{"title":"Solving the optimal order quantity with unknown parameters for products with stock-dependent demand and variable holding cost rate","authors":"Zhanbing Guo, Yejie Zhang","doi":"10.1007/s10878-025-01260-z","DOIUrl":null,"url":null,"abstract":"<p>Solving the optimal order quantity for products with stock-dependent demand is a challenging task as both exact values of multiple parameters and complicated procedures are required. Motivated by this practical dilemma, this paper develops a new method to overcome the above-mentioned two challenges simultaneously. This new method, referred as two-stage AEOQ (adaptive economic order quantity) policy, includes the following two merits when managing products with stock-dependent demand and variable holding cost rate. First, it is feasible even when the values of underlying parameters are unknown. Second, it is easy-to-implement as decisions are made via adaptively recalibrating the inputs of classical EOQ formula by observable variables in the previous period. Theoretical analysis and numerical example show that this two-stage AEOQ policy could obtain the optimal order quantity. Moreover, this two-stage AEOQ policy is robust to parameter misestimation, and performs better than the traditional solution method when the underlying parameters are volatile. Finally, it is shown that this two-stage AEOQ policy could be further simplified when the fixed ordering cost is negligible. Therefore, this study provides a feasible order policy when the exact values of underlying parameters are unable to gain or when the economic environment is volatile.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"45 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Combinatorial Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10878-025-01260-z","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Solving the optimal order quantity for products with stock-dependent demand is a challenging task as both exact values of multiple parameters and complicated procedures are required. Motivated by this practical dilemma, this paper develops a new method to overcome the above-mentioned two challenges simultaneously. This new method, referred as two-stage AEOQ (adaptive economic order quantity) policy, includes the following two merits when managing products with stock-dependent demand and variable holding cost rate. First, it is feasible even when the values of underlying parameters are unknown. Second, it is easy-to-implement as decisions are made via adaptively recalibrating the inputs of classical EOQ formula by observable variables in the previous period. Theoretical analysis and numerical example show that this two-stage AEOQ policy could obtain the optimal order quantity. Moreover, this two-stage AEOQ policy is robust to parameter misestimation, and performs better than the traditional solution method when the underlying parameters are volatile. Finally, it is shown that this two-stage AEOQ policy could be further simplified when the fixed ordering cost is negligible. Therefore, this study provides a feasible order policy when the exact values of underlying parameters are unable to gain or when the economic environment is volatile.
期刊介绍:
The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering.
The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.