Best-worst Method Research Articles

Improved Building Information Modeling Based Method for Prioritizing Clash Detection in the Building Construction Design Phase

The rising complexity of construction projects and the industry’s commitment to sustainable practices have driven the extensive adoption of Building Information Modeling (BIM) technology. A core function of BIM is the early identification and resolution of clashes during the design phase, which serves to mitigate costly rework and delays in the construction process. This study presents an advanced method for classifying and prioritizing hard clashes between structural components and mechanical, electrical, and plumbing (MEP) systems. Employing the Best-Worst Method (BWM), this research assigned specific weights to structural and MEP elements based on expert evaluations. Six parameters were incorporated into this prioritization framework: the weights determined by the BWM, outputs from Navisworks software (v2021), the ratio of MEP volume to floor volume, the functional purpose of each floor, and the number of adjacent elements. A custom-developed plugin for Autodesk Navisworks integrated these parameters, enabling real-time automated clash prioritization. Clashes were ranked by criticality through a calculation involving the six parameters, which enhanced the efficiency of clash detection by optimizing time and cost considerations during the design phase. Case study results indicate that beams and columns represent the most critical structural elements, while ducts are identified as the most significant MEP elements. The proposed method substantially improves clash detection and prioritization efficiency and accuracy, yielding considerable benefits in project management and resource allocation.

Integrated spatial prioritization of urban nature-based solutions for climate adaptation, mitigation, and justice

ABSTRACT In densely populated cities, people confront multiple impacts of climate change, including stormwater runoff, flooding, heat waves, and health issues. To address these impacts, nature-based solutions (NbS) have gained significant attention in recent years due to their potential to contribute to sustainability, resilience, and ecosystem services. Nevertheless, more research is required to understand the wider potential of NbS and to identify priority areas for their deployment. This study developed an analytical approach and implemented it in the Tehran Metropolitan Area (TMA) by considering factors related to NbS co-benefits. The approach included spatial prioritization, spatial correlation analysis, and suitability analysis. It combines a Geographic Information System, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, Zonation, Best-Worst Method, Bivariate Local Indicator of Spatial Autocorrelation analysis (BiLISA), a Fuzzy Inference System, and Boolean overlay analysis. The results include maps showing priority areas for different types of NbS and spatial co-benefits and mismatches. High-priority areas are located in disadvantaged areas. In total, 105.73 km2 (17% of the TMA) was designated as highly prioritized. We show that various NbS interventions are applicable, with green roofs and rainwater harvesting being the most feasible. This study can assist decision-makers in optimizing NbS to deliver maximum co-benefits.

Governance of rural solid waste under a multi-subject governance model

Rural solid waste (RSW) exhibits distinct characteristics compared to municipal solid waste (MSW), such as dispersed distribution, long governance chains, and low recycling value, making it unsuitable to apply the same management measures as MSW. Government-led RSW management results in excessive administrative costs, and the inherent characteristics of RSW reduce market enthusiasm for its management. To address these issues, this paper integrates the “ multi-subject governance “ model with RSW management and establishes a systematic evaluation index system for RSW governance. Utilizing the Best-Worst Method (BWM), key factors were identified. Utilizing the VIKOR method, representative provinces in China were used as case studies to validate the scientific nature of the evaluation indices. The research findings indicate that the multi-subject governance model is an effective approach for RSW management. Key factors influencing RSW management effectiveness include various governmental measures, waste sorting, and a long-term multi-subject governance mechanism. Case analysis reveals a strong correlation between the level of economic development and the effectiveness of RSW management. However, constructing a government-led, multi-subject collaborative urban-rural interconnected RSW management model can effectively address RSW management issues in economically underdeveloped areas. This research provides innovative solutions for RSW management, contributing to high-quality and sustainable development in rural areas.

A hybrid metaheuristic and simulation approach towards green project scheduling

AbstractThis research tackles the environmental concern of greenhouse gas emissions in the execution of projects, with a focus on multi-site projects where the transportation of resources is a major source of emissions. Despite growing consciousness among consumers and stakeholders about sustainability, the domain of project scheduling has often overlooked the environmental impact. This paper seeks to bridge this oversight by exploring how to reduce greenhouse gas emissions during both project activities and resource transportation. A novel approach is proposed, combining a simulation model with an improved non-dominated sorted genetic algorithm. The simulation model incorporates the stochastic nature of emission rates and costs. This method is further refined with innovative techniques such as magnet-based crossover and mode reassignment. The former is a genetic algorithm operation inspired by magnetic attraction, which allows for a more diverse and effective exploration of solutions by aligning similar ’genes’ from parent solutions. The latter is a strategy for reallocating resources during project execution to optimize efficiency and reduce emissions. The efficacy of the proposed method is validated through testing on 2810 scenarios from established benchmark libraries, 100 additional scenarios adhering to the conventional multi-site problems, and a case study. The Best-Worst Method (BWM) is applied for identifying the best solution. The findings indicate substantial enhancements compared to traditional methods with a 12.7% decrease in project duration, 11.4% in costs, and a remarkable 13.6% reduction in greenhouse gas emissions.

A FMEA Optimization Method Based on TODIM and Best Worst Method-Water Filling Theory in Pythagorean Fuzzy Language Environment for Reliability Assessment of Industrial Robot

Abstract In view of the shortcomings of traditional failure modes and effects analysis (FMEA) in risk evaluation language, weight information, risk priority number (RPN), this paper proposes an FMEA optimization method. First, using the Pythagorean fuzzy language as the evaluation language, the hesitation psychology of the evaluator is truly reflected. Then, the best worst method (BWM) is used to calculate the weight of the evaluator, it can reduce the number of pairwise comparison evaluations. Second, water filling theory (WFT) uses mean values instead of extreme values to determine the discreteness of evaluation information, which is more consistent with FMEA. Therefore, WFT is used to calculate the weight of influencing factors. Finally, the tomada de-decisao iterativa multicriterio (TODIM) method is used for compromise calculation to obtain the risk ranking of failure modes. Compared with RPN, TODIM can avoid the situation that the failure mode scores are the same. At the end of the paper, the robustness and superiority of the new method are verified by taking the reliability assessment of reversing system of industrial robots as an example.

A new probabilistic linguistic decision-making process based on PL-BWM and improved three-way TODIM methods

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, which allow decision-makers to label linguistic information with different probabilities. In this paper, a method based on a PLTS is proposed to address multi-criteria decision-making problems (MCDM). We develop the theory of PLTSs and put forward a novel best–worst method (BWM), termed PL-BWM, based on PLTS. Our method fully reflects the preference information of decision-makers and accurately provides the importance level of the criteria. The combined weight of the criteria is obtained by merging PL-BWM-based subjective weights and similarity minimization-based objective weights. Upon introducing a three-way decision system to improve the TODIM method, a novel three-way TODIM method is proposed and showcased on an optimal new energy vehicle selection problem. The effectiveness and accuracy of the proposed method are verified by sensitivity analysis and comparative analysis. Our approach paves the way for new developments in solving MCDM problems and for novel applications in otherwise difficult ranking problems.

Design of a smart medical service quality evaluation system based on a hybrid multi-criteria decision model

As medical informatization continues to progress, the sophistication of smart medicine (SM) systems has led to a marked enhancement in the caliber of medical services provided. Nevertheless, the existing body of literature is sparse when it comes to frameworks for evaluating service quality, specifically within the domain of smart medicine (SM). Drawing on a hybrid multi-criteria decision-making model that fuses the best–worst method (BWM) with the VIKOR approach, this research has crafted an innovative framework for assessing the service quality of SM. BWM was used to obtain the weights of all dimensions and indicators under each dimension. Then, the service quality of hospitals H1, H2 and H3 in Xiamen was evaluated using the VIKOR method. The results showed that smart appointments, diagnosis and treatment are three important dimensions to evaluate SM service quality in medical institutions. The stability and robustness of the model were proved through sensitivity analysis. Findings suggest that hospitals can strengthen the construction of their appointment information platforms, the quality management of internal doctors, and the information connection between self-service terminals and information platforms to improve hospital service quality in the construction of smart medicine.

