Optimization of facility location and size problem based on bi-level multi-objective programming
With the rapid urbanization, solving the facility location and size problem (FLSP) of general service infrastructure (GSI) has become an essential issue in spatial planning. Due to unreasonable location and regional scale, the satisfaction of residents has been seriously affected. This paper develop...
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| Vydáno v: | Computers & operations research Ročník 145; s. 105860 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Elsevier Ltd
01.09.2022
Pergamon Press Inc |
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| ISSN: | 0305-0548, 1873-765X, 0305-0548 |
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| Abstract | With the rapid urbanization, solving the facility location and size problem (FLSP) of general service infrastructure (GSI) has become an essential issue in spatial planning. Due to unreasonable location and regional scale, the satisfaction of residents has been seriously affected. This paper develops a bi-level multi-objective programming (BLMOP) to optimize both facility location and size. Three major problems have been addressed: (1) solving the contradiction between supply and demand; (2) keeping a balance of social, economic, and environmental benefits; and (3) designing a multi-objective particle swarm optimization (MOPSO) algorithm by modifying the parameters and learning strategies. To obtain feasible solutions, a combination of optimistic and pessimistic approaches is adopted. Taking the rural areas of Southwest China as an example, the results find that the proposed model enables to provide objective-oriented optimization schemes depending on the decision-maker’s (DM) preferences. Furthermore, the MOPSO algorithm can solve the BLMOP and provide Pareto-optimal solutions separately.
•FLSP is optimized by balancing supply and demand.•Trade-offs between social, economic, and environmental impacts in FLSP are addressed.•MOPSO algorithm is designed to solve nonlinear bi-level programming.•Objective-oriented FLSP schemes are obtained depending on DMs’ preferences. |
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| AbstractList | With the rapid urbanization, solving the facility location and size problem (FLSP) of general service infrastructure (GSI) has become an essential issue in spatial planning. Due to unreasonable location and regional scale, the satisfaction of residents has been seriously affected. This paper develops a bi-level multi-objective programming (BLMOP) to optimize both facility location and size. Three major problems have been addressed: (1) solving the contradiction between supply and demand; (2) keeping a balance of social, economic, and environmental benefits; and (3) designing a multi-objective particle swarm optimization (MOPSO) algorithm by modifying the parameters and learning strategies. To obtain feasible solutions, a combination of optimistic and pessimistic approaches is adopted. Taking the rural areas of Southwest China as an example, the results find that the proposed model enables to provide objective-oriented optimization schemes depending on the decision-maker's (DM) preferences. Furthermore, the MOPSO algorithm can solve the BLMOP and provide Pareto-optimal solutions separately. With the rapid urbanization, solving the facility location and size problem (FLSP) of general service infrastructure (GSI) has become an essential issue in spatial planning. Due to unreasonable location and regional scale, the satisfaction of residents has been seriously affected. This paper develops a bi-level multi-objective programming (BLMOP) to optimize both facility location and size. Three major problems have been addressed: (1) solving the contradiction between supply and demand; (2) keeping a balance of social, economic, and environmental benefits; and (3) designing a multi-objective particle swarm optimization (MOPSO) algorithm by modifying the parameters and learning strategies. To obtain feasible solutions, a combination of optimistic and pessimistic approaches is adopted. Taking the rural areas of Southwest China as an example, the results find that the proposed model enables to provide objective-oriented optimization schemes depending on the decision-maker’s (DM) preferences. Furthermore, the MOPSO algorithm can solve the BLMOP and provide Pareto-optimal solutions separately. •FLSP is optimized by balancing supply and demand.•Trade-offs between social, economic, and environmental impacts in FLSP are addressed.•MOPSO algorithm is designed to solve nonlinear bi-level programming.•Objective-oriented FLSP schemes are obtained depending on DMs’ preferences. |
| ArticleNumber | 105860 |
| Author | Qin, Jindong Gan, Lu Wang, Li Hu, Zhineng Lev, Benjamin |
| Author_xml | – sequence: 1 givenname: Zhineng surname: Hu fullname: Hu, Zhineng organization: Business School, Sichuan University, Chengdu, 610064, China – sequence: 2 givenname: Li surname: Wang fullname: Wang, Li organization: Business School, Sichuan University, Chengdu, 610064, China – sequence: 3 givenname: Jindong surname: Qin fullname: Qin, Jindong organization: School of Management, Wuhan University of Technology, Wuhan, 430070, China – sequence: 4 givenname: Benjamin surname: Lev fullname: Lev, Benjamin organization: LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA – sequence: 5 givenname: Lu orcidid: 0000-0001-9062-5204 surname: Gan fullname: Gan, Lu email: ganlu_soarpb@sicau.edu.cn organization: Business School, Sichuan University, Chengdu, 610064, China |
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| Keywords | Bi-level programming Multi-objective programming Particle swarm optimization Facility location and size problem |
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| SubjectTerms | Algorithms Bi-level programming Decision making Facility location and size problem Machine learning Mathematical programming Multi-objective programming Multiple objective analysis Operations research Optimization Parameter modification Pareto optimum Particle swarm optimization Regional planning Rural areas Urbanization |
| Title | Optimization of facility location and size problem based on bi-level multi-objective programming |
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