A Hybrid Genetic Algorithm for Integrated Truck Scheduling and Product Routing on the Cross-Docking System with Multiple Receiving and Shipping Docks

In this research, a truck scheduling problem for a cross-docking system with multiple receiving and shipping docks is studied. Until recently, single-dock cross-docking problems are studied mostly. This research is focused on the multiple-dock problems. The objective of the problem is to determine the best docking sequences of inbound and outbound trucks to the receiving and shipping docks, respectively, which minimize the maximal completion time. We propose a new hybrid genetic algorithm to solve this problem. This genetic algorithm improves the solution quality through the population scheme of the nested structure and the new product routing heuristic. To avoid unnecessary infeasible solutions, a linked-chromosome representation is used to link the inbound and outbound truck sequences, and locus-pairing crossovers and mutations for this representation are proposed. As a result of the evaluation of the benchmark problems, it shows that the proposed hybrid GA provides a superior solution compared to the existing heuristics.

Adaptive memory red deer algorithm for cross-dock truck scheduling with products time window

Purpose The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks. Design/methodology/approach This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time. Findings Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window. Research limitations/implications The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies. Originality/value For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

Models and Solution Algorithms for Efficient Truck Scheduling at Cross-docking Terminals with Time-sensitive Products

This dissertation focuses on the scheduling of trucks (both in- and outbound trucks) at a CDT, where some of the delivered products are perishable in nature. The short lifespan of perishable products (i.e., foods and drugs) poses critical challenges to the CDT operations management. Perishable goods are time-sensitive products that require minimal handling time to preserve their quality and profitability. Cross-docking is expected to facilitate the distribution of perishable products within supply chains. There are many challenges involved in the management of the cross-docking terminals with perishable products, including determination of the service order of the trucks (inbound and outbound) carrying perishable products, selection of preemption strategies for certain trucks (i.e., a given truck can leave the door, so another truck can be docked for service), allocation of suitable temporary storage space for products, quality loss due to late delivery or errors in temperature control.This dissertation aims to develop a mathematical model for scheduling the arriving trucks at a cross-dock terminal, taking product decay into consideration throughout the handling process. The objective of the mathematical model minimizes the total truck service cost, which includes (1) waiting cost; (2) service cost; (3) cost of product storage; (4) cost of delay in truck departure; and (5) the cost associated with the decay of products that are perishable in nature. A number of linearization techniques are discussed in order to linearize the original nonlinear mathematical model (where the nonlinearity is caused by the adopted product decay function). The complexity of the linearized model is evaluated in this dissertation. Moreover, the candidate solution approaches for the proposed mathematical model are described.The developed model was solved using the exact optimization technique. In particular, the model was solved to optimality using CPLEX. However, it was observed that the computational time increased as the problem size increased due to the model complexity. Four alternative solution approaches namely: (1) Evolutionary Algorithm (EA); (2) Variable Neighborhood Search (VNS); (3) Tabu Search (TS); and (4) Simulated Annealing (SA), which are common metaheuristic algorithms, were developed and compared with CPLEX using small-size problem instances. These metaheuristics were able to achieve optimal solutions for the small-size problem instances and required relatively low computational times. The metaheuristic algorithms were further compared, and EA was found to outperform the others (VNS, TS, and SA) based on the balance between the objective function and computational time values. A set of analyses were carried out using EA, and managerial insights that could be of interest to supply chain stakeholders were drawn. The proposed mathematical model, the developed EA, and the managerial insights could assist the CDT manager in making efficient and timely truck scheduling decisions in any planning horizon.