Industry 4.0 factors affecting SMEs towards sustainable manufacturing

Industry 4.0 (I4.0) is one of the essential topics that has been researched extensively in the research domain. In the same way, there are also several research available to check the connections between I4.0 and sustainable manufacturing. It is because of the increasing concern of stakeholders over environmental challenges that manufacturing units are often blamed. It is also true that a major part of manufacturing is done by SMEs in almost every developed or developing economy of the world including India. Therefore, the present research work took place to identify the factors that are essential for sustainable manufacturing using I4.0 in Indian SMEs. In the present study, a total of six factors were identified from the previous studies, which are technological factors (TF), organizational factors (OF), environmental factors (ENF), societal factors (SF), economic factors (ECF), and external stakeholders' factors (ESF) considering the triple bottom line (TBL) approach of the sustainability model. Each factor consists of a few sub-factors, and a total of thirty-five factors were developed. The best-worst method (BWM) and Hierarchical Decision Model (HDM) were applied to bring the results. The result suggests that OF and TF are ranked number one and two respectively. ECF and ENF ranked at three and four whereas, ESF and SF ranked at five and six respectively. The study helps firm managers revolutionize their organizations’ approach to the sustainability model by looking at the Industry 4.0 factors affecting SMEs toward sustainable manufacturing. When managers and practitioners try to convert their business digitally, this study also enables them to prioritize the many I4.0 factors that are most important to their organization. The model was validated by a regional application in Saudi Arabia.

Sustainable supply chain management challenges analysis in local plastic recycling business

Sustainable supply chain management (SSCM) has become the key concept for every industry in managing their supply chain system by focusing on three aspects: economics, social, and environmental. Even though the implementation of SSCM will help the industry increase the efficiency of supply chain management, some challenges make the firms cannot implement the SSCM concept well and unsuccessful. The challenges of SSCM implementation need to be identified and managed to overcome these challenges and increase the efficiency of SSCM management. Therefore, this study aims to identify and analyze the challenges to SSCM implementation in the case of the local plastic recycling business in Thailand. The questionnaire was used to collect data from plastic recycling business experts. The best-worst method was used to analyze the significant challenges and prioritize ranking the SSCM implementation challenges. The results of this research found that management challenges are the first rank which lack of the policy and strategy for SSCM is a critical sub-challenge in the management dimension. The financial challenges are the second rank for SSCM due to the local plastic recycling business still being faced with financial constraints, which is the critical sub-challenges in the financial dimension, and the technology challenges are ranked last, which presented that it is less critical for SSCM in the local scale. Furthermore, the research conclusion and the guideline for the research direction of this research are presented

The performance model of logistic distribution centers: Quality function deployment based on the Best-Worst Method.

This paper aims to evaluate the performance of logistic distribution centers (LDCs) by Quality Function Deployment (QFD) based on the Best-Worst Method (BWM). The originalities of this paper include: (1) exploring five constructs with 20 customer requirement attributes (CRAs) for LDCs' operations based on the SERQUAL model, (2) using the QFD model to identify five primary divisions and 18 corresponding service technical requirements (STRs) for LDCs' operations, (3) recognizing the top five STRs should be prioritized for the allocation of limited resources from House of Quality (HoQ), including cargo order (7.15%), value-added activities (6.68%), document preparation (6.31%), consolidating and assembling (6.10%), and document management (6.00%), (4) applying the Best-Worst Method (BWM) to estimate CRAs' relative weight. The proposed research model can provide a methodological reference to the relevant literature in association with logistics operations and multiple-criteria decision analysis (MCDA).

