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A N–N optimization model for logistic resources allocation with multiple logistic tasks under demand uncertainty

机译:一种N-N优化模型,用于逻辑资源分配的需求不确定性

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摘要

It is challenging to make an optimal allocation scheme for multiple paralleling logistics tasks match suitable resources in collaborative logistics network (CLN) due to the uncertainty. In this paper, we propose a multi-objective optimization mathematical model to solve the N–N task-resource assignment (TRA) problem in CLN under demand uncertainty. Chance constraint in the model is used to indicate demand uncertainty, and resource leveling is considered in the execution of multiple paralleling logistics tasks scheduling. A hybrid heuristic algorithm based on genetic algorithm and tabu search is presented to solve the N–N TRA model. The experimental results demonstrate that the proposed model can promisingly describe the TRA problem, and the hybrid heuristic algorithm can achieve superior result about the size of uncertainty degree on allocation scheme.
机译:对多个并行物流任务进行最佳分配方案充满挑战,由于不确定性,在协作物流网络(CLN)中匹配合适的资源。 在本文中,我们提出了一种多目标优化数学模型,以解决需求不确定性的CLN中的N-N任务资源分配(TRA)问题。 模型中的机会约束用于表示需求不确定性,并且在执行多个并行物流任务调度时考虑资源调平。 提出了一种基于遗传算法和禁忌搜索的混合启发式算法来解决N-N Tra模型。 实验结果表明,所提出的模型可以承诺描述TRA问题,混合动力启发式算法可以实现卓越的结果关于分配方案的不确定性程度的大小。

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