bertrand Mareschal, 2011–2013 [27].

The theoretical calculation of gathered data using the PROMETHEE method presented in the fundamental work mentioned in the introductory chapter will therefore not be presented in its entirety within this paper. The practical application of the appropriate software solution also mentioned above, with a clear listing and explanation of all relevant parameters and data required for the application of the PROMETHEE method, is presented, as well as results of each important calculation phase. The latter are presented below.

The process of applying the PROMETHEE method consists of several phases, with the main ones shown in the Figure 5.

The process starts with creation of an m × n evaluation matrix according to evaluation/assessment techniques, which are presented in Table 1, where m is the number of alternatives and n is the number of criteria.

Table 4. Evaluation matrix.

	C1.1	C1.2	C1.3	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4
P1	5	24	100	100	25	1	25	100	0	1	2
P2	3	12	100	100	25	1	25	1000	0	0	2
P3	5	24	100	100	3500	1	25	200	0	1	1
P4	6	24	50	100	2300	1	5	800	1	0	0
P5	5	12	50	50	700	1	5	200	1	0	0
P6	5	4	50	100	25	1	1	300	0	0	0
P7	4	36	100	25	50,000	0	50	200	1	0	1
P8	4	30	100	100	55,000	0	50	800	1	0	2
P9	10	1	25	100	30	1	1	15,000	0	1	0
P10	6	2	75	75	2.5	1	3	500	0	0	0
P11	9	6	50	100	25	1	25	1000	1	0	0
P12	3	12	50	50	200	0	5	300	1	0	0
min	3	1	25	25	2.5	0	1	100	0	0	0
max	10	36	100	100	55,000	1	50	15,000	1	1	2

presents the decision matrix for the analyzed problem.

The decision matrix presented in Table 4 was normalized using Equations (1) and (2) according to the selected criteria problem. The criteria problem can be a beneficial problem (problem of maximum) or it can be a cost problem (problem of minimum).

For maximum problem criteria Rij = (xij − min(xij))/(max(xij) − min(xij)),

(1)

For minimum problem criteria Rij = (max(xij) − xij)/(max(xij) − min(xij)),

(2)

where i = 1, 2, 3 … m; j = 1, 2, 3 … n.

All the criteria except C1.2 and C2.1 are maximum problem in nature, meaning the alternatives with higher values are desired. Conversely, in the case of criteria C1.2 and C2.1, which fall into minimum problems, smaller values are more desirable. Equation (1) is used for the normalization of all criteria within the decision matrix except C1.2 and C2.2 when Equation (2) is used. The following

Table 5. Normalized decision matrix.

	C1.1	C1.2	C1.3	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4
P1	0.29	0.34	1.00	1.00	1.00	1.00	0.49	0.00	0.00	1.00	1.00
P2	0.00	0.69	1.00	1.00	1.00	1.00	0.49	0.06	0.00	0.00	1.00
P3	0.29	0.34	1.00	1.00	0.94	1.00	0.49	0.01	0.00	1.00	0.50
P4	0.43	0.34	0.33	1.00	0.96	1.00	0.08	0.05	1.00	0.00	0.00
P5	0.29	0.69	0.33	0.33	0.99	1.00	0.08	0.01	1.00	0.00	0.00
P6	0.29	0.91	0.33	1.00	1.00	1.00	0.00	0.01	0.00	0.00	0.00
P7	0.14	0.00	1.00	0.00	0.09	0.00	1.00	0.01	1.00	0.00	0.50
P8	0.14	0.17	1.00	1.00	0.00	0.00	1.00	0.05	1.00	0.00	1.00
P9	1.00	1.00	0.00	1.00	1.00	1.00	0.00	1.00	0.00	1.00	0.00
P10	0.43	0.97	0.67	0.67	1.00	1.00	0.04	0.03	0.00	0.00	0.00
P11	0.86	0.86	0.33	1.00	1.00	1.00	0.49	0.06	1.00	0.00	0.00
P12	0.00	0.69	0.33	0.33	1.00	0.00	0.08	0.01	1.00	0.00	0.00

presents the normalized decision matrix.

Normalization is followed by calculation of the evaluative differences of the ith alternative with respect to the other alternatives using Equation (3).

D (Pa − Pb) = (R(ij)a − R(ij)b)

(3)

The evaluative differences (deviations) were calculated and are presented in

Table 6. Normalized decision matrix for P1.

