Search Results (25)

Construction projects in Saudi Arabia are frequently plagued by cost overruns and time delays, with change orders being a major contributing factor. These modifications, which can occur at various stages throughout the project lifecycle, tend to negatively impact costs and cash flows. This study developed a classification impact index model to assess the potential impact of change orders on contractors’ cash flow in building construction projects. This study identified the factors and subfactors affecting contractors’ cash flow, organizing them into a hierarchical structure of main and sub-main factors. The relative importance of these factors was then assessed using the Analytical Hierarchical Process (AHP). The findings showed that project financing schemes had the most substantial impact (44%), followed by contract type (30%) and characteristics of change orders (26%). Among the subfactors, cash availability under project financing had the highest influence at 56%, while the value of change orders was the most significant subfactor under change order characteristics, contributing 30%. The Multi-Attribute Utility Theory (MAUT) was subsequently used to evaluate the attributes of these factors, applying utility scores to provide a more comprehensive assessment. The resulting Change Order Impact Index integrates AHP and MAUT to quantify the impacts of change orders. This model categorizes the effects of change orders into four levels: minor, moderate, significant, and severe. The developed Change Order Impact Index provides a systematic approach for proactively managing financial risks, optimizing project financing decisions, and supporting contractors in mitigating the impacts of change orders to maintain financial stability and reduce negative impacts on their cash flow. Full article

Saudi Arabia’s rapid urbanization and economic growth have increased the number of petrol stations crucial to national infrastructure. Despite oversight from seven local authorities, many stations fail to meet Ministry of Municipal and Rural Affairs (MMRA) standards due to decentralized management. This study develops a Condition Rating Index (CRI) for petrol stations, designed to serve as the backbone of a comprehensive decision support system for the operation and rehabilitation processes of petrol stations in Saudi Arabia. It is based on dividing petrol stations into four key areas: refueling tanks, pump stations, car service buildings, and commercial spaces. Performance factors for each area are identified and categorized hierarchically into main and sub-factors. The Analytical Hierarchy Process (AHP) is used to determine relative importance weights for these factors, while Multi-Attribute Utility Theory (MAUT) is used to assign utility scores (1–10 scale) reflecting performance levels, where 1 is poor, and 10 is optimal. The overall CRI for each petrol station is calculated by aggregating the scores of all four spaces, combining their relative importance weights with the specific CRI scores aligned with each factor’s utility level. These space-specific CRI scores reveal particular performance levels and rehabilitation needs for each area. The developed CRI functions as a transparent, integrated tool for effectively communicating performance levels and rehabilitation needs among all stakeholders. It serves as an effective decision support tool for prioritizing rehabilitation interventions based on performance levels and budget constraints, offering a comprehensive approach for enhancing petrol station management across Saudi Arabia. This paper develops a transparent and adaptable Condition Rating Index (CRI) that bridges gaps in petrol station management and aligns with sustainability goals. Full article

In this paper, we focus on weighting methods within multi-attribute utility/value theory (MAUT/MAVT). In these methods, the decision maker (DM) provides ordinal information about the relative importance of criteria, but also additional information concerning the strength of the differences between the ranked criteria, which can be expressed in different forms, including precise/imprecise cardinal information, ratio-based methods, a ranking of differences, a semantic scale, or preference statements. Although many comparison analyses of weighting methods based on ordinal information have been carried out in the literature, these analyses do not cover all of the available methods, and it is not possible to identify the best one depending on the information provided by the DM. We review the analyses comparing the performance of these weighting methods based on empirical and simulated data using different quality measures. The aim is to identify weighting methods that could be recommended for use in each situation (depending on the available information) or the missing comparison analyses that should be carried out to arrive at a recommendation. We conclude that in the case of additional information in the form of a semantic scale, the cardinal sum reciprocal method can definitively be recommended. However, when only ordinal information is provided by the DM and in cases where additional information is provided in the form of precise/imprecise cardinal information or a ranking of differences, although there are some outstanding methods, further comparison analysis should be carried out to recommend a weighting method. Full article

