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A genetic algorithm integrated with the initial solution procedure and parameter tuning for capacitated P-median problem

Öksüz, Mehmet Kürşat; Büyüközkan, Kadir; Bal, Alperen; Satoğlu, Şule Itır


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    <subfield code="a">&lt;p&gt;The capacitated p-median problem is a well-known location-allocation problem that is NP-hard. We proposed an advanced&lt;br&gt;
Genetic Algorithm (GA) integrated with an Initial Solution Procedure for this problem to solve the medium and large-size&lt;br&gt;
instances. A 3&lt;sup&gt;3&lt;/sup&gt; Full Factorial Design was performed where three levels were selected for the probability of mutation,&lt;br&gt;
population size, and the number of iterations. Parameter tuning was performed to reach better performance at each&lt;br&gt;
instance. MANOVA and Post-Hoc tests were performed to identify significant parameter levels, considering both computational&lt;br&gt;
time and optimality gap percentage. Real data of Lorena and Senne (2003) and the data set presented by&lt;br&gt;
Stefanello et al. (2015) were used to test the proposed algorithm, and the results were compared with those of the other&lt;br&gt;
heuristics existing in the literature. The proposed GA was able to reach the optimal solution for some of the instances in&lt;br&gt;
contrast to other metaheuristics and the Mat-heuristic, and it reached a solution better than the best known for the largest&lt;br&gt;
instance and found near-optimal solutions for the other cases. The results show that the proposed GA has the potential to&lt;br&gt;
enhance the solutions for large-scale instances. Besides, it was also shown that the parameter tuning process might improve&lt;br&gt;
the solution quality in terms of the objective function and the CPU time of the proposed GA, but the magnitude of&lt;br&gt;
improvement may vary among different instances.&lt;/p&gt;</subfield>
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    <subfield code="a">Bal, Alperen</subfield>
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    <subfield code="a">Capacitated p-median problem</subfield>
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