A Knowledge-Based Two-Population Optimization Algorithm for Distributed Energy-Efficient Parallel Machines Scheduling

In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-e...

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Vydané v:	IEEE transactions on cybernetics Ročník 52; číslo 6; s. 5051 - 5063
Hlavní autori:	Pan, Zixiao, Lei, Deming, Wang, Ling
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Distributed scheduling Energy consumption energy-efficient scheduling Evolutionary computation Genetic algorithms Job shop scheduling knowledge Knowledge based systems Optimization Parallel machines parallel machines scheduling Population Production facilities Scheduling Sorting algorithms Statistical analysis two-population
ISSN:	2168-2267, 2168-2275, 2168-2275
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Abstract	In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-efficient parallel machines scheduling problem (DEPMSP) has not been studied yet. This article aims to solve DEPMSP by integrating factory assignment and machine assignment into an extended machine assignment to handle the coupled relations of subproblems. A knowledge-based two-population optimization (KTPO) algorithm is proposed to minimize total energy consumption and total tardiness simultaneously. Five properties are derived by analyzing the characteristics of DEPMSP. The population is initialized by using two heuristics based on problem-specific knowledge and a random heuristic. The nondominated sorting genetic algorithm-II and differential evolution perform cooperatively on the population in parallel. Moreover, two knowledge-based local search operators are proposed to enhance the exploitation. Extensive simulation experiments are conducted by comparing KTPO with four algorithms from the literature. The comparative results and statistical analysis demonstrate the effectiveness and advantages of KTPO in solving DEPMSP.
AbstractList	In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-efficient parallel machines scheduling problem (DEPMSP) has not been studied yet. This article aims to solve DEPMSP by integrating factory assignment and machine assignment into an extended machine assignment to handle the coupled relations of subproblems. A knowledge-based two-population optimization (KTPO) algorithm is proposed to minimize total energy consumption and total tardiness simultaneously. Five properties are derived by analyzing the characteristics of DEPMSP. The population is initialized by using two heuristics based on problem-specific knowledge and a random heuristic. The nondominated sorting genetic algorithm-II and differential evolution perform cooperatively on the population in parallel. Moreover, two knowledge-based local search operators are proposed to enhance the exploitation. Extensive simulation experiments are conducted by comparing KTPO with four algorithms from the literature. The comparative results and statistical analysis demonstrate the effectiveness and advantages of KTPO in solving DEPMSP. In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-efficient parallel machines scheduling problem (DEPMSP) has not been studied yet. This article aims to solve DEPMSP by integrating factory assignment and machine assignment into an extended machine assignment to handle the coupled relations of subproblems. A knowledge-based two-population optimization (KTPO) algorithm is proposed to minimize total energy consumption and total tardiness simultaneously. Five properties are derived by analyzing the characteristics of DEPMSP. The population is initialized by using two heuristics based on problem-specific knowledge and a random heuristic. The nondominated sorting genetic algorithm-II and differential evolution perform cooperatively on the population in parallel. Moreover, two knowledge-based local search operators are proposed to enhance the exploitation. Extensive simulation experiments are conducted by comparing KTPO with four algorithms from the literature. The comparative results and statistical analysis demonstrate the effectiveness and advantages of KTPO in solving DEPMSP.In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems, the distributed energy-efficient scheduling problem is of great realistic significance. To the best of our knowledge, the distributed energy-efficient parallel machines scheduling problem (DEPMSP) has not been studied yet. This article aims to solve DEPMSP by integrating factory assignment and machine assignment into an extended machine assignment to handle the coupled relations of subproblems. A knowledge-based two-population optimization (KTPO) algorithm is proposed to minimize total energy consumption and total tardiness simultaneously. Five properties are derived by analyzing the characteristics of DEPMSP. The population is initialized by using two heuristics based on problem-specific knowledge and a random heuristic. The nondominated sorting genetic algorithm-II and differential evolution perform cooperatively on the population in parallel. Moreover, two knowledge-based local search operators are proposed to enhance the exploitation. Extensive simulation experiments are conducted by comparing KTPO with four algorithms from the literature. The comparative results and statistical analysis demonstrate the effectiveness and advantages of KTPO in solving DEPMSP.
Author	Pan, Zixiao Lei, Deming Wang, Ling
Author_xml	– sequence: 1 givenname: Zixiao orcidid: 0000-0001-5153-5564 surname: Pan fullname: Pan, Zixiao email: pzx19@mails.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing, China – sequence: 2 givenname: Deming orcidid: 0000-0002-3388-950X surname: Lei fullname: Lei, Deming email: deminglei11@163.com organization: School of Automation, Wuhan University of Technology, Wuhan, China – sequence: 3 givenname: Ling orcidid: 0000-0003-1226-2801 surname: Wang fullname: Wang, Ling email: wangling@mail.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing, China
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Snippet	In recent years, both distributed scheduling problem and energy-efficient scheduling have attracted much attention. As the integration of these two problems,...
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SubjectTerms	Algorithms Distributed scheduling Energy consumption energy-efficient scheduling Evolutionary computation Genetic algorithms Job shop scheduling knowledge Knowledge based systems Optimization Parallel machines parallel machines scheduling Population Production facilities Scheduling Sorting algorithms Statistical analysis two-population
Title	A Knowledge-Based Two-Population Optimization Algorithm for Distributed Energy-Efficient Parallel Machines Scheduling
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