Reliable and resilient power transmission networks serve as vital for sustainable development and uninterrupted electricity supply. Effective maintenance programs are necessary to comply with reliability and sustainability requirements in the power sector. To that end, RAM (reliability, availability, and maintainability) assessments can provide efficient maintenance services that minimize adverse consequences and increase productivity at the lowest possible cost. We employ a statistical framework to evaluate RAM principles, including data acquisition, homogenization, trend hypothesis validation, and parameter estimation. The RAM evaluation of power transmission networks identifies primary bottlenecks in subsystems based on failure and repair behavior trends, which should be prioritized. To find the optimal maintenance policies for each subsystem, we adapt a Multi-Attribute Utility Theory (MAUT) approach, taking costs, availability, and dependability into account. The results of this approach can help improve the operational performance and sustainability of power transmission networks. Full article

The integration of green building technology is currently regarded as a critical step towards a sustainable future because it is a means of attaining sustainable development. It takes skill to combine a sustainable ecosystem with comfortable living areas to create eco-friendly building designs. The use of modern technologies can also enhance traditional methods for developing greener structures and thereby help maintain sustainable built environments. This research paper is intended to develop a selection framework to evaluate three different structural floor systems for a high-rise office building in Alhasa, the Kingdom of Saudi Arabia. The three structural floor systems are as follows: a two-way ribbed slab system, a post-tension slab system, and a hollow core slab system. The main selection criteria used for the investigation in this paper are as follows: initial cost, running costs (operating and maintenance costs), salvage value, self-structural weight, and the possibility of utilities passage. A questionnaire survey was designed to collect the opinions of experts (project managers) regarding the relative importance of the different selection criteria, and these were used to determine the most suitable structural system for the office building. The analytic hierarchy process (AHP) was the tool used to determine the weights of the different criteria, and it was applied in combination with an Eigenvector analysis. Another objective of the investigation was to determine the utility preference values of the selection criteria by employing the multi-attribute utility theory (MAUT) technique. The results showed that the most important criterion is utilities passage, which is followed by structural weight and then initial cost, salvage value, and running costs. From the results of this research, we conclude that the system with the highest total value is the post-tension slab system. The limitations of the study include the fact that it only investigated three concrete floor systems commonly used in office buildings in Saudi Arabia, and that it included only five selection criteria that were identified and evaluated by the experts. Full article

The choice of attributes in the multi-attribute decision-making process becomes frequently uncertain because of the diverse degree of preference for alternatives. These are assessed utilizing human decisions and linguistic terms that can be utilized for a more adaptable and delicate assessment. The present article illustrates a multi-attribute decision-making (MADM) process, named the exponential technique for order of preference by similarity to an ideal solution (Exp-TOPSIS), considering the selection of attributes with existing uncertainty. Another three notable multi-attribute decision-making (MADM) processes, termed as multi-attribute utility theory (MAUT), elimination and choice expressing reality method (ELECTRE), and the technique for order of preference by similarity to an ideal solution (TOPSIS) are utilized to present a comparison with the proposed methodology by proposing a mathematical model for a solid transportation problem intending to minimize carbon emissions under an uncertain environment. The uncertainty theory, which depends on human conviction degree, is utilized to define the uncertain parameters of the model related to the problem. Applying the proposed one and the other three multi-attribute decision-making processes, the best emission factors are observed to mitigate the carbon emissions from the transport sectors. In this context, the proposed method has some advantages over the existing techniques in selecting the emission factors. All four MADM approaches with different weights have been tested to choose the best five attributes among nine options to be utilized in the mathematical model to minimize the total carbon emission ejection from transportation. In every case, the obtained result states that the proposed Exp-TOPSIS gives the minimum carbon emissions in a range of 2100–2500 units. LINGO 13.0 solver is used to address the deterministic solid transportation problem, and finally, this study presents some investigations on the selection of carbon emission factors and future utilization of the proposed multi-attribute decision-making process. Full article

The textile industry is one of the most complex sectors, in terms of the materials and chemical processes used from petroleum and the environmental degradation during its production and disposal. It is therefore a sector looking for new possibilities and for more sustainable materials and applications. One option is to use natural dyes, as they are considered biodegradable, do not pollute the environment, and have potential use for many sectors, including the fashion industry. In this study, Alanya silk was dyed by a natural dyeing method with crocus sativus, Helichrysum arenarium, and Glycyrrhiza glabra L., plants that grow in and around the Alanya region. Quercus aegilops L. grown in the region was preferred as mordant, a natural binder, and is one of the plants with the highest tannin content, and it was used with a more environmentally friendly and sustainable approach to increase the binding in natural dyeing instead of chemical mordants. The aim is to provide an environmental and scientific contribution to the dyeing producers in this region. According to the MAUT (Multi-Attribute Utility Theory) method, the best dyes in terms of fastness and color efficiency were determined as the dyes made with the Glycyrrhiza glabra L. plant. Full article

Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast and well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated data set of images of almost all traffic signs on Austrian highways in 2014, and corresponding images of full traffic scenes they are contained in. Altogether, the data set consists of almost 7500 scene images with more than 28,000 detailed annotations of more than 100 distinct traffic sign classes. It covers diverse environments, ranging from urban to rural and mountainous areas, and includes many images recorded in tunnels. We further evaluate state-of-the-art traffic sign detectors and classifiers on ATSD to establish baselines for future experiments. The data set and our baseline models are freely available online. Full article

The rush of the humanitarian suppliers into the disaster area proved to be counter-productive. To reduce this proliferation problem, the present research is designed to provide a technique for supplier ranking/selection in disaster response using the principles of utility theory. A resource allocation problem is solved using optimisation based on decision maker’s preferences. Due to the lack of real-time data in the first 72 h after the disaster strike, a Decision Support System (DSS) framework called EDIS is introduced to employ secondary historical data from disaster response in four humanitarian clusters (WASH: Water, Sanitation and Hygiene, Nutrition, Health, and Shelter) to estimate the demand of the affected population. A methodology based on multi-attribute decision-making (MADM), Analytical Hierarchy processing (AHP) and Multi-attribute utility theory (MAUT) provides the following results. First a need estimation technique is put forward to estimate minimum standard requirements for disaster response. Second, a method for optimization of the humanitarian partners selection is provided based on the resources they have available during the response phase. Third, an estimate of resource allocation is provided based on the preferences of the decision makers. This method does not require real-time data from the aftermath of the disasters and provides the need estimation, partner selection and resource allocation based on historical data before the MIRA report is released. Full article

The crisis that affected Brazil as a result of the coronavirus disease 2019 (COVID-19) pandemic impacted several businesses. However, the distribution of products sold by e-commerce is one business that had increased its demand on a large scale. The Southeastern Region of Brazil has a wide territorial extension, a high number of inhabitants, and difficulties with transport logistics. Thus, this work presents a solution for choosing a location for the installation of an e-commerce warehouse, considering three possible locations, in two different Brazilian states: Rio de Janeiro and Sao Paulo. Our main objective is to identify policies that allow a balance in the trade-off between risks and service levels. Specifically, we seek to decrease logistics costs and ensure improvements in services. The choice of a location for a warehouse is a decision problem that can be solved with multiple criteria decision-making (MCDM). Three methods of MCDM have been applied: Fuzzy Decision Approach (FDA), Multi-Attribute Utility Theory (MAUT), and Multi-Attribute Value Theory (MAVT). The reasons for the application of the MAUT and MAVT methods originate from their easy understanding and use, while the concepts of FDA address the consideration of uncertainty. To maintain the simplicity of MCDM application, the FDA model is symmetric, even with symmetrical and asymmetrical fuzzy sets. Important criteria were the distance from capital cities, distance to ports, distance to airports, distance to highways, and the costs of land. Three different locations were considered for the installation of the warehouse. All three methods, FDA, MAUT, and MAVT, resulted in Location 1 as the best location for the installation of the warehouse for the distribution of e-commerce products. A discussion on conceptual qualitative divergences and the small quantitative divergence of results is also included in the conclusions. Full article

Responsible mine closure and repurposing are key to contributing to sustainable development by ensuring successful environmental rehabilitation and reducing socioeconomic risks. However, mine closure has primarily focused on remediation and rehabilitation of mined lands with limited consideration of stakeholder perspectives and the broader social, economic, and cultural impacts of closure. In this paper, we use stakeholder input to evaluate and compare three different repurposing alternatives for the tailings dam area of a mine in the state of Colorado, USA, which is expected to close in the next twenty years. By using multi-attribute utility theory (MAUT), we determine which alternative better reflects stakeholder preferences and results in the most economically, environmentally, and socially sustainable outcome. Our results show that although stakeholder groups have different ideas about what constitutes sustainable development in the context of mine closure and repurposing, it is possible to identify to what extent different scenarios can address these perspectives. We argue that integrating stakeholder views into mine closure design and repurposing can lead to more responsible and sustainable mine closure that is unique to a particular setting and stakeholder needs, and we provide a methodology that mining companies may use to understand stakeholder priorities and preferences. Full article

