https://scholars.lib.ntu.edu.tw/handle/123456789/612237
標題: | A novel hybrid ant colony optimization algorithm for emergency transportation problems during post-disaster scenarios | 作者: | Wang X. TSAN MING CHOI Liu H. Yue X. |
關鍵字: | Ant colony optimization (ACO); cumulative multidepot vehicle routing problem (cum-MDVRP); emergency transportation; fairness and efficiency; integer linear programming | 公開日期: | 2018 | 出版社: | Institute of Electrical and Electronics Engineers Inc. | 卷: | 48 | 期: | 4 | 起(迄)頁: | 545-556 | 來源出版物: | IEEE Transactions on Systems, Man, and Cybernetics: Systems | 摘要: | The increasing impacts of natural disasters have led to concerns regarding predisaster plans and post-disaster responses. During post-disaster responses, emergency transportation is the most important part of disaster relief supply chain operations, and its optimal planning differs from traditional transportation problems in the objective function and complex constraints. In disaster scenarios, fairness and effectiveness are two important aspects. This paper investigates emergency transportation in real-life disasters scenarios and formulates the problem as an integer linear programming model (called cum-MDVRP), which combines cumulative vehicle routing problem and multidepot vehicle routing problem. The cum-MDVRP is NP-hard. To solve it, a novel hybrid ant colony optimization-based algorithm is proposed by combining both saving algorithms and a simple two-step 2-opt algorithm. The proposed algorithm allows ants to go in and out the depots for multiple rounds, so we abbreviate it as ACOMR. Moreover, we present a smart design of the ants' tabus, which helps to simplify the solution constructing process. The ACOMR could yield good solutions quickly, then the decision makers for emergency responses could do expert planning at the earliest time. Computational results on standard benchmarking data sets show that the proposed cum-MDVRP model performs well, and the ACOMR algorithm is more effective and stable than the existing algorithms. ? 2013 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038017926&doi=10.1109%2fTSMC.2016.2606440&partnerID=40&md5=ae352377d3d1d7989074efba6bbfdbd6 https://scholars.lib.ntu.edu.tw/handle/123456789/612237 |
DOI: | 10.1109/TSMC.2016.2606440 | SDG/關鍵字: | Ant colony optimization; Artificial intelligence; Civil defense; Decision making; Disaster prevention; Disasters; Integer programming; Supply chains; Vehicle routing; Vehicles; Ant Colony Optimization (ACO); Hybrid ant colony optimization; Integer Linear Programming; Integer linear programming models; Multi-depot vehicle routing problems; Supply chain operation; Transportation problem; Vehicle Routing Problems; Optimization |
顯示於: | 工商管理學系 |
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