{"title":"A two-stage emergency supplies procurement model based on prospect multi-attribute three-way decision","authors":"Fan Jia, Yujie Wang, Yuanyuan Liu","doi":"10.1007/s13042-024-02291-4","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"77 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Machine Learning and Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s13042-024-02291-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
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