A Novel Multi-Agent Scheduling Mechanism for Adaptation of Production Plans in Case of Supply Chain Disruptions

TL;DR

This paper introduces an agent-based scheduling mechanism that quickly adapts existing production plans in response to supply chain disruptions, balancing efficiency and schedule validity.

Contribution

It presents a novel multi-agent approach for rapid replanning that minimizes computational effort while maintaining schedule feasibility during disruptions.

Findings

Efficient schedule adaptation achieved in short time.

System successfully tested with real Huawei production data.

Prototype implemented on Jadex agent platform.

Abstract

Manufacturing companies typically use sophisticated production planning systems optimizing production steps, often delivering near-optimal solutions. As a downside for delivering a near-optimal schedule, planning systems have high computational demands resulting in hours of computation. Under normal circumstances this is not issue if there is enough buffer time before implementation of the schedule (e.g. at night for the next day). However, in case of unexpected disruptions such as delayed part deliveries or defectively manufactured goods, the planned schedule may become invalid and swift replanning becomes necessary. Such immediate replanning is unsuited for existing optimal planners due to the computational requirements. This paper proposes a novel solution that can effectively and efficiently perform replanning in case of different types of disruptions using an existing plan. The approach is based on the idea to adhere to the existing schedule as much as possible, adapting it based on limited local changes. For that purpose an agent-based scheduling mechanism has been devised, in which agents represent materials and production sites and use local optimization techniques and negotiations to generate an adapted (sufficient, but non-optimal) schedule. The approach has been evaluated using real production data from Huawei, showing that efficient schedules are produced in short time. The system has been implemented as proof of concept and is currently reimplemented and transferred to a production system based on the Jadex agent platform.