Evaluative Difference of Ith Alternatives with Respect to Other Alternatives
					P1
	C1.1	C1.2	C1.3	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4
D(P1–P2)	0.29	−0.34	0.00	0.00	0.00	0.00	0.00	−0.06	0.00	1.00	0.00
D(P1–P3)	0.00	0.00	0.00	0.00	0.06	0.00	0.00	−0.01	0.00	0.00	0.50
D(P1–P4)	−0.14	0.00	0.67	0.00	0.04	0.00	0.41	−0.05	−1.00	1.00	1.00
D(P1–P5)	0.00	−0.34	0.67	0.67	0.01	0.00	0.41	−0.01	−1.00	1.00	1.00
D(P1–P6)	0.00	−0.57	0.67	0.00	0.00	0.00	0.49	−0.01	0.00	1.00	1.00
D(P1–P7)	0.14	0.34	0.00	1.00	0.91	1.00	−0.51	−0.01	−1.00	1.00	0.50
D(P1–P8)	0.14	0.17	0.00	0.00	1.00	1.00	−0.51	−0.05	−1.00	1.00	0.00
D(P1–P9)	−0.71	−0.66	1.00	0.00	0.00	0.00	0.49	−1.00	0.00	0.00	1.00
D(P1–P10)	−0.14	−0.63	0.33	0.33	0.00	0.00	0.45	−0.03	0.00	1.00	1.00
D(P1–P11)	−0.57	−0.51	0.67	0.00	0.00	0.00	0.00	−0.06	−1.00	1.00	1.00
D(P1–P12)	0.29	−0.34	0.67	0.67	0.00	1.00	0.41	−0.01	−1.00	1.00	1.00

. Due to insufficient space in the paper in Table 6, evaluative differences of the ith alternative are shown only for P1 with all other alternatives, while the other evaluative differences that are implemented in the same way.

The preference function Pj (Pa, Pb) must be calculated then (preference function for project P1 is shown in

Table 7. Preference function, Pj (Pa, Pb), for P1.

P1
	C1.1	C1.2	C1.3	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4
D(P1, P2)	0.29	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.00	0.00
D(P1, P3)	0.00	0.00	0.00	0.00	0.06	0.00	0.00	0.00	0.00	0.00	0.50
D(P1, P4)	0.00	0.00	0.67	0.00	0.04	0.00	0.41	0.00	0.00	1.00	1.00
D(P1, P5)	0.00	0.00	0.67	0.67	0.01	0.00	0.41	0.00	0.00	1.00	1.00
D(P1, P6)	0.00	0.00	0.67	0.00	0.00	0.00	0.49	0.00	0.00	1.00	1.00
D(P1, P7)	0.14	0.34	0.00	1.00	0.91	1.00	0.00	0.00	0.00	1.00	0.50
D(P1, P8)	0.14	0.17	0.00	0.00	1.00	1.00	0.00	0.00	0.00	1.00	0.00
D(P1, P9)	0.00	0.00	1.00	0.00	0.00	0.00	0.49	0.00	0.00	0.00	1.00
D(P1, P10)	0.00	0.00	0.33	0.33	0.00	0.00	0.45	0.00	0.00	1.00	1.00
D(P1, P11)	0.00	0.00	0.67	0.00	0.00	0.00	0.00	0.00	0.00	1.00	1.00
D(P1, P12)	0.29	0.00	0.67	0.67	0.00	1.00	0.41	0.00	0.00	1.00	1.00

below, while Pj for all other projects are calculated in the same manner as for P1) using two conditions expressed by Equations (4) and (5).

Pj (Ma, Mb) = 0

(4)

if R(ij)a ≤ R(ij)b → D (Pa − Pb) ≤ 0

Pj (Ma, Mb) = (R(ij)a − R(ij)b)

(5)

if R(ij)a > R(ij)b → D (Ma − Mb) > 0

The next step is the calculation of the aggregated preference, π(Pa, Pb), according to Equation (6), and aggregated preference values for P1 are shown in

Table 8. Aggregated preference, π(Pa, Pb), for P1.

