Search Results (17)

Centered on lean production, this study integrates operational technologies (OT), communication technologies (CT), and information technologies (IT) within an open-system software architecture. Under stochastic customer demand, reliance on static data and experience-based decision-making constrains firms’ responsiveness to market. The integration of lean management with a data-driven database enhances operational flexibility and decision quality, enabling small and medium-sized enterprises (SMEs) in the bicycle industry to develop responsive digital factory environments with real-time monitoring and improved operational transparency. The proposed platform is applicable to both manufacturing processes and operational management, improving overall equipment effectiveness (OEE), production efficiency, process optimization, and reducing quality losses, inventory levels, and workforce misallocation. This study investigates the application of the Analytic Hierarchy Process (AHP) and multi-criteria decision-making (MCDM) within a performance framework integrating ESG indicators and a balanced scorecard to identify key success factors for digital lean improvement in the bicycle industry. A case study of a bicycle manufacturer was conducted using questionnaire surveys and expert interviews with exporters. The results indicate that the five most critical success factors are: enhancing return on invested capital, strengthening digital capabilities, improving product quality, minimizing inventory waste, and reducing lead time. These findings provide practical guidance for decision-makers in designing more effective lean management strategies in highly competitive digital markets. Furthermore, by facilitating the adoption of appropriate digital technologies under a reasonable return on investment, this approach supports the systematic implementation of Industry 4.0 initiatives and transforms traditional lean practices into more efficient and sustainable digital lean operations. Full article

Climate change has intensified the frequency and severity of meteorological disasters, posing significant challenges to the resilience and adaptability of port-centric transport networks (PCTNs) and global trade stability. Unlike previous studies that adopt generalised resilience frameworks or treat disaster types uniformly, this study develops a disaster-specific, integrated assessment framework whose novelty lies in coupling three complementary methods, each playing a distinct role: (i) integer programming optimises post-disaster recovery decisions under budgetary constraints by selecting cost-effective measures that maximise re-stored container-handling capacity; (ii) Monte Carlo simulation (10,000 iterations) captures the stochastic nature of meteorological disruptions and quantifies probabilistic resilience under typhoons, storm surges, and heavy fog; and (iii) an Analytic Hierarchy Process–Evidence Reasoning (AHP–ER) hybrid integrates subjective expert judgement with objective field data to evaluate adaptability across a four-level indicator system, thereby reducing the subjectivity of conventional multi-criteria approaches. Applied to Shanghai Port, the framework yields normalised resilience scores on a [0, 1] scale, where 1.0 denotes full operational continuity (network throughput equals demand) and values below 0.80 indicate substantial disruption requiring urgent intervention. Heavy fog produces the lowest score (0.73, ‘moderate-to-severe disruption’), followed by typhoons (0.81, ‘mild disruption’) and storm surges (0.89, ‘near-normal operation’), revealing that low-visibility events—not high-energy storms—pose the dominant operational threat at Shanghai Port. Translating these findings into practice, the study recommends the following: (1) deploying real-time visibility-monitoring (LiDAR) and AI-driven traffic-scheduling systems to mitigate fog-related disruptions; (2) reinforcing gantry-crane anchoring and prepositioning emergency power supplies in typhoon-prone berths; (3) prioritising hinterland-port handling redundancy in Jiangsu and Anhui sub-networks (adaptability scores 0.639 and 0.642); and (4) piloting an integrated Shanghai–Zhejiang cross-regional emergency-response corridor with shared berthing rights and standardised joint drills. These targeted, quantitatively grounded recommendations offer port authorities and policymakers an evidence base for prioritising infrastructure investment and organisational reform to safeguard global supply chains against escalating climatic threats. Full article

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations ($N={10}^7$) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size. Full article

