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Fuzzy Optimal Allocation Model for Task–Resource Assignment Problem in a Collaborative Logistics Network

机译:协同物流网络中任务-资源分配问题的模糊最优分配模型

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摘要

Obtaining a multiresource allocation scheme for multitask influenced by uncertain factors is a critical problem in a collaborative logistics network. This paper presents an optimal allocation model of fuzzy resources for multistage random logistics tasks based on the six-point trapezoidal fuzzy number and the membership function. Besides, considering task demands and resource constraints, a new cost-time-quality multiobjective programming of N-N task-resource assignment is introduced, which can be divided into minimize total logistics cost and execution time, maximize total service quality. Furthermore, by setting the different simulation scenarios, the results show that if the decision maker has a higher risk preference and pursues the optimization of single or multiobjective, the higher degree membership and satisfaction function values can be obtained with a larger compensation coefficient. The allocation scheme of task-resource assignment generated by proposed model has a high global level of utilization efficiency, which can effectively utilize fuzzy resources in collaborative logistics network, and avoid resource shortage caused by the excessive occupation of local resources.
机译:对于不确定因素影响的多任务,获得多资源分配方案是协同物流网络中的关键问题。提出了基于六点梯形模糊数和隶属度函数的多阶段随机物流任务模糊资源最优分配模型。此外,考虑到任务需求和资源约束,引入了一种新的N-N任务-资源分配的成本—时间—质量多目标规划方法,可分为最小化总物流成本和执行时间,最大化总服务质量。此外,通过设置不同的模拟方案,结果表明,如果决策者具有较高的风险偏好并追求单目标或多目标的优化,则可以使用较大的补偿系数获得较高的隶属度和满意度函数值。该模型生成的任务资源分配分配方案具有较高的全局利用率,可以有效利用协同物流网络中的模糊资源,避免因过度占用本地资源而导致资源短缺。

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