Consumer adoption of electric vehicles is essentially related to product quality factors, such as safety, performance and compatibility; however, the relationship between product quality standards and consumer behavior is not clear. Based on Multi-Attribute Utility Theory (MAUT) and Prospect Theory, we distinguish claimed quality attributes, intrinsic quality attributes, measured quality attributes and perceived quality attributes and establish a conceptional model using System Dynamics (SD) simulation from the perspective of a Standard-Information-Behavior framework to explore the heterogeneous impacts of technical standards on consumers’ willingness to adopt electric vehicles. Based on the theory model and simulation, we try to explain the heterogeneous effects of three different standards: safety, performance and compatibility. We find that safety standards affect adoption through a market access mechanism, perceived performance of risk standards positively impacts customers’ perceived quality, and compatibility standards influence consumers’ perceived network value. The perceived risk, perceived quality and perceived network value influence consumer adoption willingness and behavior. The study contributes to the theory of innovation diffusion and consumer adoption behavior, and offers insights for standardizing activity, innovation diffusion and marketing product information for electric vehicles. Full article

Sustainable supplier selection is a key strategic problem in supply chain management. The aim of this research is to provide a new hybrid multicriteria model for evaluating technology suppliers and validate it with a case study in the banking sector. This approach allows companies to perform qualification, selection, ranking and sorting of suppliers on a sustainable basis. Integration of several techniques is necessary to address this complex decision problem with conflicting economic, environmental and social criteria. Analytic hierarchy process (AHP) is useful for problem structuring and weighting criteria collaboratively. Multi-attribute utility theory (MAUT) is applied to obtain indicators for product quality and supplier risks, whose utility functions are derived by data-driven models that favour evaluation objectivity and transparency. Preference ranking organisation method for enrichment evaluation (PROMETHEE) is suitable for supplier selection due to its discriminant power among alternatives. Finally, FlowSort is proposed to classify suppliers into ordered groups and the outcomes are compared with results from MAUT. Results show its applicability by increasing process transparency and reducing operational risks in practice. Full article

This article presents a Model-Based Systems Engineering (MBSE) methodology for the development of a Digital Twin (DT) for an Unmanned Aerial System (UAS) with the ability to demonstrate route selection capability with a Mission Engineering (ME) focus. It reviews the concept of ME and integrates ME with a MBSE framework for the development of the DT. The methodology is demonstrated through a case study where the UAS is deployed for a Last Mile Delivery (LMD) mission in a military context where adversaries are present, and a route optimization module recommends an optimal route to the user based on a variety of inputs including potential damage or destruction of the UAS by adversary action. The optimization module is based on Multiple Attribute Utility Theory (MAUT) which analyzes predefined criteria which the user assessed would enable the successful conduct of the UAS mission. The article demonstrates that the methodology can execute a ME analysis for route selection to support a user’s decision-making process. The discussion section highlights the key MBSE artifacts and also highlights the benefits of the methodology which standardizes the decision-making process thereby reducing the negative impact of human factors which may deviate from the predefined criteria. Full article

Aged earthworks constitute a major proportion of European rail infrastructures, the replacement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these assets. It is, therefore, imperative to develop a rational means of managing slope infrastructure to determine the best use of available resources and plan maintenance in order of criticality. To do so, it is necessary to not just consider the structural performance of the asset but also to consider the safety and security of its users, the socioeconomic impact of remediation/failure and the relative importance of the asset to the network. This paper addresses this by looking at maintenance planning on a network level using multi-attribute utility theory (MAUT). MAUT is a methodology that allows one to balance the priorities of different objectives in a harmonious fashion allowing for a holistic means of ranking assets and, subsequently, a rational means of investing in maintenance. In this situation, three different attributes are considered when examining the utility of different maintenance options, namely availability (the user cost), economy (the financial implications) and structural reliability (the structural performance and subsequent safety of the structure). The main impact of this paper is to showcase that network maintenance planning can be carried out proactively in a manner that is balanced against the needs of the organization. Full article