基于公路-铁路联运的多目标优化短驳运输调度策略

Sustainability Pub Date : 2024-07-24 DOI:10.3390/su16156310
Dudu Guo, Yinuo Su, Xiaojiang Zhang, Zhen Yang, Pengbin Duan
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引用次数: 0

摘要

本研究侧重于多式联运中的 "短驳运输 "场景。针对调度策略的复杂性和不确定性导致的运输效率低下问题,提出了一种车辆卸载预约机制,以优化需求点调度系统。本文建立了一个调度策略优化模型,以最小化短途回程成本并获得最短交货时间窗口,并设计了一种适合多目标优化的混合 NSGWO 算法来解决该问题。该算法在灰狼优化算法(GWO)的基础上融入了非支配排序遗传算法 II(NSGA-II)算法,弥补了单一算法收敛过早的缺陷。实验选取了某物流公司的实际公路-铁路多式联运短驳数据,并对上述数据进行了对比和验证。结果表明,该算法得到的调度方案与原方案相比,可节约运输成本 41.01%,总交货时间缩短 46.94%,在实现及时交货的同时,有效保障了企业的经济效益。同时,优化后的调度方案减少了运输车辆的数量,对绿色物流的可持续发展产生了积极影响。
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Multi-Objective Optimization of Short-Inverted Transport Scheduling Strategy Based on Road–Railway Intermodal Transport
This study focuses on the ‘short-inverted transportation’ scenario of intermodal transport. It proposes a vehicle unloading reservation mechanism to optimize the point-of-demand scheduling system for the inefficiency of transport due to the complexity and uncertainty of the scheduling strategy. This paper establishes a scheduling strategy optimization model to minimize the cost of short backhaul and obtain the shortest delivery time window and designs a hybrid NSGWO algorithm suitable for multi-objective optimization to solve the problem. The algorithm incorporates the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm based on the Grey Wolf Optimizer (GWO) algorithm, compensating for a single algorithm’s premature convergence. The experiment selects a logistics carrier’s actual road–rail intermodal short-inverted data and compares and verifies the above data. The results show that the scheduling scheme obtained by this algorithm can save 41.01% of the transport cost and shorten the total delivery time by 46.94% compared with the original scheme, which can effectively protect the enterprise’s economic benefits while achieving timely delivery. At the same time, the optimized scheduling plan resulted in a lower number of transport vehicles, which positively impacted the sustainability of green logistics.
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