Investigating a Hybrid Metaheuristic For Job Shop Rescheduling

TL;DR

This paper explores a hybrid metaheuristic combining artificial immune systems, genetic algorithms, simulated annealing, and the great deluge algorithm to improve job shop rescheduling robustness against disturbances.

Contribution

It introduces a novel hybrid approach that integrates multiple metaheuristics to enhance schedule robustness in manufacturing environments.

Findings

Hybrid approach improves schedule robustness.

Simulated annealing and great deluge enhance immune system results.

Effective coverage of specific manufacturing scenarios.

Abstract

Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios.