A multi-objective bi-level location planning problem for stone industrial parks. (April 2015)

• Record Type: Journal Article
• Authors: Gang, Jun; Tu, Yan; Lev, Benjamin; Xu, Jiuping; Shen, Wenjing; Yao, Liming
• Is Part Of: Computers & operations research. Volume 56(2015)
• Page Start: 8
• Page End: 21
• Publication Date: 2015-04
• Subjects: Stone industry -- Location -- Multi-objective bi-level optimization -- Random variables -- Particle swarm optimization; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2014.10.005
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: This paper focuses on a stone industrial park location problem with a hierarchical structure consisting of a local government and several stone enterprises under a random environment. In contrast to previous studies, conflicts between the local authority and the stone enterprises are considered. The local government, being the leader in the hierarchy, aims to minimize both total pollution emissions and total development and operating costs. The stone enterprises, as the followers in the hierarchy, only aim to minimize total costs. In addition, unit production cost and unit transportation cost are considered random variables. This complicated multi-objective bi-level optimization problem poses several challenges, including randomness, two-level decision making, conflicting objectives, and difficulty in searching for the optimal solutions. Various approaches are employed to tackle these challenges. In order to make the model trackable, expected value operator is used to deal with the random variables in the objective functions and a chance constraint-checking method is employed to deal with such variables in the constraints. The problem is solved using a bi-level interactive method based on a satisfactory solution and Adaptive Chaotic Particle Swarm Optimization (ACPSO). Finally, a case study is conducted to demonstrate the practicality and efficiency of the proposed model and solution algorithm. The performance of the proposed bi-level model and ACPSO algorithm was highlighted by comparing to a single-level model and basic PSO and GA algorithms.