Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition

TL;DR

This paper presents a distributed optimization framework combining model predictive control and Benders decomposition to dynamically schedule maintenance and production in manufacturing systems with degradation, improving reliability and efficiency.

Contribution

It introduces a novel distributed algorithm for joint maintenance and production scheduling using MPC and Benders decomposition, addressing large-scale, dynamic, and coupled manufacturing systems.

Findings

Effective handling of large-scale dynamic scheduling problems.

Improved system reliability through condition-based maintenance.

Successful case study validation of the proposed method.

Abstract

Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on a case study.