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Energy-efficient multi-objective flexible manufacturing scheduling

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
0959-6526
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Moghdani, Reza
f3ccdd7d-145d-4c95-bb47-23c1341df155
Maghsoudlou, Hamidreza
31029a70-7ce3-45c8-8db1-8218d39345e0
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).

Record type: Article

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.

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AGV final 08.09.2020-JCLP - Accepted Manuscript
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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
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

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Date deposited: 16 Dec 2020 17:31
Last modified: 28 Apr 2022 05:55

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Contributors

Author: Sasan Barak ORCID iD
Author: Reza Moghdani
Author: Hamidreza Maghsoudlou

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