12 Nisan 2023 Dergi makalesi Açık Erişim

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

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{
  "@context": "https://schema.org/", 
  "@id": 263031, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0001-5791-3845", 
      "@type": "Person", 
      "affiliation": "Erzincan Binali Y\u0131ld\u0131r\u0131m \u00dcniversitesi", 
      "name": "\u00d6ks\u00fcz, Mehmet K\u00fcr\u015fat"
    }, 
    {
      "@id": "https://orcid.org/0000-0001-6321-0302", 
      "@type": "Person", 
      "affiliation": "Karadeniz Teknik \u00dcniversitesi", 
      "name": "B\u00fcy\u00fck\u00f6zkan, Kadir"
    }, 
    {
      "@id": "https://orcid.org/0000-0003-0675-0796", 
      "@type": "Person", 
      "affiliation": "American University of the Middle East", 
      "name": "Bal, Alperen"
    }, 
    {
      "@id": "https://orcid.org/0000-0003-2768-4038", 
      "@type": "Person", 
      "affiliation": "\u0130stanbul Teknik \u00dcniversitesi", 
      "name": "Sato\u011flu, \u015eule It\u0131r"
    }
  ], 
  "datePublished": "2023-04-12", 
  "description": "<p>The capacitated p-median problem is a well-known location-allocation problem that is NP-hard. We proposed an advanced<br>\nGenetic Algorithm (GA) integrated with an Initial Solution Procedure for this problem to solve the medium and large-size<br>\ninstances. A 3<sup>3</sup> Full Factorial Design was performed where three levels were selected for the probability of mutation,<br>\npopulation size, and the number of iterations. Parameter tuning was performed to reach better performance at each<br>\ninstance. MANOVA and Post-Hoc tests were performed to identify significant parameter levels, considering both computational<br>\ntime and optimality gap percentage. Real data of Lorena and Senne (2003) and the data set presented by<br>\nStefanello et al. (2015) were used to test the proposed algorithm, and the results were compared with those of the other<br>\nheuristics existing in the literature. The proposed GA was able to reach the optimal solution for some of the instances in<br>\ncontrast to other metaheuristics and the Mat-heuristic, and it reached a solution better than the best known for the largest<br>\ninstance and found near-optimal solutions for the other cases. The results show that the proposed GA has the potential to<br>\nenhance the solutions for large-scale instances. Besides, it was also shown that the parameter tuning process might improve<br>\nthe solution quality in terms of the objective function and the CPU time of the proposed GA, but the magnitude of<br>\nimprovement may vary among different instances.</p>", 
  "headline": "A genetic algorithm integrated with the initial solution procedure and parameter tuning for capacitated P-median problem", 
  "identifier": 263031, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "keywords": [
    "Location-Allocation", 
    "Capacitated p-median problem", 
    "Facility location", 
    "Genetic algorithm", 
    "Initial solution algorithm", 
    "Parameter tuning"
  ], 
  "license": "https://creativecommons.org/licenses/by-nc-nd/4.0/", 
  "name": "A genetic algorithm integrated with the initial solution procedure and parameter tuning for capacitated P-median problem", 
  "url": "https://aperta.ulakbim.gov.tr/record/263031"
}