计及漂移瓶颈的时变物料配送路径优化

针对不确定作业车间环境下物料配送路径优化问题，采用能反映制造单元相对生产负荷及其变化趋势的瓶颈指数和瓶颈漂移指数表征实时变化的制造单元物料配送优先级，对路径选择过程中违反此优先级的行为设置惩罚成本，提出以最小化包括车辆运输成本和违反优先级的惩罚成本在内的总配送成本为优化目标，建立了时变的物料配送路径优化模型。在此基础上，为保证运输车辆所载物料全额配送，避免非必要负载以及由此造成的非必要配送子路径，对配送路径优化模型进行改进，允许运输车辆非满载和物料拆分配送，以提高物料配送效率降低配送成本；并结合模型特点将贪婪策略融入遗传算法对优化模型求解。最后，通过某作业车间内物料配送实例验证了所提出的计及漂移瓶颈的改进时变物料配送路径优化方法在不确定作业环境中具有有效性和实用性。

Time-varying Material Distribution Routing Optimization Considering Shifting Bottleneck

Aiming at the material distribution routing optimization problem under uncertain job shop environment, the bottleneck index and bottleneck shifting index which represent the relative production load of each manufacturing unit and its the change trend are adopted to denote the time-varying distribution priority of the manufacturing unit. Then the time-varying material distribution routing optimization models are established and its objective is minimizing the distribution cost including vehicle transportation cost and punishment cost due to the violation of distribution priority. Based on that, suppose the condition that the vehicle haven’t to be loaded fully and material can be distributed to one manufacturing more times in order to guarantee that the material in the vehicle is distributed totally before the vehicle comes back to the distribution center and to avoid unnecessary distribution sub-route, then propose the modified time-varying material distribution routing optimization model to enhance the distribution productivity and decrease the distribution cost further. The greedy-based genetic algorithm is presented to solve the proposed optimization model. An example of some job shop material distribution routing optimization is given to prove the validation and practicability of the proposed method under uncertainty.