A self-adaptive evolutionary algorithm for a fuzzy multi-objective hub location problem: An integration of responsiveness and social responsibility. (June 2017)

• Record Type: Journal Article
• Authors: Zhalechian, Mohammad; Tavakkoli-Moghaddam, Reza; Rahimi, Yaser
• Is Part Of: Engineering applications of artificial intelligence. Volume 62(2017:Feb.)
• Page Start: 1
• Page End: 16
• Publication Date: 2017-06
• Subjects: Hub location problem -- Responsiveness -- Social responsibility -- Uncertainty -- Evolutionary algorithm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.03.006
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, we present a new multi-objective model for a hub location problem under uncertainty. This model simultaneously considers economic, responsiveness and social aspects in designing a hub-and-spoke network. An M / M / c queuing system is used to calculate waiting time at each hub node and maximize responsiveness. Employment and regional development are selected as social responsibility measures in the proposed model. Furthermore, a hybrid two-phase solution method is proposed based on possibilistic programming, fuzzy multi-objective programming and an efficient algorithm, called self-adaptive differential evolution algorithm. Finally, several numerical experiments and sensitivity analyses are carried out to assess the proposed model and the solution method.