Paper Info
Title
A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem
Abstract
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.
Year
DOI
Venue
2013
10.1016/j.asoc.2012.12.016
Appl. Soft Comput.
Keywords
Field
DocType
proposed mohs,multi-objective coefficient,proposed methodology,novel multi-objective location model,developed model,genetic algorithm,evolutionary computation,multi-objective harmony search,soft-computing pareto-based meta-heuristic algorithm,multi-objective multi-server facility location,pareto-based meta-heuristic algorithm,proposed algorithm,queuing system,soft computing
Facility location problem,Queueing theory,Artificial intelligence,Soft computing,Location model,Genetic algorithm,Mathematical optimization,Algorithm,Sorting,Harmony search,Pareto principle,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
13
4
1568-4946
Citations 
PageRank 
References 
27
1.02
29
Authors
5
Name
Order
Citations
PageRank
Seyed Habib A. Rahmati11657.56
Vahid Hajipour2796.26
Seyed Taghi Akhavan Niaki362457.47
A RahmatiSeyed Habib4271.02
NiakiSeyed Taghi Akhavan5944.17