Integrated optimization on both vehicle filling and routing for van truck transportation
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摘要: 通过引入剩余空间及其相关作业, 建立了基于剩余空间的厢式货车配载与配送联合优化的混合整数规划模型, 提出了一种由C-W节约算法与基于剩余空间的装箱算法有机结合的交互式算法, 并分别与配载优先及配送优先的单独优化进行了对照仿真试验。试验结果表明: 在总目标函数方面, 联合优化比配载优先与配送优先的单独优化分别提高了40.8%与18.2%, 所提出的模型及算法有效。Abstract: By means of introducing the residual space and its correlative operations, a mixed integer programming model was proposed based on the integrated optimization on both vehicle filling problem (VFP) and vehicle routing problem (VRP) for van truck.A new interactive algorithm was designed which combined C-W saving algorithm with packing algorithm based on residual space, and simulation tests were compared.Test result shows that the total objective function of the integrated optimization increases by 40.8% and 18.2% compared with two kinds of separate optimization instances, which preferentially consider VFP and VRP respectively, so the integrated optimized model and its algonithm are valid.
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Key words:
- logistics engineering /
- residual space /
- VFP /
- VRP /
- integrated optimization /
- interactive algorithm
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表 1 联合优化与配载优先的单独优化结果比较
Table 1. Comparison of integrated optimization and filling first tactic
组号 货物数量 算法 运输距离节省比率 空间利用率 总目标函数 1 380 联合优化 0.573 9 0.739 1 0.606 9 随机配送顺序下的装箱优化 0.332 4 0.733 9 0.410 7 2 239 联合优化 0.573 4 0.782 2 0.615 2 随机配送顺序下的装箱优化 0.377 2 0.743 6 0.450 4 3 320 联合优化 0.583 4 0.727 4 0.612 2 随机配送顺序下的装箱优化 0.271 0 0.717 0 0.360 2 4 235 联合优化 0.595 9 0.813 9 0.638 8 随机配送顺序下的装箱优化 0.321 7 0.803 4 0.418 0 5 345 联合优化 0.377 2 0.615 5 0.363 3 随机配送顺序下的装箱优化 0.214 9 0.510 0 0.273 9 6 296 联合优化 0.621 2 0.804 3 0.657 8 随机配送顺序下的装箱优化 0.363 5 0.733 6 0.437 6 7 253 联合优化 0.566 4 0.803 4 0.613 8 随机配送顺序下的装箱优化 0.267 2 0.797 8 0.373 3 8 230 联合优化 0.603 8 0.798 6 0.642 8 随机配送顺序下的装箱优化 0.450 8 0.791 4 0.518 9 9 263 联合优化 0.574 2 0.802 3 0.539 6 随机配送顺序下的装箱优化 0.370 2 0.792 7 0.454 7 10 244 联合优化 0.565 4 0.809 8 0.533 2 随机配送顺序下的装箱优化 0.351 5 0.788 1 0.438 8 均值 280 联合优化 0.563 5 0.769 7 0.582 4 随机配送顺序下的装箱优化 0.403 0 0.741 2 0.413 7 
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表 2 联合优化与配送优先的单独优化结果比较
Table 2. Comparison of integrated optimization and routing first tactic
组号 货物数量 算法 运输距离节省比率 空间利用率 总目标函数 1 380 联合优化 0.573 9 0.739 1 0.606 9 装箱优先规则下的线路优化 0.384 6 0.583 9 0.424 4 2 239 联合优化 0.573 4 0.782 2 0.615 2 装箱优先规则下的线路优化 0.426 1 0.558 6 0.452 6 3 320 联合优化 0.583 4 0.727 4 0.612 2 装箱优先规则下的线路优化 0.528 4 0.545 9 0.531 9 4 235 联合优化 0.595 9 0.813 9 0.638 8 装箱优先规则下的线路优化 0.496 8 0.611 6 0.519 7 5 345 联合优化 0.377 2 0.615 5 0.363 3 装箱优先规则下的线路优化 0.328 2 0.534 5 0.262 5 6 296 联合优化 0.621 2 0.804 3 0.657 8 装箱优先规则下的线路优化 0.509 0 0.722 9 0.551 8 7 253 联合优化 0.566 4 0.803 4 0.613 8 装箱优先规则下的线路优化 0.561 5 0.714 3 0.592 1 8 230 联合优化 0.603 8 0.798 6 0.642 8 装箱优先规则下的线路优化 0.524 4 0.669 7 0.553 5 9 263 联合优化 0.574 2 0.802 3 0.539 6 装箱优先规则下的线路优化 0.494 4 0.698 3 0.535 2 10 244 联合优化 0.565 4 0.809 8 0.533 2 装箱优先规则下的线路优化 0.540 3 0.705 6 0.502 8 均值 280 联合优化 0.563 5 0.769 7 0.582 4 装箱优先规则下的线路优化 0.479 4 0.634 5 0.492 7
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