Search results

The successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.

Based on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.

The study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.

This study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

The purpose of this study is to establish a synthetic group decision framework based on the Pythagorean fuzzy (PF) set to select the optimal medicine cold chain logistics provider (MCCLP). Fierce market competition makes enterprises must constantly improve every link in the process of enterprise sustainable development. The evaluation of MCCLP in pharmaceutical enterprises is an important link to enhance the comprehensive competitiveness. Because of the fuzziness of expert cognition and the complexity of the decision procedure, PF set can effectively handle the uncertainty and ambiguity in the process of multi-criteria group decision decision-making (MCGDM).

This paper develops an integrated group decision framework through combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and combined compromise solution (CoCoSo) approach to select a satisfactory MCCLP within PF circumstances. First, the PF set is used to process the ambiguity and uncertainty of the cognition ability of experts. Second, a novel PF knowledge measure is propounded to measure the vagueness of the PF set. Third, a comprehensive criterion weight determination technique is developed through aggregating subjective weights attained utilizing the PF DEMATEL approach and objective weight deduced by knowledge measure method. Furthermore, an integrated MCGDM approach based on synthetic weight and CoCoSo method is constructed.

The outcomes of sensibility analysis and comparison investigation show that the suggested decision framework can help decision experts to choose a satisfactory MCCLP scientifically and reasonably. Accordingly, the propounded comprehensive decision framework can be recommended to enterprises and organizations to assess the MCCLP for their improvement of core competitiveness.

MCCLP selection is not only momentous for pharmaceutical enterprises to improve transportation quality and ensure medicine safety but also provides a strong guarantee for enterprises to improve their core competitiveness. Nevertheless, enterprises face certain challenges due to the uncertainty of the assessment environment as well as human cognition in the process of choosing a satisfactory MCCLP. PF set possesses a formidable capability to address the uncertainty and imprecision information in the process of MCGDM. Therefore, pharmaceutical enterprises can implement the proposed method to evaluate the suppliers to further improve the comprehensive profit of enterprises.

The purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.

To achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.

The COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.

The study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.

Decision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.

The study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.

The originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

As environmental protection and sustainable development become more widely recognized, greater emphasis has been placed on the significance of green supplier selection (GSS), which can support businesses both upstream and downstream in enhancing their environmental performance while preserving their strategic competitiveness. Therefore, this paper aims to propose a new framework to study GSS.

Firstly, this paper establishes a GSS evaluation criteria system including product competitiveness, green performance, quality of service and enterprise social responsibility. Secondly, based on the spherical fuzzy sets (SFSs), the Average Induction Ordered Weighted Averaging Operator-Criteria Importance Through Inter Criteria Correlation (AIOWA-CRITIC) method is used to determine the subjective and objective weights and the combination of weights are determined by game theory. In addition, the GSS framework is constructed by the Cumulative Prospect Theory-Technique for Order Preference by Similarity to Ideal Solution (CPT-TOPSIS) method. Finally, the validity and robustness of the framework is verified through sensitivity comparative and ablation analysis.

The results show that Y3 is the most promising green supplier in China. This study provides a feasible guidance for GSS, which is important for the greening process of the whole supply chain.

Under spherical fuzzy sets, AIOWA and CRITIC are used to determine weights of indicators. CPT and TOPSIS are combined to construct a decision model, considering the ambiguity and uncertainty of information and the risk attitudes of decision-makers.

The United Nations' Sustainable Development Goal of ensuring healthy lives and promoting well-being for people of all ages underscores the vital role of public healthcare facilities (PHFs) in delivering essential healthcare services. However, these facilities often suffer from inadequate maintenance, exacerbated by the insufficient implementation of maintenance strategies. Recognizing the importance of PHFs in enhancing healthcare services, this paper investigates the Critical Success Factors (CSFs) in the maintenance strategies of PHFs in South Africa.

Through semi-structured interviews with nineteen purposively selected maintenance personnel from the Limpopo Department of Health (DoH), this study identified and analyzed the CSFs to enhance maintenance operations in PHFs. Thematic content analysis was employed to derive key insights from the collected data.

The study's findings highlight adequate maintenance planning and effective leadership as the two overarching CSFs in the maintenance of PHFs. These factors play a pivotal role in addressing challenges that hinder the current maintenance team from meeting maintenance requirements to the satisfaction of both staff and patients within PHFs.

The study offers valuable insights for policymakers to improve the effectiveness of maintenance operations in PHFs. By addressing the identified CSFs, policymakers can enhance maintenance operations in PHFs, positively impacting healthcare service delivery and the well-being of both staff and patients.

The paper aims to clarify the relationship of various enablers of supply chain (SC) management like lean enablers, agile enablers and leagile enablers. It proposes modeling the enablers to find the most appropriate strategy or methodology for determining the lean enabled SC agility.

The paper proposed the fuzzy SWARA-WASPAS methodology for determining the role of lean in enabling the SC agility. Also in continuation the AHP methodology is applied to find the priority weightage and ranking of leagile enablers, and a comparative analysis is done to select the best approach among the above two methodologies so that it would be beneficial for all the stakeholders.

The paper provides the investigation and identification of 28 lean enablers, 11 agile factors which are highly responsible to affect any SC specially focusing of automobile sector. Apart from above 9 leagile enablers were also identified in the paper. Finally, the comparative analysis has been done in the results obtained by two methodologies – AHP & fuzzy SWARA-WASPAS – to determine the lean enabled SC agility, and also to which strategy should be adopted by the organizations as per the customized requirement of their SC.

The research limitation is that in future, there may be more number of lean, agile and leagile enablers which may be explored by different researchers in their findings, which may vary the output result accordingly. Though the research implications focus on having an advantage and impact on all aspects whether it is social, economic or commercial, there is a possibility of exploration of new and better decision-making tools and approaches in future. Also, the researchers are encouraged to test the proposed propositions further by taking case study of any automobile manufacturing organizations for the validation of the results.

The paper includes implications for the development of a powerful interrelationships between lean enablers, agile enablers and leagile enablers, which will help organization and the managers to take decisions regarding selection of best strategy appropriate to them to enhance their SC. This will also help new researchers of the field to take help of the research findings for exploring new and better optimization tools and techniques in future.

The findings of the research work will definitely help society, as the successful implementation of the lean, agile or leagile strategies in their SC system will leads to an increase in their efficiency and productivity, which will ultimately results in huge advantage to all the stakeholders directly or indirectly connected with the organization. The productivity dynamics cycle will also improve which will lead to more benefits to all in the market and achieving higher living index with better living standards.

This paper fulfills an identified need to study the various enablers of lean, agile and leagile SC, as well as their interrelationships. Also there is a need to understand the importance and effect of lean in enabling the SC agility.

The study seeks to shed light on the estimates of the carbon shadow price in the literature relying on frontier techniques. The shadow price of undesirable outputs, such as greenhouse gas emissions, assists policymakers in determining the most cost-effective methods for reducing emissions.

The study relies on the PSALSAR and PRISMA approaches for a systematic literature review. The Web of Science and Scopus databases were used for the references.

Both parametric and nonparametric methods have been employed in the literature to estimate the shadow prices of undesirable outputs. Also, results were discussed according to the methodological and application aspects, and broad conclusions on obtained results were provided, bridging climate change mitigation policies and the shadow price of undesirable outputs.

The present study applies an integrated method, PSALSAR, to conduct a systematic review of 53 studies published between 2014 and 2023 in which efficiency models were applied to estimate the shadow price of undesirable outputs, especially CO₂. After presenting the most applicable parametric and nonparametric estimation models, a systematic summary of included articles was provided, highlighting the key features of publications.