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
Hlavní autoři:	Hu, Zhineng, Wang, Li, Qin, Jindong, Lev, Benjamin, Gan, Lu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Elsevier Ltd 01.09.2022 Pergamon Press Inc
Témata:	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 Bi-level programming Multi-objective programming Particle swarm optimization Facility location and size problem
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.
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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Snippet	With the rapid urbanization, solving the facility location and size problem (FLSP) of general service infrastructure (GSI) has become an essential issue in...
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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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