A heuristic optimization approach for multi-vehicle and one-cargo green transportation scheduling in shipbuilding
A heuristic optimization approach for multi-vehicle and one-cargo green transportation scheduling in shipbuilding
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DOI：
10.1016/j.aei.2021.101306
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Zu-hua Jiang;Yini Chen;Xinyu Li;Baihe Li
中科院分区：
文献类型：
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作者：
Zu-hua Jiang;Yini Chen;Xinyu Li;Baihe Li
为积极响应绿色造船的号召，区块合作运输在降低造船厂碳排放方面受到了特别的关注，由此产生了多车一货的绿色运输调度问题。为了有效解决这一问题，提高运输效率，降低能源消耗，提出了一种将路径模型与同步约束相结合的双目标数学模型，以同时最小化非增值运输时间成本和二氧化碳排放总量。然后设计了一种基于Pareto的多目标禁忌搜索(MOTS)算法来求解该模型，在该算法中，通过局部改进来产生有前途的邻域个体。实验结果表明，该算法即使在大规模情况下也能有效地解决该问题，并优于经典的非支配排序遗传算法-II(NSGA-Ⅱ)。希望通过本文的工作，能够实现一种高效、低能耗的运营模式，为船厂的平板车运输调度经营者提供有益的启示。
To actively respond to the call for green shipbuilding, block cooperative transportation has been particularly concerned in reducing carbon emission in the shipyard, and hence a “multi-vehicle and one-cargo” (MVOC) green transportation scheduling problem emerges. Aiming to solve this problem effectively and improve transportation efficiency and reduce energy consumption, a bi-objective mathematical model combined routing model with synchronization constraints is proposed to simultaneously minimize non-value-added transportation time cost and total CO2emission. A Pareto-based multi-objective Tabu Search (MOTS) algorithm is then designed to solve the model, in which local improvements are developed to generate promising neighboring individuals. Experimental results show that the proposed MOTS algorithm can effectively solve the problem even on a large scale and outperform the classic algorithm of nondominated sorting genetic algorithm-II (NSGA-Ⅱ). It is hoped that this work enables an operation mode with high efficiency and low energy consumption and provides useful insights for flatcar transportation scheduling operators in the shipyard.