Hybrid Multi-Criteria Decision Making for Additive or Conventional Process Selection in the Preliminary Design Phase

Additive manufacturing (AM) has become a key topic in the manufacturing industry, challenging conventional techniques. However, AM has its limitations, and understanding its convenience despite established processes remains sometimes difficult, especially in preliminary design phases. This investigation provides a hybrid multi-criteria decision-making method (MCDM) for comparing AM and conventional processes. The MCDM method consists of the Best Worst Method (BWM) for the definition of criteria weights and the Proximity Index Value (PIV) method for the generation of the final ranking. The BWM reduces the number of pairwise comparisons required for the definition of criteria weights, whereas the PIV method minimizes the probability of rank reversal, thereby enhancing the robustness of the results. The methodology was validated through a case study, an aerospace bracket. The candidate processes for the bracket production were CNC machining, high-pressure die casting, and PBF-LB/M. The production of the bracket by AM was found to be the optimal choice for small to medium production batches. Additionally, the study emphasized the significance of material selection, process design guidelines, and production batch in the context of informed process selection, thereby enabling technical professionals without a strong AM background in pursuing conscious decisions.

The hazard analysis of passenger-cargo ferries: a revised risk matrix model based on fuzzy best-worst method.

Improving hazards in maritime transport is essential to maintain the reliability and sustainability of the industry, ensure safety and security, and support global trade and economic growth. This paper is aimed at analyzing the hazards of passenger-cargo ferries (PCFs). The novelty of this paper is fourfold: (1) developing a revised risk matrix (RRM) model with considering the adaptability-based resilience of organization to assess PCFs' hazards, (2) identifying risk factors (RFs) in ferry navigation and developing adaptive strategies to mitigate hazards, (3) applying the fuzzy best-worst method (BWM) to determine the RFs' weight, and (4) employing the leading Taiwanese passenger-cargo ferry operator (the Taiwan-ferries case) as an empirical study to verify the proposed model. The empirical result can provide pragmatic information for ferry operators to improve the safety of their ferry navigations. Additionally, the proposed RRM model can provide a methodological reference for related research in hazard management.

Amplifying swift-trust, collaboration, and teamwork in warehouse management through blockchain-enabled technology

This study explores the application of blockchain technology in optimizing warehouse management, focusing on improving transparency, security, and operational efficiency. By utilizing a Blockchain-based Warehouse Platform (BCWP), the study enhances inventory tracking and supply chain transparency. A fuzzy-Delphi method was employed to identify and evaluate critical blockchain practices, with their relative importance assessed using the Best-Worst Method (BWM). The Combined Compromise Solution (CoCoSo) technique further ranked key performance outcomes. The findings reveal that practices such as quality inspection and information sharing play a pivotal role in boosting warehouse performance. Additionally, the integration of blockchain technology led to significant improvements in transaction speed and operational efficiency. This research contributes to the existing literature by providing a structured decision-making framework for blockchain implementation, offering practical insights for supply chain managers aiming to streamline warehouse operations and enhance decision-making processes.

Multimodal transport route selection: An integrated fuzzy hierarchy risk assessment and multiple criteria decision-making approach

Selecting multimodal transport routes is a complex problem due to the presence of various, often conflicting decision-making attributes, such as transportation cost and time, diverse risks with hierarchical relationships both within and across groups, decision-makers’ preferences, and numerous possible options. To address this problem, a novel decision-making approach is proposed, integrating the best-worst method (BWM), fuzzy hierarchy risk assessment (FHRA), and additive ratio assessment (ARAS). This approach decomposes the route decision-making problem into a hierarchy structure and determines the optimal solution through three distinct phases. In the first phase, BWM assigns relative weights to various criteria within the hierarchical structure, ensuring that the decision-makers’ preferences are accurately reflected. In the second phase, FHRA evaluates qualitative risks from the lowest to the highest hierarchy levels. This process involves calculating risk magnitudes at the lowest hierarchy level and progressively recalculating them as they ascend to the highest level of the hierarchy. The multimodal transport cost-model provides transportation cost and time. In the final phase, ARAS synthesizes all the decision-making data to rank all possible multimodal alternative routes based on utility scores. The realistic case study validates the practical applicability and effectiveness of the proposed approach. The findings indicated that the proposed methodology effectively identify the most appropriate multimodal transport route as the best compromise choice. This study contributes new knowledge regarding a new hybrid decision-making framework that combines multiple criteria decision-making methods with fuzzy hierarchy risk analysis, thereby enhancing the design of transportation plans to align with organizational goals and customer requirements.

