Integrated order batching and vehicle routing operations in grocery retail - A General Adaptive Large Neighborhood Search algorithm

Abstract In recent years, established and well-known grocery retailers have increasingly been investing in the business of micro stores and petrol station shops. Supplying these stores with perishable and durable goods leads to noticeable logistics challenges for the retailers. Since the total sales volumes of these shops are typically low and the respective sales areas are very limited, highly frequent deliveries of small sizes are required. These noticeably affect a number of operational planning problems. In the warehouse, the items requested have to be collected in small order sizes. In order to achieve efficient picking operations, orders are therefore combined into larger picking orders, i.e., batches. Afterwards the orders have to be delivered to the stores at high frequency. In practice, all the planning problems mentioned are heavily interconnected due to the short planning horizon. Despite the practical relevance, order batching, order picking and delivery operations have not so far been investigated as an integrative planning problem. This paper therefore presents a novel modeling and solution approach to solve practically relevant problem sizes. The combinatorial complexity of the problem requires a heuristic solution approach. We propose an extension of the well-known Adaptive Large Neighborhood Search (ALNS) metaheuristic that we call General ALNS (GALNS), and show that a GALNS approach outperforms a similar ALNS algorithm in 96.35% of the problem instances generated. Managerial insights from general problem data and a case study with a large German grocery retailer support the applicability of the modeling and solution approach suggested in retail practice.