A multi-population, multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines
•Energy-efficient job-shop scheduling with deteriorating machines is studied.•Green production and tardiness related objectives are considered.•A multi-population, multi-objective memetic algorithm is proposed for the problem.•The proposed algorithm exhibits superior performance across a range of me...
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| Vydáno v: | Expert systems with applications Ročník 157; s. 113348 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Elsevier Ltd
01.11.2020
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •Energy-efficient job-shop scheduling with deteriorating machines is studied.•Green production and tardiness related objectives are considered.•A multi-population, multi-objective memetic algorithm is proposed for the problem.•The proposed algorithm exhibits superior performance across a range of metrics.
This paper focuses on an energy-efficient job-shop scheduling problem within a machine speed scaling framework, where productivity is affected by deterioration. To alleviate the deterioration effect, necessary maintenance activities must be put in place during the scheduling process. In addition to sequencing operations on machines, the problem at hand aims to determine the appropriate speeds of machines and positions of maintenance activities for the schedule, in order to minimise the total weighted tardiness and total energy consumption simultaneously. To deal with this problem, a multi-population, multi-objective memetic algorithm is proposed, in which the solutions are distributed into sub-populations. Besides a general local search, an advanced objective-oriented local search is also executed periodically on a portion of the population. These local search methods are designed based on a new disjunctive graph introduced to cover the solution space. Furthermore, an efficient non-dominated sorting method for bi-objective optimisation is developed. The performance of the memetic algorithm is evaluated via a series of comprehensive computational experiments, comparing it with state-of-the-art algorithms presented for job-shop scheduling problems with/without considering energy efficiency. Experimental results confirm that the proposed algorithm can outperform other algorithms being compared across a range of performance metrics. |
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| AbstractList | This paper focuses on an energy-efficient job-shop scheduling problem within a machine speed scaling framework, where productivity is affected by deterioration. To alleviate the deterioration effect, necessary maintenance activities must be put in place during the scheduling process. In addition to sequencing operations on machines, the problem at hand aims to determine the appropriate speeds of machines and positions of maintenance activities for the schedule, in order to minimise the total weighted tardiness and total energy consumption simultaneously. To deal with this problem, a multi-population, multi-objective memetic algorithm is proposed, in which the solutions are distributed into sub-populations. Besides a general local search, an advanced objective-oriented local search is also executed periodically on a portion of the population. These local search methods are designed based on a new disjunctive graph introduced to cover the solution space. Furthermore, an efficient non-dominated sorting method for bi-objective optimisation is developed. The performance of the memetic algorithm is evaluated via a series of comprehensive computational experiments, comparing it with state-of-the-art algorithms presented for job-shop scheduling problems with/without considering energy efficiency. Experimental results confirm that the proposed algorithm can outperform other algorithms being compared across a range of performance metrics. •Energy-efficient job-shop scheduling with deteriorating machines is studied.•Green production and tardiness related objectives are considered.•A multi-population, multi-objective memetic algorithm is proposed for the problem.•The proposed algorithm exhibits superior performance across a range of metrics. This paper focuses on an energy-efficient job-shop scheduling problem within a machine speed scaling framework, where productivity is affected by deterioration. To alleviate the deterioration effect, necessary maintenance activities must be put in place during the scheduling process. In addition to sequencing operations on machines, the problem at hand aims to determine the appropriate speeds of machines and positions of maintenance activities for the schedule, in order to minimise the total weighted tardiness and total energy consumption simultaneously. To deal with this problem, a multi-population, multi-objective memetic algorithm is proposed, in which the solutions are distributed into sub-populations. Besides a general local search, an advanced objective-oriented local search is also executed periodically on a portion of the population. These local search methods are designed based on a new disjunctive graph introduced to cover the solution space. Furthermore, an efficient non-dominated sorting method for bi-objective optimisation is developed. The performance of the memetic algorithm is evaluated via a series of comprehensive computational experiments, comparing it with state-of-the-art algorithms presented for job-shop scheduling problems with/without considering energy efficiency. Experimental results confirm that the proposed algorithm can outperform other algorithms being compared across a range of performance metrics. |
| ArticleNumber | 113348 |
| Author | Abedi, Mehdi Noman, Nasimul Zhang, Rui Chiong, Raymond |
| Author_xml | – sequence: 1 givenname: Mehdi surname: Abedi fullname: Abedi, Mehdi email: Mehdi.Abedi@uon.edu.au organization: School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW 2308, Australia – sequence: 2 givenname: Raymond surname: Chiong fullname: Chiong, Raymond email: Raymond.Chiong@newcastle.edu.au organization: School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW 2308, Australia – sequence: 3 givenname: Nasimul surname: Noman fullname: Noman, Nasimul email: Nasimul.Noman@newcastle.edu.au organization: School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW 2308, Australia – sequence: 4 givenname: Rui surname: Zhang fullname: Zhang, Rui email: r.zhang@ymail.com organization: School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China |
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| Keywords | Cumulative deterioration effect Periodic local search Maintenance activity Memetic algorithm Machine speed scaling Job-shop scheduling |
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| Snippet | •Energy-efficient job-shop scheduling with deteriorating machines is studied.•Green production and tardiness related objectives are considered.•A... This paper focuses on an energy-efficient job-shop scheduling problem within a machine speed scaling framework, where productivity is affected by... |
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| SubjectTerms | Algorithms Cumulative deterioration effect Deterioration Energy consumption Energy efficiency Job shop scheduling Job shops Machine speed scaling Maintenance Maintenance activity Memetic algorithm Optimization Performance measurement Periodic local search Schedules Scheduling Searching Sequences Solution space |
| Title | A multi-population, multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines |
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