Energy-efficient multi-objective flexible manufacturing scheduling
Energy-efficient multi-objective flexible manufacturing scheduling
This paper presents a novel scheduling of a resource-constrained Flexible Manufacturing System (FMS) with consideration of the following sub-problems: (i) machine loading and unloading, (ii) manufacturing operation scheduling, (iii) machine assignment, and (iv) Automated Guided Vehicle (AGV) scheduling. In the proposed model, both the AGV and machinery are considered as the required resources. Energy efficiency of AGVs has been studied in order to improve environmental sustainability in terms of a linear function, which is based on load and distance, accordingly. Because of the NP-hard characteristics of the problem, a modified multi-objective particle swarm optimization (MMOPSO) has been developed for solving the model and compared with the classic version of the multi-objective particle swarm optimization (MOPSO) algorithm in terms of five performance metrics. Finally, the results are evaluated by the application of a multi-criteria decision-making (MCDM) algorithm according to which the MMOPSO outperforms the MOPSO.
Automated guided vehicle (AGV), Flexible manufacturing systems (FMS), Multi-objective particle swarm optimization (MOPSO), Scheduling
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Moghdani, Reza
f3ccdd7d-145d-4c95-bb47-23c1341df155
Maghsoudlou, Hamidreza
31029a70-7ce3-45c8-8db1-8218d39345e0
2 October 2021
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Moghdani, Reza
f3ccdd7d-145d-4c95-bb47-23c1341df155
Maghsoudlou, Hamidreza
31029a70-7ce3-45c8-8db1-8218d39345e0
Barak, Sasan, Moghdani, Reza and Maghsoudlou, Hamidreza
(2021)
Energy-efficient multi-objective flexible manufacturing scheduling.
Journal of Cleaner Production, 283, [124610].
(doi:10.1016/j.jclepro.2020.124610).
Abstract
This paper presents a novel scheduling of a resource-constrained Flexible Manufacturing System (FMS) with consideration of the following sub-problems: (i) machine loading and unloading, (ii) manufacturing operation scheduling, (iii) machine assignment, and (iv) Automated Guided Vehicle (AGV) scheduling. In the proposed model, both the AGV and machinery are considered as the required resources. Energy efficiency of AGVs has been studied in order to improve environmental sustainability in terms of a linear function, which is based on load and distance, accordingly. Because of the NP-hard characteristics of the problem, a modified multi-objective particle swarm optimization (MMOPSO) has been developed for solving the model and compared with the classic version of the multi-objective particle swarm optimization (MOPSO) algorithm in terms of five performance metrics. Finally, the results are evaluated by the application of a multi-criteria decision-making (MCDM) algorithm according to which the MMOPSO outperforms the MOPSO.
Text
AGV final 08.09.2020-JCLP
- Accepted Manuscript
More information
Accepted/In Press date: 8 October 2020
e-pub ahead of print date: 29 October 2020
Published date: 2 October 2021
Additional Information:
Funding Information: research was supported by the Czech Science Foundation (GACR) , Project number GA18- 15530 S .
Keywords:
Automated guided vehicle (AGV), Flexible manufacturing systems (FMS), Multi-objective particle swarm optimization (MOPSO), Scheduling
Identifiers
Local EPrints ID: 445562
URI: http://eprints.soton.ac.uk/id/eprint/445562
ISSN: 0959-6526
PURE UUID: f68b49c8-a4ad-41b2-b1d7-008d23cc6ce0
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Date deposited: 16 Dec 2020 17:31
Last modified: 17 Mar 2024 06:10
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Contributors
Author:
Reza Moghdani
Author:
Hamidreza Maghsoudlou
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