Public healthcare infrastructure is among the most energy-intensive of public facilities; therefore, it needs to become more environmentally and economically sustainable by increasing energy efficiency and improving service reliability. Achieving these goals requires modernizing hospital energy systems with renewable energy sources (RESs). This process often involves Energy Service Companies (ESCOs), which propose integrated RES technologies with tailored contractual schemes. However, comparing ESCO offers is challenging due to their heterogeneous technologies, contractual structures, and long-term performance commitments, which make simple cost-based assessments inadequate. This study develops a structured Multi-Criteria Decision-Making (MCDM) methodology to evaluate energy projects in public healthcare facilities. The framework, based on the Analytic Hierarchy Process (AHP), combines both quantitative (net present value, stochastic simulations of energy cost savings, and CO₂ emission reductions) with qualitative assessments (redundancy, flexibility, elasticity, and stakeholder image). It addresses the lack of standardized tools for ranking real-world ESCO proposals in public procurement. The approach, applied to a case study, involves three ESCO proposals for a large hospital in Southern Italy. The results show that integrating photovoltaic generation with trigeneration achieves the highest overall score. The proposed framework provides a transparent, replicable tool to support evidence-based energy investment decisions, extendable to other public-sector infrastructures. Full article

This research introduces a novel framework that allows the comparative evaluation of airlines based on passengers’ flight experiences. The proposed framework combines a typical and a simulation-based extension of the AHP in a group decision-making environment to elicit rankings of various airlines. The first option (T-AHP) generates rankings by combining individual passengers’ preferences using the geometric mean synthesis rule. The second option (S-AHP) simulates the stochastic characteristics of the responses, aiming to handle the inherent uncertainty and the variety of preferences obtained by the customers. The rankings are derived by mapping the decision space according to the evaluation criteria implemented and passengers’ preference dimensions. The proposed options are illustrated through a case study where four airlines are evaluated using 51 satisfaction dimensions (sub-criteria). Although the derived results indicate similar rankings, those obtained by the S-AHP option are more stable and robust, with greater discriminatory capacity compared to those of its typical counterpart (T-AHP). Full article

This study introduces a robust methodology utilizing Multi-Criteria Decision Analysis (MCDA) combined with an Analytic Hierarchy Process (AHP) to repurpose abandoned hydrocarbon fields for energy storage, supporting the transition to renewable energy sources. We use a geostatistical approach integrated with Python scripting to analyze reservoir parameters—including porosity, permeability, thickness, lithology, temperature, heat capacity, and thermal conductivity—from a decommissioned hydrocarbon field in Southeast Hungary. Our workflow leverages stochastic simulation data to identify potential zones for energy storage, categorizing them into high-, moderate-, and low-suitability scenarios. This innovative approach provides rapid and precise analysis, enabling effective decision-making for energy storage implementation in depleted fields. The key finding is the development of a methodology that can quickly and accurately assess the feasibility of repurposing abandoned hydrocarbon reservoirs for underground thermal energy storage, offering a practical solution for sustainable energy transition. Full article

In 2020, China put forward the goals of “peak carbon dioxide emissions” and “carbon neutrality” (“double-carbon”) and it is urgent for the energy industry to achieve green transformation. Aiming at the rigid requirements of the carbon-peaking and carbon-neutrality goals (“double-carbon”), this study established a performance evaluation index system for an energy supply chain of a four-tier structure based on the “double-carbon” goals, calculating its weight by the analytic hierarchy process (AHP). On this basis, a three-stage data envelopment analysis (DEA) evaluation model was established to evaluate the performance of the energy supply chain in 2010–2019. According to the three-stage DEA evaluation mode, the initial input–output efficiency value of the energy supply chain was calculated by the DEA-BCC (extended by Banker, Charnes and Cooper) model and DEA-CCR (proposed by Charnes, Cooper and Rhodes) model and the influence of environmental noise was eliminated by stochastic frontier analysis (SFA) regression; we then obtained the adjusted efficiency value for the energy supply chain. At the same time, taking 2015 as the dividing point, the advantages and disadvantages between the traditional energy supply chain and new energy supply chain were analyzed and summarized. Further analysis and suggestions are provided to consumers, enterprises and countries from four aspects: energy supply, energy production and processing, energy transmission and distribution and energy consumption. Full article

