{"title":"An integrated economic production quantity model with shortages considering energy utilization in production and warehousing","authors":"Hong-Nguyen Nguyen, Matthieu Godichaud, Lionel Amodeo","doi":"10.1007/s11081-024-09926-z","DOIUrl":null,"url":null,"abstract":"<p>This article presents a mathematical analysis of the effects of shortages in an integrated inventory model that considers energy utilization in manufacturing and warehousing. A Non-Linear Programming model is proposed for the Energy-Economic Production Quantity with Shortages problem. A solution procedure is proposed to minimize total cost by analyzing different shortage policies (full backorder, partial backorder, and full lost sale). Numerical examples illustrate the significance of integrating energy consumption components in inventory modeling, particularly in the context of rising energy prices. Accepting shortages reduces average inventory levels, decreases warehousing energy costs and achieves overall cost minimization. Full backorder policy do not reduce energy consumption in production, highlighting partial backorder policy as the optimal choice from both economic and environmental perspectives. Sensitivity analysis highlights the significant influence of model parameters on total costs and energy components. The impact of energy unit cost is particularly noteworthy given increasing energy demand and supply disruptions. These findings underscore the importance of considering energy consumption and associated costs in supply chain operations, emphasizing the study’s role in addressing these challenges.</p>","PeriodicalId":56141,"journal":{"name":"Optimization and Engineering","volume":"16 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optimization and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11081-024-09926-z","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This article presents a mathematical analysis of the effects of shortages in an integrated inventory model that considers energy utilization in manufacturing and warehousing. A Non-Linear Programming model is proposed for the Energy-Economic Production Quantity with Shortages problem. A solution procedure is proposed to minimize total cost by analyzing different shortage policies (full backorder, partial backorder, and full lost sale). Numerical examples illustrate the significance of integrating energy consumption components in inventory modeling, particularly in the context of rising energy prices. Accepting shortages reduces average inventory levels, decreases warehousing energy costs and achieves overall cost minimization. Full backorder policy do not reduce energy consumption in production, highlighting partial backorder policy as the optimal choice from both economic and environmental perspectives. Sensitivity analysis highlights the significant influence of model parameters on total costs and energy components. The impact of energy unit cost is particularly noteworthy given increasing energy demand and supply disruptions. These findings underscore the importance of considering energy consumption and associated costs in supply chain operations, emphasizing the study’s role in addressing these challenges.
期刊介绍:
Optimization and Engineering is a multidisciplinary journal; its primary goal is to promote the application of optimization methods in the general area of engineering sciences. We expect submissions to OPTE not only to make a significant optimization contribution but also to impact a specific engineering application.
Topics of Interest:
-Optimization: All methods and algorithms of mathematical optimization, including blackbox and derivative-free optimization, continuous optimization, discrete optimization, global optimization, linear and conic optimization, multiobjective optimization, PDE-constrained optimization & control, and stochastic optimization. Numerical and implementation issues, optimization software, benchmarking, and case studies.
-Engineering Sciences: Aerospace engineering, biomedical engineering, chemical & process engineering, civil, environmental, & architectural engineering, electrical engineering, financial engineering, geosciences, healthcare engineering, industrial & systems engineering, mechanical engineering & MDO, and robotics.