	P1
Weights	0.07	0.07	0.123	0.07	0.137	0.156	0.04	0.192	0.036	0.028	0.078	∑ = 1
Criteria	C1.1	C1.2	C1.3	C1.4	C2.1	C2.2	C2.3	C3.1	C3.2	C3.3	C3.4	π (Pa, Pb)
π (P1, P2)	0.020	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.028	0.000	0.048
π (P1, P3)	0.000	0.000	0.000	0.000	0.009	0.000	0.000	0.000	0.000	0.000	0.039	0.048
π (P1, P4)	0.000	0.000	0.082	0.000	0.006	0.000	0.016	0.000	0.000	0.028	0.078	0.210
π (P1, P5)	0.000	0.000	0.082	0.047	0.002	0.000	0.016	0.000	0.000	0.028	0.078	0.253
π (P1, P6)	0.000	0.000	0.082	0.000	0.000	0.000	0.020	0.000	0.000	0.028	0.078	0.208
π (P1, P7)	0.010	0.024	0.000	0.070	0.124	0.156	0.000	0.000	0.000	0.028	0.039	0.451
π (P1, P8)	0.010	0.012	0.000	0.000	0.137	0.156	0.000	0.000	0.000	0.028	0.000	0.343
π (P1, P9)	0.000	0.000	0.123	0.000	0.000	0.000	0.020	0.000	0.000	0.000	0.078	0.221
π (P1, P10)	0.000	0.000	0.041	0.023	0.000	0.000	0.018	0.000	0.000	0.028	0.078	0.188
π (P1, P11)	0.000	0.000	0.082	0.000	0.000	0.000	0.000	0.000	0.000	0.028	0.078	0.188
π (P1, P12)	0.020	0.000	0.082	0.047	0.000	0.156	0.016	0.000	0.000	0.028	0.078	0.427

below (aggregated preferences for all other projects are calculated in the same manner as for P1).

$$\mathsf{\pi }\left(\mathrm{P}\mathrm{a},\mathrm{P}\mathrm{b}\right)={\displaystyle \frac{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{j}}{\mathrm{P}}_{\mathrm{j}}(\mathrm{P}\mathrm{a},\mathrm{P}\mathrm{b})}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{j}}}}$$

(6)

An aggregate preference function matrix was created, and within this validation, 12 alternatives were considered, so a 12 × 12 matrix was formed, as shown in

Table 9. Aggregate preference function matrix.

Project	P1	P2	P3	P4	P5	P6	P7	P8	P9	P10	P11	P12
P1	-	0.048	0.048	0.210	0.253	0.208	0.451	0.343	0.221	0.188	0.188	0.427
P2	0.036	-	0.082	0.209	0.235	0.189	0.448	0.332	0.221	0.167	0.160	0.388
P3	0.001	0.048	-	0.165	0.212	0.169	0.404	0.334	0.182	0.149	0.149	0.388
P4	0.055	0.066	0.057	-	0.064	0.056	0.397	0.319	0.080	0.065	0.000	0.239
P5	0.061	0.056	0.067	0.028	-	0.039	0.360	0.337	0.080	0.038	0.000	0.176
P6	0.043	0.036	0.050	0.046	0.066	-	0.426	0.355	0.041	0.023	0.004	0.239
P7	0.058	0.066	0.056	0.158	0.158	0.197	-	0.012	0.238	0.154	0.141	0.168
P8	0.065	0.066	0.103	0.197	0.251	0.242	0.129	-	0.277	0.221	0.180	0.260
P9	0.288	0.300	0.295	0.303	0.339	0.273	0.699	0.622	-	0.280	0.228	0.513
P10	0.059	0.050	0.067	0.091	0.100	0.059	0.419	0.369	0.084	-	0.049	0.273
P11	0.124	0.108	0.131	0.091	0.127	0.105	0.471	0.394	0.097	0.114	-	0.300
P12	0.063	0.036	0.070	0.029	0.003	0.039	0.197	0.173	0.080	0.038	0.000	-

below.

Determination of the leaving and entering outranking flow of the alternatives has to be done if partial (PROMETHEE I) and complete rankings (PROMETHEE II) are to be accomplished.

Results and Discussion

After the comparison, the complete ranking of the alternatives/projects was obtained by the PROMETHEE II method as stated previously, while a graphical and numerical presentation is provided in Figure 6 and Table 10 below.

Figure 6. PROMETHEE II complete ranking (The Visual PROMETHEE - version 1.4.0.0 © Bertrand Mareschal, 2011-2013.user interface).

Figure 6. PROMETHEE II complete ranking (The Visual PROMETHEE - version 1.4.0.0 © Bertrand Mareschal, 2011-2013.user interface).

