The development of omni-channel retailing brings both opportunities and challenges to conventional supermarkets. Utilising local establishments to complete online customer orders is critical to providing timely and seamless omni-channel shopping experiences. We propose a bi-objective mixed-integer non-linear order batching and assignment optimisation model with the objectives of minimising person-hours, earliness and tardiness penalties, and workload imbalance. We apply a multi-objective genetic algorithm-based heuristic to obtain the Pareto frontier solutions in an omni-channel supermarket case, demonstrating the effectiveness of the proposed model and trade-offs among objectives. By conducting extensive numerical experiments, we discuss the influences of unique operational attributes in omni-channel supermarkets, namely workforce heterogeneity and store crowding, on the performance of in-store order fulfilment. Results suggest that the proposed model are relatively robust to workforce heterogeneity and coordinating online/offline channel traffic in peak hours is of importance. Overall, the research highlights the essence of bi-objective optimisation in order fulfilment and the importance of incorporating the operational attributes in omni-channel retail stores. We lastly provide managerial insights for in-store order fulfilment in omni-channel supermarkets.

全渠道零售的发展给传统超市带来机遇和挑战。利用本地机构完成在线客户订单对于提供及时和无缝的全渠道购物体验至关重要。我们提出了一个双目标混合整数非线性订单批处理和分配优化模型，其目标是最大限度地减少工时、提前和迟到处罚以及工作量不平衡。我们应用基于多目标遗传算法的启发式来获得全渠道超市案例中的帕累托前沿解，证明了所提出模型的有效性和目标之间的权衡。通过广泛的数值实验，我们讨论了全渠道超市独特运营属性的影响，即劳动力异质性和商店拥挤，对店内订单履行的表现。结果表明，所提出的模型对劳动力异质性相对稳健，并且在高峰时段协调在线/离线渠道流量非常重要。总体而言，该研究强调了订单履行中双目标优化的本质以及将运营属性纳入全渠道零售店的重要性。最后，我们为全渠道超市的店内订单履行提供管理见解。该研究强调了订单履行中双目标优化的本质以及将运营属性纳入全渠道零售店的重要性。最后，我们为全渠道超市的店内订单履行提供管理见解。该研究强调了订单履行中双目标优化的本质以及将运营属性纳入全渠道零售店的重要性。最后，我们为全渠道超市的店内订单履行提供管理见解。