Abstract
The present paper proposes a Greedy based Pareto Local Search (GB-PLS) algorithm for the bi-objective robust airport gate assignment problem (bRAGAP). The bRAGAP requires to minimize the total passenger walking distance and the total robust cost of gate assignment. The robust cost is measured through our proposed evaluation function considering the impact of delay cost on the allocation of idle time. GB-PLS uses the Random and Greedy Move (RGM) as a neighborhood search operator to improve the convergence and diversity of the solutions. Two populations are maintained in GB-PLS: the external population (EP) stores the nondominated solutions and the starting population (SP) maintains all the starting solutions for Pareto local search (PLS). The PLS is applied to search the neighborhood of each solution in the SP and the generated solutions are used to update the EP. A number of extensive experiments has been conducted to validate the performance of GB-PLS over Pareto Simulated Annealing (PSA).
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant 61300159, by the Natural Science Foundation of Jiangsu Province of China under grant BK20130808 and by China Postdoctoral Science Foundation under grant 2015M571751.
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Sun, W., Cai, X., Xia, C., Sulaman, M., Mısır, M., Fan, Z. (2017). Greedy Based Pareto Local Search for Bi-objective Robust Airport Gate Assignment Problem. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_56
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DOI: https://doi.org/10.1007/978-3-319-68759-9_56
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