Energy-Aware Flexible Job Shop Scheduling Using Mixed Integer Programming and Constraint Programming

Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shut...

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Vydáno v:Mathematical problems in engineering Ročník 2021; s. 1 - 12
Hlavní autoři: Ham, Andy, Park, Myoung-Ju, Kim, Kyung Min
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 30.06.2021
John Wiley & Sons, Inc
Témata:
Constraints
Energy conservation
Energy consumption
Energy efficiency
Energy management
Engineering
Genetic algorithms
Integer programming
Job shop scheduling
Job shops
Linear programming
Literature reviews
Manufacturing
Methods
Mixed integer
Productivity
Scheduling
Shutdowns
ISSN:1024-123X, 1563-5147
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Abstract Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.
AbstractList Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.
Author Ham, Andy
Park, Myoung-Ju
Kim, Kyung Min
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  surname: Kim
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  organization: Industrial Management and EngineeringMyong Ji UniversitySeoulRepublic of Korea
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Copyright Copyright © 2021 Andy Ham et al.
Copyright © 2021 Andy Ham et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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Snippet Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain...
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SubjectTerms Constraints
Energy conservation
Energy consumption
Energy efficiency
Energy management
Engineering
Genetic algorithms
Integer programming
Job shop scheduling
Job shops
Linear programming
Literature reviews
Manufacturing
Methods
Mixed integer
Productivity
Scheduling
Shutdowns
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