Fermatean fuzzy group decision model for agile, resilient and sustainable logistics service provider selection in the manufacturing industry

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-08-13 DOI:10.1108/jm2-02-2024-0040

Mohammad Akhtar

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引用次数: 0

Abstract

Purpose Logistics service provider (LSP) selection involves multiple criteria, alternatives and decision makers. Group decision-making involves vagueness and uncertainty. This paper aims to propose a novel fuzzy method for assessing and selecting agile, resilient and sustainable LSP, taking care of the inconsistency and uncertainty in subjective group ratings. Design/methodology/approach Eighteen agile, resilient, operational, economic, environmental and social sustainability criteria were identified from the literature and discussion with experts. Interval-valued Fermatean fuzzy (IVFF) sets are more flexible and accurate for handling complex uncertainty, impreciseness and inconsistency in group ratings. The IVFF PIvot Pairwise RElative Criteria Importance Assessment Simplified (IVFF-PIPRECIAS) and IVFF weighted aggregated sum product assessment (IVFF-WASPAS) methods are applied to determine criteria weights and LSP evaluation, respectively. Findings Collaboration and partnership, range of services, capacity flexibility, geographic coverage, cost of service and environmental safeguard are found to have a greater influence on the LSP selection, as per this study. The LSP (L3) with the highest score (0.949) is the best agile, resilient and sustainable LSP in the manufacturing industry. Research limitations/implications Hybrid IVFF-based PIPRECIAS and WASPAS methods are proposed for the selection of agile, resilient and sustainable LSP in the manufacturing industry. Practical implications The model can help supply chain managers in the manufacturing industry to easily adopt the hybrid model for agile, resilient and sustainable LSP selection. Social implications The paper also contributes to the social sustainability of logistics workers. Originality/value To the best of the authors’ knowledge, IVFF-PIPRECIAS and IVFF-WASPAS methods are applied for the first time to select the best agile, resilient and sustainable LSP in a developing economy context.

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为制造业选择灵活、有弹性和可持续的物流服务供应商而建立的 Fermatean 模糊群体决策模型

目的物流服务供应商（LSP）的选择涉及多个标准、备选方案和决策者。集体决策涉及模糊性和不确定性。本文旨在提出一种新颖的模糊方法，用于评估和选择敏捷、弹性和可持续的物流服务提供商，同时考虑到小组主观评分中的不一致性和不确定性。区间值费马泰模糊（IVFF）集在处理群体评分中的复杂不确定性、不精确性和不一致性方面更加灵活和准确。本研究发现，合作与伙伴关系、服务范围、能力灵活性、地理覆盖范围、服务成本和环境保障对 LSP 的选择有较大影响。得分最高（0.949）的 LSP（L3）是制造业中敏捷、弹性和可持续发展的最佳 LSP。研究局限/意义本文提出了基于 IVFF 的 PIPRECIAS 和 WASPAS 混合方法，用于在制造业中选择敏捷、弹性和可持续发展的 LSP。社会意义本文还为物流工作者的社会可持续发展做出了贡献。原创性/价值据作者所知，IVFF-PIPRECIAS 和 IVFF-WASPAS 方法首次被应用于在发展中经济体中选择最佳的敏捷、弹性和可持续物流服务提供商。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.