In this validation, the leaving and entering outranking flow in PROMETHEE II was calculated and is presented in Table 10 (presented within the second, third, and fourth columns). Leaving (positive) flow for the a_th alternative, φ + is calculated according to Equation (7)

$$(1/\mathrm{n}-1) {\sum }_{b=1}^m\pi (a, b)$$

(7)

Entering the (negative) flow for the a_th alternative, φ—is calculated according to Equation (8),

$$(1/\mathrm{n}-1){\sum }_{b=1}^m\pi (a, b)$$

(8)

where a ≠ b, and n is the number of alternatives.

The net outranking flow of the alternatives presented below in Table 10 (fourth column) was calculated for each alternative according to Equation (9)

Net Flow {φ(a)} = Leaving Flow {φ + (a)} − Entering Flow {φ-(a)}

(9)

Table 10. PROMETHEE II complete ranking.

Project	φ+	φ−	φ	Rank	φ	Project
P1	0.23497	−0.07748	0.15749	1	0.251068	P9
P2	0.224076	−0.07575	0.148324	2	0.15749	P1
P3	0.200109	−0.08887	0.11124	3	0.148324	P2
P4	0.12708	−0.11956	0.007519	4	0.11124	P3
P5	0.112983	−0.14131	−0.02832	5	0.104324	P11
P6	0.120727	−0.12438	−0.00365	6	0.033791	P10
P7	0.127904	−0.35896	−0.23106	7	0.007519	P4
P8	0.181076	−0.29515	−0.11408	8	−0.00365	P6
P9	0.37646	−0.12539	0.251068	9	−0.02832	P5
P10	0.147273	−0.11348	0.033791	10	−0.11408	P8
P11	0.187258	−0.08293	0.104324	11	−0.20167	P12
P12	0.066018	−0.26769	−0.20167	12	−0.23106	P7

Table 10. PROMETHEE II complete ranking.

Project	φ+	φ−	φ	Rank	φ	Project
P1	0.23497	−0.07748	0.15749	1	0.251068	P9
P2	0.224076	−0.07575	0.148324	2	0.15749	P1
P3	0.200109	−0.08887	0.11124	3	0.148324	P2
P4	0.12708	−0.11956	0.007519	4	0.11124	P3
P5	0.112983	−0.14131	−0.02832	5	0.104324	P11
P6	0.120727	−0.12438	−0.00365	6	0.033791	P10
P7	0.127904	−0.35896	−0.23106	7	0.007519	P4
P8	0.181076	−0.29515	−0.11408	8	−0.00365	P6
P9	0.37646	−0.12539	0.251068	9	−0.02832	P5
P10	0.147273	−0.11348	0.033791	10	−0.11408	P8
P11	0.187258	−0.08293	0.104324	11	−0.20167	P12
P12	0.066018	−0.26769	−0.20167	12	−0.23106	P7

From Figure 6 and Table 10 (columns 5, 6, and 7), it is evident that in the overall ranking based on the positive net flow of the phi function, the alternatives/projects are ranked as follows: P9 in first place, followed by P1, P2, P3, P11, and P10. The lowest-ranked project is P7.

The results shown in Figure 6 and Table 10 represent the primary basis for decision making—the ranking, which is the result of Phase 7 of the DSC PIMPI implementation. The first recommendation to the decision-maker is to select alternatives or create a plan using only projects with a positive net flow of the phi function, as this indicates that these projects, overall, have more positive than negative effects when compared to other projects in the portfolio. In contrast, the projects with negative net flow of the phi function have more negative than positive effects (P4, P5, P6, P7, and P8) and have a less favorable outcome in achieving the set main goal (MG). However, these lower-ranked projects should not be excluded a priori and should still be considered, especially if some of the positively ranked projects cannot be executed due to other constraints.

By applying the PROMETHEE V method—which is a combination of 0–1 integer linear programming and the results of the PROMETHEE II method (overall ranking expressed by the objective function)—it is possible to introduce the aforementioned constraints, which represents Phase 8 of the DSC PIMPI implementation. For the purposes of this research, only one such constraint was introduced, specifically the total available funds for the realization of projects over the observed two-year period (EUR 6 million—estimated based on contracted values and on values estimated by projects designers) during which the research was conducted. The result is Phase 9 of the DSC PIMPI implementation, or the basis for decision making, according to which, in addition to the previously listed projects (P1, P2, P3, P9, P10, and P11), projects P4 and P6 are also considered for inclusion in the final plan. This concludes the research, as only the institution’s head—in this case, the rector—can make the final decision, thereby executing phase 10 of the DSC PIMPI. However, since this research was a simulation of past events, it can be concluded that all decisions related to the management of university facilities/projects over the past two years, or projects that have been realized or are in the process of realization, are those that will be proposed to decision-maker according to the simulation results.

