An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery
This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while min...
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| Published in: | Knowledge-based systems Vol. 218; p. 106881 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier B.V
22.04.2021
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
| Online Access: | Get full text |
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| Abstract | This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while minimizing distribution cost. For this purpose, a multi-objective mixed-integer linear programming model is first formulated. Then an effective multi-objective evolutionary algorithm is developed for solving the problem. In the algorithm, a constructive heuristic is presented and incorporated into the population initialization. A multi-objective local search based on an ideal-point is used to enforce the exploitation capability. A novel two-point crossover operator is designed to make full use of valuable information collected in the non-dominated solutions. A restart strategy is proposed to avoid the algorithm trapping into a local optimum. At last, a series of comparative experiments are implemented based on a number of real-world instances from an electronic equipment manufacturing enterprise. The results show that the proposed algorithm has a significantly better performance than the existing multi-objective algorithms for solving the problem under consideration. |
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| AbstractList | This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while minimizing distribution cost. For this purpose, a multi-objective mixed-integer linear programming model is first formulated. Then an effective multi-objective evolutionary algorithm is developed for solving the problem. In the algorithm, a constructive heuristic is presented and incorporated into the population initialization. A multi-objective local search based on an ideal-point is used to enforce the exploitation capability. A novel two-point crossover operator is designed to make full use of valuable information collected in the non-dominated solutions. A restart strategy is proposed to avoid the algorithm trapping into a local optimum. At last, a series of comparative experiments are implemented based on a number of real-world instances from an electronic equipment manufacturing enterprise. The results show that the proposed algorithm has a significantly better performance than the existing multi-objective algorithms for solving the problem under consideration. |
| ArticleNumber | 106881 |
| Author | Pan, Quan-Ke Zou, Wen-Qiang Wang, Ling |
| Author_xml | – sequence: 1 givenname: Wen-Qiang surname: Zou fullname: Zou, Wen-Qiang organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, PR China – sequence: 2 givenname: Quan-Ke surname: Pan fullname: Pan, Quan-Ke email: panquanke@shu.edu.cn organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, PR China – sequence: 3 givenname: Ling orcidid: 0000-0003-1226-2801 surname: Wang fullname: Wang, Ling organization: Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, PR China |
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| Keywords | Matrix manufacturing workshop Scheduling Pickup and delivery Multi-objective evolutionary algorithm Automated guided vehicles |
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| SubjectTerms | Algorithms Automated guided vehicles Batch production Client satisfaction Crossovers Customer satisfaction Distribution costs Electronic equipment Evolutionary algorithms Experiments Exploitation Genetic algorithms Heuristic Integer programming Linear programming Manufacturing Matrix manufacturing workshop Mixed integer Multi-objective evolutionary algorithm Objectives Pickup and delivery Satisfaction Scheduling |
| Title | An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery |
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