Truck scheduling with fixed outbound departures in a closed-loop conveyor system with shortcuts

With the global trend of e-commerce, companies pursue a higher quality of parcel delivery service since customers expect faster transportation in this fast-paced society. Several of them are committed to improving the efficiency of logistics and reducing operating costs and increasing their market competitiveness in the industry. The parcel distribution center thus plays a critical role in parcel delivery industries to sort and consolidate parcel flows to full truckloads. The benefit of the strategy is significant such as reducing transfer time and related costs. On the other hand, an automated sorting system (ASS) is also highly used in many supply chains with impressive characteristics like fast operation speed, large capacity, high reliability. The goal of this research is to apply the scheduling method to reduce the sorting time in an automated sorting system to improve distribution efficiency. This study focuses on the truck scheduling problem with fixed outbound schedules in a closed-loop conveyor system with shortcuts. The objective is to minimize the costs of extra trucks used to deliver delayed parcels and holding cost of parcels at each shipping dock door. If a parcel fails to be loaded onto the pre-determined outbound trucks, an extra outbound truck will be used to deliver the parcel. The problem is modeled with a mixed integer nonlinear programming model. This problem is proven to be NP-hard in the strong sense. As a result, an adaptive genetic algorithm with local search (LSAGA) is developed to solve the problem under twelve scenarios and compared with other algorithms, and a full factorial design of experiment was conducted. The computational experiments show that four factors, layout, inbound truck, outbound truck, and algorithm are significant to the objective value, and the proposed algorithm can obtain high-quality solutions with more stability. A sensitivity analysis is also conducted and bring some managerial insights.