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Online retail orders, especially online supermarket orders, have been highlighted to have several distinguished features from traditional online retailers. These include a huge amount of daily orders and orders containing multiple items. Tens of thousands of Stock Keeping Units (SKUs) sold by online retailers have to be stored at multiple storage zones due to the limit capacity of one area, and ordered items should to be picked with a parallel picking strategy. What is the most efficient and accurate method of picking, sorting and packaging the ordered items from SKUs for online orders? This paper focuses on scheduling the three processes of order picking problems in a warehouse for an online supermarket. Referring to the principle of the mixed-model assembly line, it presents a new optimization method of group order picking. With an objective of minimizing the picking and packaging time, this paper studies order batching and order sequencing. In order batching, considering the workload balance, it builds a mathematical optimization model and applies a bi-objective genetic algorithm to solve it. Then an order batching sequencing model is built, and a solving algorithm based on Pseudo-Boolean Optimization is developed. Case study and sensitivity analyses are conducted to verify the effectiveness of the method.

Mixed Model Assembly Line Scheduling Approach to Order Picking Problem in Online Supermarkets