Risk assessment of transmission towers based on variable weight theory and matter-element extension model

Abstract The risk assessment of transmission towers is a critical approach to ensuring the safe and stable operation of power grids. In response to the increasingly frequent and severe impacts of seasonal permafrost on transmission towers in permafrost regions, this paper proposes a novel risk assessment method based on Variable Weight Theory and the Matter-Element Extension Model. By incorporating key influencing factors—such as the number of freeze-thaw cycles, soil moisture content, temperature fluctuations, soil dry density, melting settlement coefficient, and porosity ratio—an indicator system tailored to risk assessment in seasonal permafrost regions is established. Utilizing the Matter-Element Extension Model, this approach applies Variable Weight Theory to integrate both the best-worst method and the entropy weight method for calculating subjective and objective indicator weights. The risk level of the towers is then determined by their proximity to risk thresholds. Finally, a case study involving transmission towers in a specific region of a province in China is presented, where the assessment results are compared with the actual conditions to validate the rationality and effectiveness of the proposed method.

Techno economic analysis of electrolytic hydrogen production by alkaline and PEM electrolysers using MCDM methods

Hydrogen, a crucial clean and renewable energy source, addresses pressing challenges of energy security and environmental pollution. Water electrolysis for hydrogen production is a promising approach to satisfy the growing demand for sustainable energy. This study uniquely performs a comprehensive techno-economic analysis of hydrogen production using both Alkaline and Proton Exchange Membrane (PEM) electrolyzers, a first in the field to evaluate their performance comprehensively with advanced Multi-Criteria Decision-Making (MCDM) techniques. Leveraging TOPSIS, WASPAS interval methods, and the Best Worst Method (BWM) with fuzzy logic, this research introduces a novel evaluation framework that incorporates a wide-ranging set of factors, including environmental, technical, technological, economic, and social aspects, divided into 30 sub-criteria. These insights offer a comprehensive understanding of each electrolyser's strengths and weaknesses, helping stakeholders make informed decisions about cost reduction in hydrogen production technologies. This has not been done before. Although cost results favour Alkaline electrolysers, PEM electrolysers are attractive for specific applications where their benefits justify the higher initial cost, choosing between Alkaline and PEM electrolysers dependent on a given hydrogen production project's requirements.

Multi-objective optimization design of automobile seat backrest considering coupling effect

To investigate the impact of the coupling effects of carbon fiber reinforced polymer in the seat back layer on the performance of car seats, this paper presents a comprehensive optimization design method for composite materials. In detail, the finite element models firstly established and validated through five typical working conditions of automotive seats based on experimental data. Then, the optimized variables are divided and determined through backrest stress nephograms for each working conditions of the automotive seats, in which the various perspective are taken into account, such as the total mass of seat backrest, safety performance, and comfort index. Subsequently, an optimization strategy for unequal thickness layers lay-up design is constructed, which combines strength factors, optimal Latin hypercube sampling, best-worst method, gray relational analysis, and Visekriterijumsko KOmpromisno Rangiranje method for the optimal design of the automotive CFRP seat backrest. Additionally, the impact of layer coupling effects on different performance indices of the seat is examined through simulating and analyzing the seat backrest with various layer coupling types, while incorporating the classical laminate theory. The study reveals that by minimizing the laminate coupling effect, the comfort of the seat can be enhanced. Finally, a comprehensive comparative analysis of the optimal trade-off solution is carried out in terms of optimization strategies. The results show that ensuring the safety performance, the total mass of the seat backrest decreased by 21.3%, as a result of the optimization strategy proposed in this paper, and the comfort performance is also improved to some extent. Therefore, the multi-objective optimization strategy proposed in this paper performs well in terms of effectiveness and provides a reliable reference for related composite material multi-objective optimization.

