Dynamic Order-based scheduling algorithms for automated retrieval system in Smart warehouses - Liu, Jialei (2022)
With the rapid development of logistics industry, Smart Warehouse, which aims toautomate the tasks of storage, picking, packaging, delivery, etc., has become a veryimportant part in the logistics system. To automate and speed up the itemretrieval process, a Smart Warehouse usually employs a management system, calledtheAutomated Retrieval System (ARS), to control and schedule the retrieval jobs. However, most of the existing ARS scheduling algorithms handle the retrieval jobs of items independently, but do not consider the integrality of orders. Thus, the overall delay of orders cannot be optimized. In this dissertation, we introduce the concept of Order Tag to the ARS scheduling algorithms. First, we verify whether the Order Tagstrategy can reduce the overall delay in the case of "Static Order Arrival". We propose two static algorithms, namely Static Order-Based Scheduling Algorithm – I (SOB-I) and Static Order-BasedScheduling Algorithm II (SOB-II). Simulation results demonstrate that these two strategies canreduce the total retrieval delay by approximately 30% compared to the existing algorithms, suchas Order-Based Random Out Algorithm (OBRO), Item-Based Shortest-Job-First Algorithm(IB- SJF). Next, we study the case of "Dynamic Order Arrival". Instead of assuming that all iii orders arrive to the system before processing, the algorithm considers orders arrivedynamically and it handles each new order once received. This makes the warehousemore flexible and efficient, but it also has higher requirements on the schedulingalgorithms. To minimize the average delay and ensuring the fairness, two algorithms are proposed. They are named as Dynamic Order-Based (DOB) and Dynamic Order- Based with Threshold (DOBT) Scheduling Algorithms, respectively. Compared withthe First-Come-First-Serve and other approaches, the simulation results showthat DOB and DOBT are able to reduce the average order retrieval delay by at least 30%, and generate less backlog pressure to the downstream operations.