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We herein present a tabu search algorithm with variable cluster grouping (TSVCG for short) to deal with Multidepot vehicle routing problem (MDVRP for short). In TSVCG, we firstly adopt variable cluster grouping to convert a complicated MDVRP to typical single depot vehicle routing problem (SDVRP for short). And then we apply a tabu search algorithm to solve each SDVRP. In the grouping process, we discuss how to find a scale factor and minimum geometric semicircle correction factor to improve the customer points' grouping, and thus get the different groups for further problemsolving. The experimental results shown that the proposed variable cluster grouping can reduce grouping blindness and improve the efficiency of grouping and viability of group results. The results also shown that the proposed TSVCG performed well compared with the previous work with the geometric grouping.

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Considering the actual situation of China's automobile industry, this paper pioneers the discussion of the multi-factory milk run pickup problem with uncertain demand and frequency (MFMRPP-UDF). Considering the balance between inventory cost and distribution cost, a mixed-integer programming model was built for the problem, and converted into a robust optimization model by the Chernoff-Hoeffding theorem; then, the adaptive genetic algorithm (AGA) and local search (LS) were combined into a general hybrid heuristic algorithm (AGA-LS) to solve the problem. Then, the proposed algorithm was run 10 times and contrasted with the standard GA. The results show that the AGA-LS outperformed the standard GA in the reduction of the overall cost. This research provides important insights into the cost efficiency of inventory and delivery in the automobile parts industry.