The results achieved with this approach show that the proposed DSC well describe processes in institution that are relevant for decision-making. This is precisely why the results obtained in this way are significantly better than those achieved with classical methods because each of them, for example, classical economic methods NPV (Net Present Value Method) or IRR (Internal Rate of Return Method) for assessing investments (especially capital, including investments in infrastructure because they increase the value of the organization), take into account only economic parameters. Likewise, some technical methods, for example, assessing the condition of the structure in terms of mechanical resistance and stability, ignore all aspects except construction/technical ones. Some aspects are not even supported by specific methods because such methods do not exist, as is the case with assessing the usefulness of alternative solutions for the user, the urgency of undertaking project construction activities, the horizon of the impact of activities if they were undertaken, and others that all together contribute to the better work of the institution and that can be introduced here and objectively viewed and, by comparison with others, taken into account to an adequate extent, which ensures a compromise management solution/plan. This is a solution that maximizes user requirements while respecting the limitations of primarily limited available resources. It is also necessary to highlight the possibility of using approaches such as a feasibility study or only one part of it, the cost–benefit analysis (CBA), which are complex approaches that need to be carried out separately for each project, with some of these projects being of relatively small value, so there is no economic justification for such an evaluation. In addition, even if everything is analyzed using the aforementioned methods, there is still the problem of comparing projects to each other in order to establish priorities for implementation, which is solved by the proposed approach.

The validation of DSC PIMPI demonstrates the confirmation of the possible and high-quality systematic organization of planning activities for the management of public institutions’ infrastructure as assumed. The proposed DSC PIMPI is a unique way and approach to organizing these activities—phases for such a management problem. In addition to being possible to perform, the proposed DSC PIMPI provides high-quality and understandable and acceptable approaches to stakeholder involvement, as well as results in a form suitable and easy to understand for the decision-maker. It is about simple stakeholder involvement in the processes of defining criteria and their importance in a simple way, as well as a transparent way of determining compromise weights. The presentation of results is very clear and allows the decision-maker not only to clearly recognize the compromise solution but also to easily and clearly distinguish the relationships between other alternatives. Defining constraints is also easy to understand for the decision-maker, and the results are clearly and unambiguously presented.

A significant contribution of the proposed DSC PIMPI is enabling the processing of a large amount of data in different ways (different measurement units and scales, different and often very specific evaluation/estimation techniques that lead to the most diverse results/data that can be expressed descriptively or numerically) while avoiding the influence of the subjectivity of stakeholders, harmonizing with the previous ways of decision making and management of the institution related to its infrastructure. It is particularly important to emphasize that in using the proposed DSC PIMPI, decision support is established on a relatively small set of completed projects, which represents the usual/used way of decision making in the organization, which is implementing it. Therefore, the value of this approach also lies in the fact that after its establishment with current adjustments (e.g., the weight of the criteria), this DSC can provide support in planning a much larger number of projects while maintaining consistency of decision making. It can also be concluded that it is useful for simulating possible changes in the organization’s attitudes, in order to examine its development possibilities.

The usefulness of the proposed DCS PIMPI is also obvious due to the possibility of applying the proposed DSC to different organizations/users, because through the processes of generating projects and criteria and their importance, the user can simply express their views, approaches, and decision-making methods, as well as take into account their strategic goals. Therefore, there is no limitation except in the sense that the application refers to infrastructure projects of public institutions. For application in other and different organizations, certain modifications should be made, which may be a future direction of research.

It should also be noted that the proposed concept can be upgraded, i.e., improved, because it is an open-ended concept. The fact that it is such a concept is confirmed, for example, by the possibility of improving the criteria by introducing new criteria but also new assessment techniques for existing ones. For example, it is possible to introduce assessment techniques based on prediction models based on artificial neural networks or similar approaches from the field of artificial intelligence, which can improve planning by predicting, for example, deterioration of infrastructure, or by training artificial intelligence to make judgments in the manner of an expert in a certain field, which then enables the evaluation of a larger number of alternatives in a shorter time.

