A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility

Nowadays, supply chain stakeholders have to successfully address a large number of challenges in order to ensure an efficient distribution of products within supply chains. These challenges include uncertainties in the supply chain operations, strict delivery deadlines, perishability of the transported products, partnership termination, and others. Cross-docking facilities have been widely used to tackle some of these challenges and improve effectiveness of the product distribution. This study aims to enhance scheduling of the inbound and outbound trucks at a cross-docking facility. The truck scheduling problem is formulated as a mixed integer linear programming model, minimizing the total truck service cost. A novel Delayed Start Parallel Evolutionary Algorithm is proposed to solve the problem. The algorithm executes separate Evolutionary Algorithms on its islands in a sequential manner and exchanges the promising solutions among the active islands based on an adaptive migration criterion. The computational experiments showcase superiority of the proposed algorithm in terms of the key algorithmic performance indicators against the other five metaheuristic algorithms, which have been commonly used in the cross-docking literature. Furthermore, this study demonstrates how the developed algorithm can be effectively used for the analysis of important managerial insights from the transportation perspective.