Joint optimization of demand-side operational utility and manufacture-side energy consumption in a distributed parallel machine environment
•Energy-efficient scheduling model with demand-side operational utility is studied.•An optimal speed adjustment strategy is designed to improve operational utility.•A problem-dependent memetic algorithm is presented.•Elaborate tests are conducted to verify the performance of the memetic algorithm. P...
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| Vydané v: | Computers & industrial engineering Ročník 164; s. 107863 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.02.2022
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | •Energy-efficient scheduling model with demand-side operational utility is studied.•An optimal speed adjustment strategy is designed to improve operational utility.•A problem-dependent memetic algorithm is presented.•Elaborate tests are conducted to verify the performance of the memetic algorithm.
Previous production scheduling models often set optimization objectives from the perspective of manufacturers, such as makespan, tardiness and energy consumption. However, none of the objectives can reflect the extent to which the scheduling plan affects the demand side. In fact, the delivery time of orders will directly affect the equipment utilization or project schedule on the demand side. In this paper, we focus on a new objective named total operational utility of all distributed equipment from the demand side, and integrate it into an energy-efficient production scheduling model based on the distributed parallel machine environment, in which the total energy consumption of manufacture side including processing energy consumption and transportation energy consumption is another objective. The orders are the spare parts used to replace the deteriorated components of distributed equipment based on forecasting information. Based on the scheduled delivery time fed back from the scheduling plan, the relationship among operating speed, deterioration rate and operating efficiency is used, and an optimal speed adjustment strategy is developed for each equipment to improve the operational utility. A memetic algorithm (NMA) based on the structure of NSGA-Ⅱ is presented for the model. A list scheduling heuristic and a problem-dependent heuristic are designed to generate initial population. Two problem-dependent local search operators are developed to enhance the searching ability. By performing extensive experiments and comparing NMA with some well-known algorithms, the effectiveness and superiority of NMA are demonstrated. |
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| AbstractList | •Energy-efficient scheduling model with demand-side operational utility is studied.•An optimal speed adjustment strategy is designed to improve operational utility.•A problem-dependent memetic algorithm is presented.•Elaborate tests are conducted to verify the performance of the memetic algorithm.
Previous production scheduling models often set optimization objectives from the perspective of manufacturers, such as makespan, tardiness and energy consumption. However, none of the objectives can reflect the extent to which the scheduling plan affects the demand side. In fact, the delivery time of orders will directly affect the equipment utilization or project schedule on the demand side. In this paper, we focus on a new objective named total operational utility of all distributed equipment from the demand side, and integrate it into an energy-efficient production scheduling model based on the distributed parallel machine environment, in which the total energy consumption of manufacture side including processing energy consumption and transportation energy consumption is another objective. The orders are the spare parts used to replace the deteriorated components of distributed equipment based on forecasting information. Based on the scheduled delivery time fed back from the scheduling plan, the relationship among operating speed, deterioration rate and operating efficiency is used, and an optimal speed adjustment strategy is developed for each equipment to improve the operational utility. A memetic algorithm (NMA) based on the structure of NSGA-Ⅱ is presented for the model. A list scheduling heuristic and a problem-dependent heuristic are designed to generate initial population. Two problem-dependent local search operators are developed to enhance the searching ability. By performing extensive experiments and comparing NMA with some well-known algorithms, the effectiveness and superiority of NMA are demonstrated. |
| ArticleNumber | 107863 |
| Author | Gong, Guiliang Liu, Xiaoyan Deng, Qianwang Fan, Qing Zhang, Like Zhao, Yan |
| Author_xml | – sequence: 1 givenname: Like surname: Zhang fullname: Zhang, Like email: likezhang@hnu.edu.cn organization: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China – sequence: 2 givenname: Qianwang surname: Deng fullname: Deng, Qianwang email: deng_arbeit@hnu.edu.cn organization: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China – sequence: 3 givenname: Yan surname: Zhao fullname: Zhao, Yan email: zy1593969724@163.com organization: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China – sequence: 4 givenname: Qing surname: Fan fullname: Fan, Qing email: fqzlzk@163.com organization: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China – sequence: 5 givenname: Xiaoyan surname: Liu fullname: Liu, Xiaoyan email: xiaoyan.liu@hnu.edu.cn organization: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China – sequence: 6 givenname: Guiliang surname: Gong fullname: Gong, Guiliang email: gongguiliang@hnu.edu.cn organization: Department of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China |
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