Xu, M. (2025). A Preliminary Study of Indicator-based Genetic Programming for Multi-objective Dynamic Flexible Scheduling. In Computational Intelligence and Industrial Applications (pp. 160–174). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-4756-9_14
Abstract:
Multi-objective dynamic flexible job shop scheduling (MO-DFJSS) presents an intricate task of creating optimal job schedules in a manufacturing environment characterised by uncertainty and flexibility, while simultaneously balancing multiple, often conflicting objectives. Current approaches integrate genetic programming (GP) with Pareto dominance-based and scalarising function-based multi-objective methods to learn Pareto fronts of scheduling heuristics for MO-DFJSS. However, these approaches often rely on approximate performance indicators, which can complicate achieving the final goal. In contrast, indicator-based multi-objective methods offer a more straightforward way by using performance indicators as the assessment criterion. Despite their effectiveness in other domains, no indicator-based multi-objective methods have been combined with GP for MO-DFJSS to date. Addressing this gap, this paper proposes SMS-MOGP, a fusion of GP with the SMS-EMOA, which is a popular indicator-based multi-objective algorithm, to learn scheduling heuristics for MO-DFJSS. Experiment results demonstrate that SMS-MOGP achieves comparable performance to NSGPII and significantly outperforms MOGP/D in solving the MO-DFJSS problems.
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Funding Info:
There was no specific funding for the research done
Description:
This is a post-peer-review, pre-copyedit version of an article published in Communications in Computer and Information Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-981-96-4756-9_14.