A two-stage emergency supplies procurement model based on prospect multi-attribute three-way decision

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Machine Learning and Cybernetics Pub Date : 2024-08-28 DOI:10.1007/s13042-024-02291-4
Fan Jia, Yujie Wang, Yuanyuan Liu
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Abstract

Emergency supply chain management has recently drawn growing attention of managers and researchers with frequent appearance of pandemics, disasters and safety accidents. Previous studies proposed methods for supplier selection and order allocation, while they cannot satisfy the demand for emergency supplies as emergency events bring many uncertainties and risks in supply chain disruption. To guarantee the efficiency in emergency supplies procurement, this work aims at putting forward a two-stage approach for emergency supplier selection and order allocation by use of three-way decision and fuzzy multi-objective optimization. Firstly, by considering the perceived utilities and perceived losses of purchasing process simultaneously, a prospect profit-based three-way decision model is established. Next, the prospect multi-attribute three-way decision model for emergency supplier selection is proposed, constructing the calculation approaches of thresholds, conditional probabilities as well as decision rules. Thirdly, inspired by perceived utilities and perceived losses of supplies purchasing, the utility-based objective function and loss-based objective function are introduced to multi-objective optimization model for order allocation. Finally, a real case of government emergency supplies procurement is discussed to show the applicability and effectiveness of the proposed approach. The final results of the proposed methodology show that it can effectively manage data with uncertainty, determine the qualified suppliers as well as alternative suppliers simultaneously to prevent emergency supply chain disruption, and provide satisfactory solutions for order allocation by introducing different combinations of objective functions according to decision makers’ preference.

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基于前景多属性三向决策的两阶段应急物资采购模型
近来,随着流行病、灾害和安全事故的频繁出现,应急供应链管理日益受到管理者和研究者的关注。以往的研究提出了供应商选择和订单分配的方法,但由于突发事件给供应链中断带来了许多不确定性和风险,这些方法无法满足应急物资的需求。为保证应急物资采购的效率,本研究旨在利用三向决策和模糊多目标优化提出一种两阶段的应急供应商选择和订单分配方法。首先,通过同时考虑采购过程中的感知效用和感知损失,建立了基于前景利润的三向决策模型。其次,提出了应急供应商选择的前景多属性三向决策模型,构建了阈值、条件概率和决策规则的计算方法。第三,受物资采购的感知效用和感知损失的启发,在订单分配的多目标优化模型中引入了基于效用的目标函数和基于损失的目标函数。最后,讨论了一个政府应急物资采购的真实案例,以说明所提方法的适用性和有效性。所提方法的最终结果表明,它能有效管理具有不确定性的数据,同时确定合格供应商和备选供应商以防止应急供应链中断,并能根据决策者的偏好引入不同的目标函数组合,为订单分配提供令人满意的解决方案。
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来源期刊
International Journal of Machine Learning and Cybernetics
International Journal of Machine Learning and Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.90
自引率
10.70%
发文量
225
期刊介绍: Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data. The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC. Key research areas to be covered by the journal include: Machine Learning for modeling interactions between systems Pattern Recognition technology to support discovery of system-environment interaction Control of system-environment interactions Biochemical interaction in biological and biologically-inspired systems Learning for improvement of communication schemes between systems
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