Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Budget constrained location problem with opening and closing of facilities
Computers and Operations Research
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A multiple server location-allocation model for service system design
Computers and Operations Research
Expert Systems with Applications: An International Journal
Performance optimization of open zero-buffer multi-server queueing networks
Computers and Operations Research
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
Harmony search algorithm for solving Sudoku
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Soft-computing based heuristics for location on networks: The p-median problem
Applied Soft Computing
Survey: Covering problems in facility location: A review
Computers and Industrial Engineering
Survey: Facility location dynamics: An overview of classifications and applications
Computers and Industrial Engineering
Computers and Industrial Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Genetic application in a facility location problem with random demand within queuing framework
Journal of Intelligent Manufacturing
International Journal of Systems Science
Journal of Intelligent Manufacturing
Survey A survey on applications of the harmony search algorithm
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
In this paper, a novel multi-objective location model within multi-server queuing framework is proposed, in which facilities behave as M/M/m queues. In the developed model of the problem, the constraints of selecting the nearest-facility along with the service level restriction are considered to bring the model closer to reality. Three objective functions are also considered including minimizing (I) sum of the aggregate travel and waiting times, (II) maximum idle time of all facilities, and (III) the budget required to cover the costs of establishing the selected facilities plus server staffing costs. Since the developed model of the problem is of an NP-hard type and inexact solutions are more probable to be obtained, soft computing techniques, specifically evolutionary computations, are generally used to cope with the lack of precision. From different terms of evolutionary computations, this paper proposes a Pareto-based meta-heuristic algorithm called multi-objective harmony search (MOHS) to solve the problem. To validate the results obtained, two popular algorithms including non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are utilized as well. In order to demonstrate the proposed methodology and to compare the performances in terms of Pareto-based solution measures, the Taguchi approach is first utilized to tune the parameters of the proposed algorithms, where a new response metric named multi-objective coefficient of variation (MOCV) is introduced. Then, the results of implementing the algorithms on some test problems show that the proposed MOHS outperforms the other two algorithms in terms of computational time.