Multi-tier shuttle warehousing systems are increasingly popular because of their high flexibility and robustness. These systems consist of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts undertakes inbound/outbound tasks instead of the stacker crane in conventional automated storage and retrieval systems. Scheduling different devices to reduce expected cycle time is an important concern. Thus, we propose a time sequence mathematical model of task operation on the basis of the movement characteristics of shuttles and lifts. The task scheduling problem between shuttles and lifts is converted into an assembly line parallel job problem by analysing the mathematical model, which generates the scheduling task queue model in the specified time window. In addition, a Pareto optimal based on an elitist non-dominated sorting genetic algorithm is adopted to solve the multi-objective optimisation function in the task scheduling problem. Finally, we illustrate the findings of the study through a practical example. Results show that the optimisation scheduling solution can reduce the total lift idle time and the total shuttle waiting time. Furthermore, it can improve warehousing efficiency and lower operation costs.

Utilizing local brick-and-mortar stores for same-day order fulfillment is becoming prominent in omni-channel retailing. Efficient in-store order picking is critical to providing timely value-added omni-channel delivery services. Despite numerous studies on order picking in traditional logistics warehouses and distribution centers, there is scant research focusing on in-store order fulfillment with the multi-skilled workforce in omni-channel retail stores. We studied the integrated Order Picking and Heterogeneous Picker Scheduling Problem (OPPSP-Het) in omni-channel retail stores. We characterized the OPPSP-Het in a mixed-integer linear optimization model with the objective of the minimization of total tardiness of all customer orders. A hybrid heuristic combining the genetic algorithm and variable neighborhood descent was designed to obtain effective solutions. Extensive experiments were conducted to validate the performance of the proposed approach relative to existing algorithms in recent literature. We further numerically showed the effects of order size and heterogeneous workforce on order fulfillment performance. We lastly emphasized the importance of workforce flexibility as a cost-effective approach to improving in-store order fulfillment performance.