Investigating the effects of barriers and challenges on Logistics 4.0 in the era of evolving digital technology

Purpose This study aims to reveal and prioritize the main barriers and challenges in front of the Logistics 4.0 transformation, which is the extension of Industry 4.0. Also, this study presents a roadmap for a company operating in developing countries to reduce and eliminate challenges and hurdles for each link in their supply chain. Design/methodology/approach A two-stage methodology was used in this study. First, a detailed literature review was conducted to identify the barriers to innovations compatible with Industry 4.0. Hence, barriers have been identified, including nine from the literature review. The best–worst method (BWM) is then used to determine these barriers’ weights and order of importance. To implement BWM, two-stage e-surveys are applied to experts. Findings The “Managerial and Economic Challenges” dimension is the most important, and “Regulatory and social challenges” is the least essential dimension among the main dimension. Moreover, financial constraints or capitals are the most critical barriers among the sub-barriers. This study gives the reader a comprehensive insight into how detected barriers affect digitalization performance. Therefore, this framework is a roadmap designed with a holistic view to guide manufacturers, logistics parties and even policy and decision-makers. Originality/value Theoretically and empirically identifies the potential barriers and challenges in the digital transformation of logistics is already missing at the desired level. From this point of view, to the best of the authors’ knowledge, this study is the first research that determines barriers based on the Logistics 4.0 model with an industrial perspective. One of the most important limitations of this study is that a total of nine dimensions were examined under only three basic barriers. Different alternatives can be identified for future studies.

A large-scale supplier evaluation approach for circular economy in the presence of circular criteria interactions and weight consistency

Circular economy (CE) is an economic model in which we achieve sustainable development through resource recovery, reuse, and remanufacturing. Large-scale supplier evaluation is crucial for ensuring the sustainability of the CE supply chain and efficient utilization of resources. Especially, criterion interaction and consistency of criterion weights are two key factors of large suppliers’ evaluation in CE. However, current research often overlooks these key factors, this study proposes a large-scale supplier evaluation approach in CE to address these issues. The novel aspects of the proposed approach come with considering the interaction between circular and non-circular criteria, as well as eliminating the impact of large-scale complex information on the consistency of criterion weights. Firstly, large-scale hesitant evaluation information is fused by evidence reasoning, and interaction rules between circular and non-circular criteria are established. Then, the interaction weights of the criteria are obtained based on the interaction of circular criteria, and the comprehensive criteria weights are determined by an improved best-worst method. Next, the decision makers’ weights are generated by the utility value of the circular criterion. Finally, the results of numerical example and analysis show that 86% of the supplier ranking results are stable, and the consistency of the proposed approach is 0.9757, indicating that the proposed approach exhibits high validity and reliability. It emphasizes the significance of the supplier’s circular ability in CE and reduces the interference of large-scale complex information on the consistency of evaluation results.

Mapping development potential and priority zones for utility-scale photovoltaic on the Qinghai-Tibet Plateau

Sustainable renewable energy is a critical goal of the 2030 Sustainable Development Agenda. The scientific and rational development of solar power in the Qinghai-Tibet Plateau (QTP) is vital for China's carbon peak and carbon neutrality goals. However, more accurate, high spatial resolution assessments are needed to evaluate the utility-scale photovoltaic (PV) development potential on the QTP. To address this, we integrated geographical and technical potentials into a unified framework for assessing PV development potential. Using the spatial multi-criteria decision analysis technique, supported by the Bayesian Best-Worst method, we generated a 30-m suitability map for assessing geographical and technical potentials, along with priority zones. We found that approximately 35.22 % of the study area is suitable for utility-scale PV development, mainly concentrated in the Qaidam Basin in Qinghai and the northern Tibet Plateau in Xizang. The estimated PV technical potential for highly suitable and suitable classes is approximately 42.34 million GWh/a, which is equivalent to reduction of approximately 31.43 billion t/a of carbon emissions. We also formulated four priority development strategies using a spatially explicit model to meet the urgent 2030 target for utility-scale PV installations. Moreover, validation of existing PV projects and sensitivity analysis were conducted, confirming the accuracy and robustness of the results. This study provides practical guidance for informed decision-making by the government, investors, and stakeholders.