It is also necessary to point out the potential challenges of implementing the proposed DSC PIMPI. The dominant influence on the creation of challenges is the complexity of the system implementation, which the author tried to overcome by introducing a special group of stakeholders who are experts in such approaches, multi-criteria decision making and decision support, but also project management in construction related to the infrastructure of public institutions. Namely, the proposed approach is complex in itself because it has numerous phases and uses numerous complex methods that managers of infrastructure systems are not familiar with, and this is precisely the dominant challenge that has been identified. In addition, managers do not want to deviate from the usual practices with which they are well acquainted, nor to expand their area of activity to multiple projects, and it is especially not easy to encourage them to adopt new knowledge and skills such as those applied here. Another challenge that should be pointed out is the need for systematic data collection for the “supply” of the proposed DSC, since it mainly represents an additional task after normal working hours for employees whose responsibilities include infrastructure management. Finally, it should also be noted that the implementation of the proposed DSC also represents a cost, and therefore management should carefully assess whether the savings are justified by potential investments and not solely in monetary terms. The challenge of organizing and staffing, and especially leading a relatively large number of relevant stakeholders during the process of implementing the proposed DSC PIMPI, is a demanding task. The task that was sought to be overcome was by designing the DSC PIMPI in a way that does not necessarily require the simultaneous and joint gathering of stakeholders, but rather their separate action or action in smaller groups (using modern communication technologies and available software solutions) that are easier to organize and manage at the desired level of efficiency. The aforementioned approach significantly reduces the problem of imposing authority in group decision making (e.g., individuals have enough time to think things over because their views are mainly expressed in written-digital form, while the proposed methods then unify them into common views, which is the case when determining the weights of criteria, where compromise weight values are determined as the arithmetic mean of the submitted weight values of stakeholder groups, and group decision making is reduced to small groups of stakeholders that can easily be agreed upon because they are experts on the given topics) and realizes cost reduction.

4. Conclusions

The proposed concept can be realized, as confirmed by the real-world example. Moreover, the simulation’s implementation demonstrated that it is in alignment with the undertaken activities, further validating the correctness and timeliness of the decisions made regarding the management of the public institution’s infrastructure. On the other hand, this also confirmed the usefulness and applicability of the proposed DSC PIMPI, indicating that it can be used for planning the management of infrastructure in any other public institution. However, it is essential to follow all phases and guidelines of the DSC PIMPI implementation, ensuring the inclusivity and integrality of its phases and processes. This approach has proven the feasibility of involving relevant stakeholders in all phases of management and decision making, regardless of differing opinions—even when they are conflicting. This concept transforms opposition into a comparative advantage and an asset in the planning process, rather than letting it remain a problem, which is often the reason planning typically fails. Additionally, this approach will facilitate implementation, as all relevant stakeholders have been involved in the decision-making process and have become more familiar with the planning subject throughout the process, especially within its early phases. Finally, it is important to highlight the main advantage of this approach, which is the quality of the decision-making framework provided to the “chief” decision-maker. With this well-prepared decision-making framework, they can confidently select solutions that will most effectively contribute to portfolio management, ensuring that the selection of projects for implementation will not be a mistake.

This approach also can have the potential to lead to the transformation of infrastructure management of public institutions in the future if it is applied long enough and in a large number of cases, because it organizes managerial functions into clear processes that no longer depend on the ability, knowledge, and desire of an individual manager to include as many relevant stakeholders and diverse data as possible. As this is about public institutions and the management of their infrastructure, which represents their significant capital values, it would be of significant benefit if the basic principles of this approach were integrated into the legislation in a flexible way. That is the case because the processes of the proposed DSC mostly correspond with the existing regulation, so its additions and minor changes would perhaps not be a big challenge for the legislator/government—it should be viewed as a flexible systematization and binding of the existing provisions of several legal acts into a single one that would regulate the management of the infrastructure of public institutions.

The limitations of the proposed approach have already been mentioned in the previous subsection, so only a concluding thought about them will be given here. The main limitations are the availability of various relevant data and stakeholders (especially experts) needed to implement the proposed DSC; the available resources; and the will, readiness, and knowledge of decision-makers in public institutions and managers of infrastructure resources of public institutions to implement this complex (in terms of organizing and leading stakeholders as well as in terms of understanding and using the proposed methods and approaches within the DSC PIMPI) approach.

In conclusion, it can be stated that the proposed DSC PIMPI is applicable and beneficial.