{"title":"考虑供给不确定性的多目标订单分配问题的层次启发式算法","authors":"Van Hop Nguyen","doi":"10.1080/21681015.2023.2200611","DOIUrl":null,"url":null,"abstract":"ABSTRACT In this paper, a hierarchical heuristic algorithm is proposed to allocate order quantities to suppliers and determine the best lot sizing for each supplier. Pre-defined policies are first implemented to generate the initial order allocation to suppliers. The solutions are then modified by reducing the gap of the least satisfying level to search for a compromised solution, and this process is repeated until no further improvement can be made. The fine-tuning process finally reduces the gap between the two consecutive order allocation schemes. In the second level, a dynamic programming approach is modified to determine the best lot-sizing plan and compensate for the loss of quality and late delivery. The contributions of this work are to develop not only the best order allocation plan instead of supplier selection but also effective lot sizing plan that can reduce supply uncertainties. Experiments are tested to confirm the performance of the proposed method. Graphical Abstract","PeriodicalId":16024,"journal":{"name":"Journal of Industrial and Production Engineering","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A hierarchical heuristic algorithm for multi-objective order allocation problem subject to supply uncertainties\",\"authors\":\"Van Hop Nguyen\",\"doi\":\"10.1080/21681015.2023.2200611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT In this paper, a hierarchical heuristic algorithm is proposed to allocate order quantities to suppliers and determine the best lot sizing for each supplier. Pre-defined policies are first implemented to generate the initial order allocation to suppliers. The solutions are then modified by reducing the gap of the least satisfying level to search for a compromised solution, and this process is repeated until no further improvement can be made. The fine-tuning process finally reduces the gap between the two consecutive order allocation schemes. In the second level, a dynamic programming approach is modified to determine the best lot-sizing plan and compensate for the loss of quality and late delivery. The contributions of this work are to develop not only the best order allocation plan instead of supplier selection but also effective lot sizing plan that can reduce supply uncertainties. Experiments are tested to confirm the performance of the proposed method. Graphical Abstract\",\"PeriodicalId\":16024,\"journal\":{\"name\":\"Journal of Industrial and Production Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Industrial and Production Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21681015.2023.2200611\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Industrial and Production Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21681015.2023.2200611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
A hierarchical heuristic algorithm for multi-objective order allocation problem subject to supply uncertainties
ABSTRACT In this paper, a hierarchical heuristic algorithm is proposed to allocate order quantities to suppliers and determine the best lot sizing for each supplier. Pre-defined policies are first implemented to generate the initial order allocation to suppliers. The solutions are then modified by reducing the gap of the least satisfying level to search for a compromised solution, and this process is repeated until no further improvement can be made. The fine-tuning process finally reduces the gap between the two consecutive order allocation schemes. In the second level, a dynamic programming approach is modified to determine the best lot-sizing plan and compensate for the loss of quality and late delivery. The contributions of this work are to develop not only the best order allocation plan instead of supplier selection but also effective lot sizing plan that can reduce supply uncertainties. Experiments are tested to confirm the performance of the proposed method. Graphical Abstract