A centralised model predictive control framework for logistics management of coordinated supply chains of perishable goods

Tomás Hipólito, João Lemos Nabais, Rafael Carmona-Benítez, Miguel Ayala Botto, Rudy R. Negenborn

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This paper proposes a centralised model predictive control framework to address logistics management of supply chains of perishable goods. Meeting customer specific requirements is decisive to gain a competitive advantage in supply chain management. This fact motivates stakeholders to address solutions that continuously improve supply chain operations. The solution proposed in this work considers the supply chain as a dynamical system in a state-space representation where different categories of commodities, namely common goods and perishable goods, are included. Additionally, the dynamical model is able to store information of the complete supply chain regarding the quantity of commodities and the due time associated to the perishable goods. A centralised controller then collects the supply chain state information and optimises the commodity flow based on the model prediction over a fixed time horizon. The model predictive control solution assigns just-in-time commodity flows, schedules production according to customer demand (pull system) and monitors work-in-progress and in-transit commodities. The success of the proposed control approach is demonstrated in a numerical simulation of a three-tier supply chain following three distinct management policies.

Original languageEnglish
Number of pages21
JournalInternational Journal of Systems Science: Operations and Logistics
DOIs
Publication statusPublished - 2020

Keywords

  • centralised model predictive control
  • logistics management
  • perishable goods
  • State-space representation
  • supply chain management

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