Tackling perishability in multi-level process industries

作     者：Wei, Wenchao Amorim, Pedro Guimaraes, Luis Almada-Lobo, Bernardo 

作者机构：Beijing Jiaotong Univ Sch Econ & Management Beijing Peoples R China Beijing Jiaotong Univ Beijing Key Lab Logist Management & Technol Beijing Peoples R China Univ Porto Fac Engn INESC TEC Rua Doutor Roberto Frias 378 P-4200465 Porto Portugal 

出 版 物：《INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH》 (国际生产研究杂志)

年 卷 期：2019年第57卷第17期

页      面：5604-5623页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0802[工学-机械工程] 

基　　金：Fundamental Funds for Humanities and Social Sciences of Beijing Jiaotong University [2017jbwy004/2015RC094] National Natural Science Foundation of China [71801013/71661167009/71602106/7161101015] Beijing Social Science Foundation [18GLC078] project TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact - North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement [NORTE-01-0145-FEDER-000020] European Regional Development Fund (ERDF) 

主　　题：Multi-level production process perishability production modelling lot-sizing and scheduling mixed-integer linear programming 

摘      要：The classical multi-level lot-sizing and scheduling problem formulations for process industries rarely address perishability issues, such as limited shelf lives of intermediate products. In some industries, ignoring this specificity may result in severe losses. In this paper, we start by extending a classical multi-level lot-sizing and scheduling problem formulation (MLGLSP) to incorporate perishability issues. We further demonstrate that with the objective of minimising the total costs (purchasing, inventory and setup), the production plans generated by classical models are often infeasible under a setting with perishable products. The model distinguishes different perishability characteristics of raw materials, intermediates and end products according to various industries. Finally, we provide quantitative insights on the importance of considering perishability for different production settings when solving integrated production planning and scheduling problems.