{"title":"直觉模糊环境下可持续供应链交货期管理的有效性:分析与元启发式优化方法","authors":"Karthick B., Uthayakumar R.","doi":"10.1080/23302674.2023.2252325","DOIUrl":null,"url":null,"abstract":"AbstractThis paper investigates a two-echelon disrupted supply chain model that includes energy consumption and carbon emissions. During the global crisis caused by the previous pandemic, demand for essential goods increased, and as a consequence, businesses struggled to produce and ship goods to buyers. In this situation, it is crucial to shorten the lead time in order to deliver the goods to the buyer as soon as possible. Based on this, this paper analyses lead time into three components, namely: set-up time, transport time and production time. Additionally, Vendor Managed Inventory-Consignment Stock policy is adopted to increase business connectivity between supply chain players and reduce inventory costs. In such a case, this work addresses the ambiguity using an intuitionistic fuzzy number for unexpected demand. Therefore, the key objective of this work is to obtain the minimum total cost of a disrupted supply chain with respect to three different optimization techniques under triangular intuitionistic fuzzy demand. So far, no such inventory model has been developed with the aim of reducing set-up and transportation time in an intuitionistic fuzzy environment. Also, numerical experiments and sensitivity analysis are performed to test the performance of the proposed model. Finally, administrative insights and conclusions are presented.Keywords: Consignment stockuncertain demandsetup timetransportation timecarbon emission Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analysed in this study.Additional informationNotes on contributorsKarthick B.B. Karthick holds the position of Assistant Professor in the Department of Mathematics at M. Kumarasamy College of Engineering, Karur. In the year 2023, he received his PhD degree in Mathematics from The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India. His research interests notably encompass operations research, delving into the intricacies of optimizing complex systems; inventory control, a critical aspect of efficient resource management; fuzzy optimization, a field dealing with uncertainty and imprecision in decision-making; and supply chain management, a pivotal area ensuring seamless product distribution and availability.Uthayakumar R.R. Uthayakumar is currently a Professor and Head in the Department of Mathematics at The Gandhigram Rural Institute (Deemed to be University) located in Gandhigram, Tamil Nadu, India. In the past, he held the position of Senior Research Fellow in the National Board for Higher Mathematics (NBHM) and worked on a Department of Atomic Energy (DAE) Project at The Gandhigram Rural Institute in 1994. This project focused on the research area of “Study on Convergence of Optimization Problems.” In 2000, he successfully obtained his PhD degree. He has authored around 220 articles that have been published in both national and international journals. His research efforts are concentrated in several areas including fractal geometry, optimization techniques, inventory control, fuzzy decision-making, and supply chain management.","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"469 1","pages":"0"},"PeriodicalIF":4.0000,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effectiveness of lead-time management in a sustainable supply chain under intuitionistic fuzzy environment: analytical and metaheuristic optimisation approach\",\"authors\":\"Karthick B., Uthayakumar R.\",\"doi\":\"10.1080/23302674.2023.2252325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractThis paper investigates a two-echelon disrupted supply chain model that includes energy consumption and carbon emissions. During the global crisis caused by the previous pandemic, demand for essential goods increased, and as a consequence, businesses struggled to produce and ship goods to buyers. In this situation, it is crucial to shorten the lead time in order to deliver the goods to the buyer as soon as possible. Based on this, this paper analyses lead time into three components, namely: set-up time, transport time and production time. Additionally, Vendor Managed Inventory-Consignment Stock policy is adopted to increase business connectivity between supply chain players and reduce inventory costs. In such a case, this work addresses the ambiguity using an intuitionistic fuzzy number for unexpected demand. Therefore, the key objective of this work is to obtain the minimum total cost of a disrupted supply chain with respect to three different optimization techniques under triangular intuitionistic fuzzy demand. So far, no such inventory model has been developed with the aim of reducing set-up and transportation time in an intuitionistic fuzzy environment. Also, numerical experiments and sensitivity analysis are performed to test the performance of the proposed model. Finally, administrative insights and conclusions are presented.Keywords: Consignment stockuncertain demandsetup timetransportation timecarbon emission Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analysed in this study.Additional informationNotes on contributorsKarthick B.B. Karthick holds the position of Assistant Professor in the Department of Mathematics at M. Kumarasamy College of Engineering, Karur. In the year 2023, he received his PhD degree in Mathematics from The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India. His research interests notably encompass operations research, delving into the intricacies of optimizing complex systems; inventory control, a critical aspect of efficient resource management; fuzzy optimization, a field dealing with uncertainty and imprecision in decision-making; and supply chain management, a pivotal area ensuring seamless product distribution and availability.Uthayakumar R.R. Uthayakumar is currently a Professor and Head in the Department of Mathematics at The Gandhigram Rural Institute (Deemed to be University) located in Gandhigram, Tamil Nadu, India. In the past, he held the position of Senior Research Fellow in the National Board for Higher Mathematics (NBHM) and worked on a Department of Atomic Energy (DAE) Project at The Gandhigram Rural Institute in 1994. This project focused on the research area of “Study on Convergence of Optimization Problems.” In 2000, he successfully obtained his PhD degree. 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Effectiveness of lead-time management in a sustainable supply chain under intuitionistic fuzzy environment: analytical and metaheuristic optimisation approach
AbstractThis paper investigates a two-echelon disrupted supply chain model that includes energy consumption and carbon emissions. During the global crisis caused by the previous pandemic, demand for essential goods increased, and as a consequence, businesses struggled to produce and ship goods to buyers. In this situation, it is crucial to shorten the lead time in order to deliver the goods to the buyer as soon as possible. Based on this, this paper analyses lead time into three components, namely: set-up time, transport time and production time. Additionally, Vendor Managed Inventory-Consignment Stock policy is adopted to increase business connectivity between supply chain players and reduce inventory costs. In such a case, this work addresses the ambiguity using an intuitionistic fuzzy number for unexpected demand. Therefore, the key objective of this work is to obtain the minimum total cost of a disrupted supply chain with respect to three different optimization techniques under triangular intuitionistic fuzzy demand. So far, no such inventory model has been developed with the aim of reducing set-up and transportation time in an intuitionistic fuzzy environment. Also, numerical experiments and sensitivity analysis are performed to test the performance of the proposed model. Finally, administrative insights and conclusions are presented.Keywords: Consignment stockuncertain demandsetup timetransportation timecarbon emission Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analysed in this study.Additional informationNotes on contributorsKarthick B.B. Karthick holds the position of Assistant Professor in the Department of Mathematics at M. Kumarasamy College of Engineering, Karur. In the year 2023, he received his PhD degree in Mathematics from The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India. His research interests notably encompass operations research, delving into the intricacies of optimizing complex systems; inventory control, a critical aspect of efficient resource management; fuzzy optimization, a field dealing with uncertainty and imprecision in decision-making; and supply chain management, a pivotal area ensuring seamless product distribution and availability.Uthayakumar R.R. Uthayakumar is currently a Professor and Head in the Department of Mathematics at The Gandhigram Rural Institute (Deemed to be University) located in Gandhigram, Tamil Nadu, India. In the past, he held the position of Senior Research Fellow in the National Board for Higher Mathematics (NBHM) and worked on a Department of Atomic Energy (DAE) Project at The Gandhigram Rural Institute in 1994. This project focused on the research area of “Study on Convergence of Optimization Problems.” In 2000, he successfully obtained his PhD degree. He has authored around 220 articles that have been published in both national and international journals. His research efforts are concentrated in several areas including fractal geometry, optimization techniques, inventory control, fuzzy decision-making, and supply chain management.