Integrating tactical planning, operational planning and scheduling using data-driven feasibility analysis

被引:13
|
作者
Badejo, Oluwadare
Ierapetritou, Marianthi
机构
[1] Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St, Newark
基金
美国国家科学基金会;
关键词
Integrated supply chain optimization; Machine learning; Feasibility analysis; Scheduling; Multi-objective optimization; Data-driven optimization; PRODUCTION ROUTING PROBLEM; MULTIOBJECTIVE OPTIMIZATION; DYNAMIC OPTIMIZATION; MODEL; FRAMEWORK; FLEXIBILITY; UNCERTAINTY; PERFORMANCE; ALGORITHMS; CHALLENGES;
D O I
10.1016/j.compchemeng.2022.107759
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Supply chain operations and scheduling are well-studied problems in the literature. Although these problems are related, they are often solved sequentially. This uncoordinated approach usually leads to suboptimal solutions. This paper proposes a methodology for integrating scheduling operations into the supply chain network, motivated by the available enterprise data, and feasibility analysis. Existing literature on integrated models for addressing the supply chain is reviewed and classified. Then the mathematical model formulation for each level is discussed, after which the proposed data-driven integrated framework is described. The methodology is tested on two different case studies of varying dimensions. For each case study, the solution solves a multi-objective problem with the overall aim of an optimal solution that is robust towards achieving optimal cost while keeping customer satisfaction in mind. (c) 2022 Elsevier Ltd. All rights reserved.
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页数:20
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