Generalizing the notion of schema in genetic algorithms
Artificial Intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Solving the uncapacitated hub location problem using genetic algorithms
Computers and Operations Research
Design and Analysis of Experiments
Design and Analysis of Experiments
Capacitated single allocation hub location problem-A bi-criteria approach
Computers and Operations Research
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Computers and Operations Research
Expert Systems with Applications: An International Journal
Genetic application in a facility location problem with random demand within queuing framework
Journal of Intelligent Manufacturing
An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
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Many research works in mathematical modeling of the facility location problem have been carried out in discrete and continuous optimization area to obtain the optimum number of required facilities along with the relevant allocation processes. This paper proposes a new multi-objective facility-location problem within the batch arrival queuing framework. Three objective functions are considered: (I) minimizing the weighted sum of the waiting and the traveling times, (II) minimizing the maximum idle time pertinent to each facility, and (III) minimizing the total cost associated with the opened facilities. In this way, the best combination of the facilities is determined in the sense of economical, equilibrium, and enhancing service quality viewpoints. As the model is shown strongly NP-hard, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) are proposed to solve the model. Not only new coding is developed in these solution algorithms, but also a random search algorithm is proposed to justify the efficiency of both algorithms. Since the solution-quality of all meta-heuristic algorithms severely depends on their parameters, design of experiments and response surface methodologies have been utilized to calibrate the parameters of both algorithms. Finally, computational results obtained by implementing both algorithms on several problems of different sizes demonstrate the performances of the proposed methodology.