A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles
Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical fac...
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| Published in: | Knowledge-based systems Vol. 243; p. 108315 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier B.V
11.05.2022
Elsevier Science Ltd |
| Subjects: | |
| ISSN: | 0950-7051, 1872-7409 |
| Online Access: | Get full text |
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| Abstract | Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical factors (e.g., transport and setup). This study integrates multiple automated guided vehicles (AGVs) into a job-shop environment. We propose a multiobjective scheduling model that considers machine processing, sequence-dependent setup and AGV transport, aiming to simultaneously minimize the makespan, total idle time of machines and total energy consumption of both machines and AGVs. To solve this problem, an effective multiobjective evolutionary algorithm (EMOEA) is developed. Within the EMOEA, an efficient encoding/decoding method is designed to represent and decode each solution. A new crossover operator is proposed for AGV assignment and AGV speed sequences. To balance the exploration and exploitation ability of the EMOEA, an opposition-based learning strategy is incorporated. A total of 75 benchmark instances and a real-world case are used for our experimental study. Taguchi analysis is applied to determine the best combination of key parameters for the EMOEA. Extensive computational experiments show that properly increasing the number of AGVs can shorten the waiting time of machines and achieve a balance between economic and environmental objectives for production systems. The experimental results confirm that the proposed EMOEA is significantly better at solving the problem than three other well-known algorithms. Our findings here have significant managerial implications for real-world manufacturing environments integrated with AGVs.
•Automated guided vehicles (AGVs) are used for energy-efficient job-shop scheduling.•A new multiobjective mathematical model is formulated for the problem.•An effective multiobjective evolutionary algorithm (EMOEA) is designed.•Opposition-based learning is employed to balance exploration and exploitation.•Results confirm the validity of the model and efficacy of the proposed EMOEA. |
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| AbstractList | Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical factors (e.g., transport and setup). This study integrates multiple automated guided vehicles (AGVs) into a job-shop environment. We propose a multiobjective scheduling model that considers machine processing, sequence-dependent setup and AGV transport, aiming to simultaneously minimize the makespan, total idle time of machines and total energy consumption of both machines and AGVs. To solve this problem, an effective multiobjective evolutionary algorithm (EMOEA) is developed. Within the EMOEA, an efficient encoding/decoding method is designed to represent and decode each solution. A new crossover operator is proposed for AGV assignment and AGV speed sequences. To balance the exploration and exploitation ability of the EMOEA, an opposition-based learning strategy is incorporated. A total of 75 benchmark instances and a real-world case are used for our experimental study. Taguchi analysis is applied to determine the best combination of key parameters for the EMOEA. Extensive computational experiments show that properly increasing the number of AGVs can shorten the waiting time of machines and achieve a balance between economic and environmental objectives for production systems. The experimental results confirm that the proposed EMOEA is significantly better at solving the problem than three other well-known algorithms. Our findings here have significant managerial implications for real-world manufacturing environments integrated with AGVs.
•Automated guided vehicles (AGVs) are used for energy-efficient job-shop scheduling.•A new multiobjective mathematical model is formulated for the problem.•An effective multiobjective evolutionary algorithm (EMOEA) is designed.•Opposition-based learning is employed to balance exploration and exploitation.•Results confirm the validity of the model and efficacy of the proposed EMOEA. Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical factors (e.g., transport and setup). This study integrates multiple automated guided vehicles (AGVs) into a job-shop environment. We propose a multiobjective scheduling model that considers machine processing, sequence-dependent setup and AGV transport, aiming to simultaneously minimize the makespan, total idle time of machines and total energy consumption of both machines and AGVs. To solve this problem, an effective multiobjective evolutionary algorithm (EMOEA) is developed. Within the EMOEA, an efficient encoding/decoding method is designed to represent and decode each solution. A new crossover operator is proposed for AGV assignment and AGV speed sequences. To balance the exploration and exploitation ability of the EMOEA, an opposition-based learning strategy is incorporated. A total of 75 benchmark instances and a real-world case are used for our experimental study. Taguchi analysis is applied to determine the best combination of key parameters for the EMOEA. Extensive computational experiments show that properly increasing the number of AGVs can shorten the waiting time of machines and achieve a balance between economic and environmental objectives for production systems. The experimental results confirm that the proposed EMOEA is significantly better at solving the problem than three other well-known algorithms. Our findings here have significant managerial implications for real-world manufacturing environments integrated with AGVs. |
| ArticleNumber | 108315 |
| Author | Budhi, Gregorius Satia Chiong, Raymond Li, Wenfeng He, Lijun Zhang, Yu |
| Author_xml | – sequence: 1 givenname: Lijun surname: He fullname: He, Lijun organization: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, PR China – sequence: 2 givenname: Raymond surname: Chiong fullname: Chiong, Raymond organization: School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia – sequence: 3 givenname: Wenfeng orcidid: 0000-0001-5493-7200 surname: Li fullname: Li, Wenfeng email: liwf@whut.edu.cn organization: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, PR China – sequence: 4 givenname: Gregorius Satia surname: Budhi fullname: Budhi, Gregorius Satia organization: School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia – sequence: 5 givenname: Yu surname: Zhang fullname: Zhang, Yu organization: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, PR China |
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| Keywords | Energy efficiency Evolutionary algorithms Opposition-based learning Automated guided vehicles Multiobjective optimization Job-shop scheduling |
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| SubjectTerms | Algorithms Automated guided vehicles Automation Decoding Encoding Encoding-Decoding Energy consumption Energy efficiency Evolutionary algorithms Experiments Exploitation Genetic algorithms Idling Job shops Job-shop scheduling Learning strategies Machinery Manufacturing Multiobjective optimization Opposition-based learning Pollution Process engineering Production Production scheduling Sequences Shortages Transportation Work environment |
| Title | A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles |
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