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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Shrnutí: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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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2022.105860