The identification of homogeneous groups of actors in a local AHP-multiactor context based on their preferences is an open problem, particularly when the number of decision-makers is high. To solve this problem in the case of using stochastic AHP, this paper proposes a new Bayesian stochastic search methodology for large-scale problems (number of decision-makers greater than 20). The new methodology, based on Bayesian tools for model comparison and selection, takes advantage of the individual preference structures distributions obtained from stochastic AHP to allow the identification of homogeneous groups of actors with a maximum common incompatibility threshold. The methodology offers a heuristic approach with several near-optimal partitions, calculated by the Occam’s window, that capture the uncertainty that is inherent when considering intangible aspects (AHP). This uncertainty is also reflected in the graphs that show the similarities of the decision-maker’s opinions and that can be used to achieve representative collective positions by constructing agreement paths in negotiation processes. If a small number of actors is considered, the proposed algorithm (AHP Bayesian clustering) significantly reduces the computational time of group identification with respect to an exhaustive search method. The methodology is illustrated by a real case of citizen participation based on e-Cognocracy. Full article

This paper proposes a hybridized simheuristic approach that couples a greedy randomized adaptive search procedure (GRASP), a Monte Carlo simulation, a Pareto archived evolution strategy (PAES), and an analytic hierarchy process (AHP), in order to solve a multicriteria stochastic permutation flow shop problem with stochastic processing times and stochastic sequence-dependent setup times. For the decisional criteria, the proposed approach considers four objective functions, including two quantitative and two qualitative criteria. While the expected value and the standard deviation of the earliness/tardiness of jobs are included in the quantitative criteria to address a robust solution in a just-in-time environment, this approach also includes a qualitative assessment of the product and customer importance in order to appraise a weighted priority for each job. An experimental design was carried out in several study instances of the flow shop problem to test the effects of the processing times and sequence-dependent setup times, obtained through lognormal and uniform probability distributions with three levels of coefficients of variation, settled as 0.3, 0.4, and 0.5. The results show that both probability distributions and coefficients of variation have a significant effect on the four decision criteria selected. In addition, the analytical hierarchical process makes it possible to choose the best sequence exhibited by the Pareto frontier that adjusts more adequately to the decision-makers’ objectives. Full article

The deployment of low-carbon energy (LCE) technologies and management of installations represents an imperative to face climate change. LCE planning is an interminable process affected by a multitude of social, economic, environmental, and health factors. A major challenge for policy makers is to select a future clean energy strategy that maximizes sustainability. Thus, policy formulation and evaluation need to be addressed in an analytical manner including multidisciplinary knowledge emanating from diverse social stakeholders. In the current work, a comparative analysis of LCE planning is provided, evaluating different multicriteria decision-making (MCDM) methodologies. Initially, by applying strengths, weaknesses, opportunities, and threats (SWOT) analysis, the available energy alternative technologies are prioritized. A variety of stakeholders is surveyed for that reason. To deal with the ambiguity that occurred in their judgements, fuzzy goal programming (FGP) is used for the translation into fuzzy numbers. Then, the stochastic fuzzy analytic hierarchical process (SF-AHP) and fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS) are applied to evaluate a repertoire of energy alternative forms including biofuel, solar, hydro, and wind power. The methodologies are estimated based on the same set of tangible and intangible criteria for the case study of Thessaly Region, Greece. The application of FGP ranked the four energy types in terms of feasibility and positioned solar-generated energy as first, with a membership function of 0.99. Among the criteria repertoire used by the stakeholders, the SF-AHP evaluated all the criteria categories separately and selected the most significant category representative. Finally, F-TOPSIS assessed these criteria ordering the energy forms, in terms of descending order of ideal solution, as follows: solar, biofuel, hydro, and wind. Full article

