An Adaptive Polyploid Memetic Algorithm for scheduling trucks at a cross-docking terminal

Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem

Recent trends in the management of supply chains have witnessed an increasing implementation of the cross-docking strategy. The cross-docking strategy, being the one that can potentially improve supply chain operations, has received a lot of attention from researchers in recent years, especially over the last decade. Cross-docking involves the reception of inbound products, deconsolidation, sorting, consolidation, and shipping of the consolidated products to the end customers. The number of research efforts, aiming to study and improve the cross-docking operations, increases every year. While some studies discuss cross-docking as an integral part of a supply chain, other studies focus on the ways of making cross-docking terminals more efficient and propose different operations research techniques for various decision problems at cross-docking terminals. In order to identify the recent cross-docking trends, this study performs a state-of-the-art review with a particular focus on the truck scheduling problem at cross-docking terminals. A comprehensive evaluation of the reviewed studies is conducted, focusing on the major attributes of the cross-docking operations. These attributes include terminal shape considered, doors considered, door service mode considered, preemption, internal transportation mode used, temporary storage capacity, resource capacity, objectives considered, and solution methods adopted. Based on findings from the review of studies, some common issues are outlined and future research directions are proposed.

Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

Probabilistic Tabu Search for the Cross-Docking Assignment Problem

A cross docking facility is a type of warehouse in supply chain management that allows orders to be prepared with or without going through the phase of storing products in the warehouse and subsequently selecting them for delivery. The goods are unloaded from incoming trucks called origins on inbound doors of a cross-docking facility platform and, using a handling device inside the platform such as a forklift, immediately transferred to outbound doors to be loaded into outgoing trucks named destinations or delivery trucks for distribution to customers. Contrary to a traditional warehouse, goods are unloaded and loaded without placing them in temporary storage inside the cross-docking facility. The goal of the cross-docking assignment problem (CDAP) is to assign origins to inbound doors and destinations to outbound doors so that the total cost inside the cross-dock platform is minimized. To the best of our knowledge, there are only three mixed integer programming (MIP) formulations of the CDAP in the literature. We propose eight new MIP models and demonstrate the mathematical equivalence of all 11 models, together with rigorously proving some of their properties. In order to detect which of these 11 models is best, we conduct an extensive comparative analysis on benchmark instances from the literature, which discloses that the best model is one proposed in this paper for the first time.

Placide Nduwayo

Papers

A comparative study of formulations for a cross-dock door assignment problem

Probabilistic Tabu Search for the Cross-Docking Assignment Problem