Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems

TL;DR

This paper presents a multi-agent framework with a model-based resource agent architecture and risk-aware rescheduling strategy to improve dynamic resource reallocation in disrupted manufacturing systems, enhancing flexibility and throughput.

Contribution

It introduces a novel resource agent architecture and a risk-aware rescheduling method for multi-agent manufacturing systems under disruptions.

Findings

Reduces computational effort compared to centralized methods.

Incorporating risk assessment improves throughput.

Demonstrates effectiveness through simulation case study.

Abstract

The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g., machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralized method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput.