.png){kind=icon}
返回
A model predictive control strategy for supply chain optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/S0098-1354(03)00047-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper describes a model predictive control strategy to find the optimal decision variables to maximize profit in supply chains with multiproduct, multiechelon distribution networks with multiproduct batch plants. The key features of this paper are: (1) a discrete time MILP dynamic model that considers the flow of material and information within the system; (2) a general dynamic optimization framework that simultaneously considers all the elements of the supply chain and their interactions; and (3), a rolling horizon approach to update the decision variables whenever changes affecting the supply chain arise. The paper compares the behavior of a supply chain under centralized and decentralized management approaches, and shows that the former yields better results, with profit increases of up to 15% as shown in an example problem. (C) 2003 Elsevier Science Ltd. All rights reserved.
Keyword:
supply chain management
scheduling
dynamic scheduling
optimal control
decision support tools
hybrid systems
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
3.9
论文数:
8.1K
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏分享收藏
查看更多内容