Multi-objective energy-efficient scheduling in two-stage hybrid flowshop under consideration of no-wait

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Date
2025-07-12
Authors
Missaoui, Ahmed
O’Sullivan, Barry
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Springer Nature
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10.1007/978-981-96-8892-0_39
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Abstract
The industrial sector is one of the world’s largest energy consumers. Moreover, the fluctuating energy costs make effective energy management a critical challenge for the manufacturing industry. To address these challenges, companies are increasingly focusing on optimizing energy-efficient scheduling practices. In the current work, we addressed the two-stage no-wait hybrid flowshop problem, aiming to minimize the total completion time and energy consumption. We initially introduced a mixed integer linear programming (MILP) model when the augmented epsilon-constraint is employed to generate the optimal Pareto front for small instances. In the second step, an efficient bi-objective iterated local search algorithm is introduced to solve a benchmark of 100 instances. The results obtained are compared against those from the Non-dominated Sorting Genetic Algorithm. The comparative analysis demonstrates our proposal’s superior effectiveness.
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Keywords
Hybrid flowshop , No-wait , Iterated local search , Multi-objective optimization , Makespan , Energy consumption
Citation
Missaoui, A. and O’Sullivan, B. (2025) 'Multi-objective energy-efficient scheduling in two-stage hybrid flowshop under consideration of no-wait', in Fujita, H., Watanobe, Y., Ali, M. and Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2025. Lecture Notes in Computer Science,15707, pp. 464-475. Springer, Singapore. https://doi.org/10.1007/978-981-96-8892-0_39
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https://hdl.handle.net/10468/17873
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Insight Centre for Data Analytics - Conference Items
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© 2025, the Authors, under exclusive license to Springer Nature Singapore Pte Ltd. This version of the paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-981-96-8892-0_39