Optimization of facility location and size problem based on bi-level multi-objective programming. (September 2022)

• Record Type: Journal Article
• Authors: Hu, Zhineng; Wang, Li; Qin, Jindong; Lev, Benjamin; Gan, Lu
• Is Part Of: Computers & operations research. Volume 145(2022)
• Publication Date: 2022-09
• Subjects: Facility location and size problem -- Bi-level programming -- Multi-objective programming -- Particle swarm optimization; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2022.105860
• Languages: English
• ISSNs: 0305-0548

Abstract

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. Highlights: 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.