LAUSR

Supply chains have become more time-sensitive in recent years. Delays in supply chain operations may cause significant negative externalities, including lost sales and customers. In order to facilitate the product distribution process within supply chains, reduce the associated delays, and improve sustainability of the supply chain operations, many distribution companies started implementing the cross-docking technique. One of the challenging problems in management of the cross-docking facilities is efficient scheduling of the arriving trucks. This study proposes a novel Diploid Evolutionary Algorithm for the truck scheduling problem at a cross-docking facility, which—unlike the Evolutionary Algorithms presented in the cross-docking literature to date—stores the genetic information from the parent chromosomes after performing a crossover operation. The objective of the formulated mathematical model is to minimize the total truck service cost. The conducted numerical experiments demonstrate that the optimality gap of the developed algorithm does not exceed 0.18% over the considered small size problem instances. The analysis of the realistic size problem instances indicates that deployment of the developed solution algorithm reduces the total truck handling time, the total truck waiting time, and the total truck delayed departure time on average by 6.14%, 32.61%, and 34.01%, respectively, as compared to a typical Evolutionary Algorithm. Furthermore, application of the diploidy concept decreases the total truck service cost by 18.17%.