This study, on the one hand, develops a newfangled risk assessment and analysis (RAA) methodological approach (the MCDM-STO/DET one) for sustainable engineering projects by the amalgamation of a multicriteria decision-making (MCDM) process with the joint-collaboration of a deterministic (DET) and a stochastic (STO) process. On the other hand, proceeds to the application of MCDM-STO/DET at the workplaces of the Greek construction sector and also of the fixed-telecommunications technical projects of OTE SA (that is, the Greek Telecommunications Organization S.A.) by means of real accident data coming from two official State databases, namely of “SEPE” (Labor Inspectorate, Hellenic Ministry of Employment) and of “IKA” (Social Insurance Institution, Hellenic Ministry of Health), all the way through the period of the years2009–2016.Consequently, the article’s objectives are the following: (i) The implementation and execution of the joint MCDM-STO/DET framework, and (ii) to make known that the proposed MCDM-STO/DET algorithm can be a precious method for safety managers (and/or decision-makers) to ameliorate occupational safety and health (OSH) and to endorse the sustainable operation of technical or engineering projects as well. Mainly, we mingle two different configurations of the MCDM method, initially the Analytical Hierarchy-Process (the typical-AHP), and afterwards the Fuzzy-Extended AHP (the FEAHP) one, along with the Proportional Risk Assessment Technique (PRAT) and the analysis of Time-Series Processes (TSP), and finally with the Fault-Tree Analysis (FTA). Full article

Climate change has adverse effects on agriculture, decreasing crop quality and productivity. This makes it necessary to implement adaptation and mitigation strategies that contribute to the maintenance of technical efficiency (TE). This study analyzed the relationship of TE with farmers’ mitigation and adaptation action preferences, their risk and environmental attitudes, and their perception of climate change. Through the stochastic frontier method, TE levels were estimated for 370 farmers in Northwest Mexico. The results showed the average efficiency levels (57%) for three identified groups of farmers: High TE (15% of farmers), average TE (72%), and low TE (13%). Our results showed a relationship between two of the preferred adaptation actions against climate change estimated using the analytical hierarchy process (AHP) method. The most efficient farmers preferred “change crops,” while less efficient farmers preferred “invest in irrigation infrastructure.” The anthropocentric environmental attitude inferred from the New Ecological Paradigm (NEP) scale was related to the level of TE. Efficient farmers were those with an anthropocentric environmental attitude, compared to less efficient farmers, who exhibited an ecocentric attitude. The climate change issues were more perceived by moderately efficient farmers. These findings set out a roadmap for policy-makers to face climate change at the regional level. Full article

Evaluating the eutrophication level of lakes with a single method alone is challenging since uncertain, fuzzy, and complex processes exist in eutrophication evaluations. The parameters selected for assessing eutrophication include chlorophyII-a, chemical oxygen demand, total phosphorus, total nitrogen, and clarity. Firstly, to deal with the uncertainties and fuzziness of data, triangular fuzzy numbers (TFN) were applied to describe the fuzziness of parameters. Secondly, to assess the eutrophication grade of lakes comprehensively, an improved fuzzy matter element (FME) approach was incorporated with TFNs with weights determined by combination of entropy method and analytic hierarchy process (AHP). In addition, the Monte Carlo (MC) approach was applied to easily simulate the arithmetic operations of eutrophication evaluation. The hybrid model of TFN, FME, and MC method is termed as the TFN–MC–FME model, which can provide more valuable information for decision makers. The developed model was applied to assess the eutrophication levels of 24 typical lakes in China. The evaluation indicators were expressed by TFNs input into the FME model to evaluate eutrophication grade. The results of MC simulation supplied quantitative information of possible intervals, the corresponding probabilities, as well as the comprehensive eutrophication levels. The eutrophication grades obtained for most lakes were identical to the results of the other three methods, which proved the correctness of the model. The presented methodology can be employed to process the data uncertainties and fuzziness by stochastically simulating their distribution characteristics, and obtain a better understanding of eutrophication levels. Moreover, the proposed model can also describe the trend of eutrophication development in lakes, and provide more valuable information for lake management authorities. Full article

