Volume 15, Issue 4 (1-2019)                   jor 2019, 15(4): 37-60 | Back to browse issues page

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Zarrinpoor N. Multi-Objective Capacitated Facility Location Problem with Chance Constraint and Customer Preference and Solving it with Multi-Objective Evolutionary Algorithms. jor 2019; 15 (4) :37-60
URL: http://jamlu.liau.ac.ir/article-1-982-en.html
Multi-Objective Capacitated Facility Location Problem with Chance Constraint and Customer Preference and Solving it with Multi-Objective Evolutionary Algorithms
N. Zarrinpoor *
Assistant Professor, Department of Industrial Engineering, Shiraz University of Technology, Shiraz, Iran.
Abstract:   (3563 Views)
Facility location decisions are considered as the most important strategic decisions of organizations, and since they need large investment costs, changing in these decisions will be often impossible. Therefore, it seems necessary to decide about facility location with regard to the constraints and assumptions of real world in an optimal way. In this article, a facility location model is proposed from both the service provider’s point of view and customer’s perspective with the objective of minimizing the fixed cost and maximizing captured demands. The customer preference is considered in the model and based on it, customers choose facilities on the basis of the quality, travel time and service expense. With regard to the uncertain nature of customer’s demand in the real world and limited capacity of facilities, chance constraint is taken into account to the model which ensures the customer’s demand will be satisfied with a certain service level. Due to the NP-hard nature of the problem, a multi-objective harmony search (MOHS) algorithm and a non-dominated sorting genetic algorithm-II (NSGA-II) are proposed to solve the model. In order to calibrate the parameters of the proposed algorithms, the Taguchi method is utilized. The performance of proposed algorithms are compared  in terms of different performance metrics such as error ratio, generational distance, spacing metric, diversification metric, number of Pareto-optimal solutions and computational time. Finally, the results are evaluated statistically by 2-sample t-test to determine if there is any significant difference among algorithms in any performance metric. The numerical results show that in total MOHS outperforms NSGA-II.
 
Keywords: Facility Location, Capacity constraint, Customer preference, Chance constraint, Multi-objective harmony search, NSGA-II
Full-Text [PDF 942 kb]   (1590 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/03/18 | Accepted: 2018/02/25 | Published: 2019/01/15
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