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This study addresses a multi-commodity many-to-many vehicle routing problem with simultaneous pickup and delivery (M-M-VRPSPD) for a fast fashion retailer in Singapore. Different from other widely studied pickup and delivery problems, the unique characteristics are: (1) collected products from customers are encouraged to be reallocated to fulfill demands of other customers; (2) it is multi-commodity and the number of involved commodities can be over 10,000. To solve this problem, we provide a nonvehicle-index arc-flow formulation and some strengthening strategies. Moreover, for large-scale instances, an adaptive memory programming based algorithm combined with techniques such as the regret insertion method for initializing the solution pool, the segment-based evaluation scheme, and advanced pool management method, is proposed. We test our algorithm on 66 real-world and 96 newly generated …