Gas-fired power plants are environmentally friendly because of their high efficiency rates and low CO₂ emissions. On the other hand, the output power of renewable generators is stochastic, meaning that additional capacity must be held in reserve throughout the system. Gas-fired power plants are ideally suited to mitigate renewable uncertainties as they are more flexible and can easily be fired up in just a few minutes, and subsequently be shut down. Increased use of gas-fired power plants makes gas and electricity networks more dependent, so that adequacy in fuel supply of electricity network becomes a majority. However expansion planning of gas and electricity systems is accomplished by private gas and electricity companies, having no effective data exchange mechanism together. So there is a need to provide a model that coordinates the expansion planning of gas and electricity networks. On the other hand, expansion cost of either gas or electricity network and risk criteria of integrated energy system may have priority in decision-making process. With different challenging attributes, there is a gap in the literature to provide a model that takes into account the privacy of energy parties with a minimum data exchange, while considering different attributes in decision-making process. In this paper a multi-attribute decision-making (MADM) method for co-expansion planning of gas and electricity systems is introduced. The proposed MADM method supposes that a central entity as Ministry of Energy (ME) is responsible for coordinated expansion planning of gas and electricity networks, while taking into account the privacy of gas and electricity energy parties. Decision-making attributes are conflicting and the proposed method selects the best plan based on a compromise among the attributes. Different attributes including gas expansion cost (GEC), electricity expansion cost (EEC), minimum of maximum regret (MMR) and β-robustness (β_R) are considered to find the best plan with regard to the preferences of independent gas and electricity network operators. In this regard, two multi-attribute decision analysis methodologies are employed: analytical hierarchy process (AHP) is used as a simple way to weight and rank all the attributes objectively and find the relative importance of various plans, and the weighted sum method to provide a general composite index and finding the final appropriate plan. A real case study in the Khorasan province of Iran, which has a high penetration level of gas-consuming generation units (GCGU), is utilized to demonstrate the effectiveness of proposed MADM method. Results are compared with a Pareto optimal method to qualify the accuracy of proposed method. Full article

In recent years, coal-fired power plants contribute the biggest part of power generation in China. Challenges of energy conservation and emission reduction of the coal-fired power plant encountering with a rapid growth due to the rising proportion of renewable energy generation in total power generation. Energy saving power generation dispatch (ESPGD) based on power units sorting technology is a promising approach to meet the challenge. Therefore, it is crucial to establish a reasonable and feasible multi-index comprehensive evaluation (MICE) framework for assessing the performance of coal-fired power units accessed by the power grid. In this paper, a hierarchical multiple criteria evaluation system was established. Except for the typical economic and environmental indices, the evaluation system considering operational flexibility and power quality indices either. A hybrid comprehensive evaluation model was proposed to assess the unit operational performance. The model is an integration of grey relational analysis (GRA) with analytic hierarchy process (AHP) and a novel entropy-based method (abbreviate as BECC) which integrates bootstrap method and correlation coefficient (CC) into entropy principle to get the objective weight of indices. Then a case study on seven typical 600 megawatts coal-fired power units was carried out to illustrate the proposed evaluation model, and a weight sensitivity analysis was developed in addition. The results of the case study shows that unit 4 has the power generating priority over the rest ones, and unit 2 ranks last, with the lowest grey relational degree. The weight sensitivity analysis shows that the environmental factor has the biggest sensitivity coefficient. And the validation analysis of the developed BECC weight method shows that it is feasible for the MICE model, and it is stable with an ignorable uncertainty caused by the stochastic factor in the bootstrapping process. The elaborate analysis of the result reveals that it is feasible to rank power units with the proposed evaluation model. Furthermore, it is beneficial to synthesize the updated multiple criteria in optimizing the power generating priority of coal-fired power units. Full article