Bi-objective vibration damping optimization for congested location–pricing problem |
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| Affiliation: | 1. Department of Supply Chain Analytics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;2. Panalpina Centre for Manufacturing and Logistics Research, Cardiff Business School, Cardiff University, Cardiff, United Kingdom;3. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands;4. Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway;5. Canada Research Chair in Distribution Management and Interuniversity Research Centre on Enterprise Networks, Logistics, and Transportation, HEC Montréal, Montréal, Canada;1. Department of Engineering Management, ANT/OR – Operations Research Group, University of Antwerp, Belgium;2. Department of Information, Logistics and Innovation, Vrije Universiteit Amsterdam, The Netherlands;3. Department of Quantitative Economics, School of Business and Economics, Maastricht University, The Netherlands |
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| Abstract: | This paper presents a bi-objective mathematical programming model for the restricted facility location problem, under a congestion and pricing policy. Motivated by various applications such as locating server on internet mirror sites and communication networks, this research investigates congested systems with immobile servers and stochastic demand as M/M/m/k queues. For this problem, we consider two simultaneous perspectives; (1) customers who desire to limit waiting time for service and (2) service providers who intend to increase profits. We formulate a bi-objective facility location problem with two objective functions: (i) maximizing total profit of the whole system and (ii) minimizing the sum of waiting time in queues; the model type is mixed-integer nonlinear. Then, a multi-objective optimization algorithm based on vibration theory (so-called multi-objective vibration damping optimization (MOVDO)), is developed to solve the model. Moreover, the Taguchi method is also implemented, using a response metric to tune the parameters. The results are analyzed and compared with a non-dominated sorting genetic algorithm (NSGA-II) as a well-developed multi-objective evolutionary optimization algorithm. Computational results demonstrate the efficiency of the proposed MOVDO to solve large-scale problems. |
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| Keywords: | Restricted facility location Computational intelligence Multi-objective optimization Pricing Queuing theory |
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