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Locating abrupt disaster emergency logistics centres using improved artificial bee colony (IABC) algorithm.
Sun, Qiang; Liu, Shupei.
Affiliation
  • Sun Q; Shandong University of Technology, Zibo, China.
  • Liu S; Shandong University of Technology, Zibo, China.
Sci Prog ; 104(2): 368504211016205, 2021.
Article in En | MEDLINE | ID: mdl-33970045
Emergency management is conceptualized as a complex, multi-objective optimization problem related to facility location. However, little research has been performed on the horizontal transportation of emergency logistics centres. This study makes contributions to the multi-objective locating abrupt disaster emergency logistics centres model with the smallest total cost and the largest customer satisfaction. The IABC algorithm is proposed in this paper to solve the multi-objective emergency logistics centres locating problem. IABC algorithm can effectively calculate the optimal location of abrupt disaster emergency logistics centres and the demand for relief materials, and it can solve the rescue time satisfaction for different rescue sites. (1) IABC has better global search capabilities to avoid premature convergence and provide a faster convergence speed, and it has optimal solution accuracy, solution diversity and robustness. (2) From the three optimal objective function values obtained, the optimal objective function values obtained by IABC algorithm are obviously better than ABC and GABC algorithms. (3) From the convergence curves of three objective functions the global search ability and the stability of IABC algorithm are better than those of ABC and GABC algorithm. The improved ABC algorithm has proven to be effective and feasible. However, emergency relief logistics systems are very complex and involve many factors, the proposed model needs to be refined further in the future.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Disasters Type of study: Prognostic_studies Language: En Journal: Sci Prog Year: 2021 Document type: Article Affiliation country: China Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Disasters Type of study: Prognostic_studies Language: En Journal: Sci Prog Year: 2021 Document type: Article Affiliation country: China Country of publication: United kingdom