Cross-Docking: Current Research Versus Industry Practice and Industry 4.0 Adoption

In a cross docking system products are received at the terminal, occasionally gathered with other products going to the same destination, then shipped at the earliest opportunity, without going into long-term storage. Applying cross docking in the distribution network can reduce transportation cost, the inventory holding cost, cycle time and increase customer satisfaction. In order to obtain such benefits, the company should be able to operate the cross docking effectively and efficiently. For this purpose several scheduling and door assignment procedures have been introduced in recent years, which aim at solving the so called truck scheduling and dock assignment problem. This research deals with both dock assignment and truck scheduling problems. The integration of both problems is discussed in two scenarios: one considers a static dock assignment and truck scheduling, and the other one addresses a dynamic dock assignment and truck scheduling. Mathematical formulations for both problems are first presented as 0-1 integer programming models. Since both dock assignment and truck scheduling problems are NP-hard, the integration of both problems is more difficult to solve. Thus we propose different heuristic algorithms to solve the integrated problem. For the static dock assignment and truck scheduling three different reduced variable neighborhood search (RVNS) algorithm frameworks are proposed, while for the dynamic dock assignment and truck scheduling a RVNS algorithm with three different types of solution initialization is proposed. The experimental results show that the RVNS algorithms for the static dock assignment and truck scheduling are capable of finding good solutions in a much shorter computation time when it is compared with Gurobi optimizer solutions. For the RVNS for the dynamic dock assignment and truck scheduling it was observed that the algorithm converges to the same quality of solution no matter the type of initial solution generated for small instances, however for large instances a better quality of solution is obtained when the algorithm is seeded by a construction heuristic.

The Physical Activity Guidelines for Americans (Guidelines) advises older adults to be as active as possible. Yet, despite the well documented benefits of physical activity just 12.8% of those ages 65 and older meet the Guidelines. To address this, the U.S. Department of Health and Human Services (HHS) developed a Midcourse Report focused on effective strategies to improve older adult physical activity behaviors. The first step in this process was a systematic literature review. A literature review team was contracted to examine the evidence on key settings and effective behavioral intervention strategies, as well as effective policy, systems, and environmental (PSE) approaches, to improve physical activity among older adults. The PSE search employed an equity-centered framework adapted to researching PSE approaches for improving physical activity outcomes in older adults. Sixteen thousand eight hundred and eighty-three titles and abstracts were screened, and 734 full articles were reviewed for inclusion. Of those, 64 original research articles were included for the final review to answer two questions, one (plus 5 sub-questions) focused on Settings/Strategies literature (45 studies) and one (plus 2 sub-questions) focused on PSE literature (19 studies). The literature review process identified key settings and evidence-based strategies to support older adults in becoming more physically active, and provides a foundation for the Physical Activity Guidelines for Americans Midcourse Report: Implementation Strategies for Older Adults. More research is needed to address how factors related to equity and psychosocial constructs influence physical activity behaviors among older adults.

Truck scheduling at zero-inventory cross docking terminals

A Genetic Algorithm Approach for Multi Objective Cross Dock Scheduling in Supply Chains

Cross dock scheduling: Classification, literature review and research agenda

At cross docking terminals, shipments from inbound trucks are unloaded, sorted and moved to dispatch points where they are directly loaded onto outbound trucks for an immediate delivery elsewhere in the distribution system. This warehouse management concept aims at realizing economies in transportation cost by consolidating divergent shipments to full truckloads without requiring excessive inventory at the cross dock. The efficient operation of such a system requires an appropriate coordination of inbound and outbound trucks, e.g. by computerized scheduling procedures.This work introduces a base model for scheduling trucks at cross docking terminals, which relies on a set of simplifying assumptions in order to derive fundamental insights into the underlying problem’s structure, i.e. its complexity, and to develop a building block solution procedure, which might be employed to solve more complex real-world truck scheduling problems.

PurposeThe purpose of this study is truck scheduling and assignment of trucks to the doors simultaneously since these issues were considered mainly separately in the previous research. Also, the door service time and its impact on truck scheduling were not taken into account, so this research endeavors to cover this gap.Design/methodology/approachIn this research, a novel model has been presented for simultaneous truck scheduling and assignment problem with time window constraints for the arrival and departure of trucks, mixed service mode dock doors and truck queuing. To resolve the developed model, two meta-heuristic algorithms, namely, genetic and imperialist competitive algorithms, are presented.FindingsThe computational results indicate that the proposed framework leads to increased total costs, although it has a more accurate planning; moreover, these indicate that the proposed algorithms have different performances based on the criteria considered for the comparison.Research limitations/implicationsThere are some limitations in this research, which can be considered by other researchers to expand the current study, among them the specifications of uncertainty about arrival times of inbound and outbound trucks, number of merchandises which has been loaded on inbound trucks are the main factors. If so, by considering this situation, a realistic scheme about planning of cross docking system would be acquired. Moreover, the capacity of temporary storage has been considered unlimited, so relaxing this limitation can prepare a real and suitable situation for further study. Examining the capacity in the front of each type of doors of cross-dock and executive servers are the other aspects, which could be expanded in the future.Originality/valueIn this study, a mathematical programing model proposed for truck scheduling to minimize total costs including holding, truck tardiness and waiting time for queue of trucks caused by the interference of each carrier’s movement. At the operational levels, this research considered a multi-door cross-docking problem with mixed service mode dock doors and time window constraints for arrival and departure time of trucks. Moreover, M/G/C queue system was developed for truck arrival and servicing of carriers to trucks.

In cross-docking operations, planners need to coordinate the inbound, docking and outbound logistics operations to ensure a smooth flow of goods across the supply chain. The operation management of cross docking is a crucial task with high complexity for the logistics systems. This paper attempts to address the Vehicle Routing Problems (VRPs) of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of time and capacity constraints. Due to high complexity of model, a variant of Particle Swarm Optimization (PSO) with a Self-Learning approach is tailored to solve the VRP. Two test problems are generated and results are obtained.

Cross docking is a logistic management strategy for reducing storage level, inventory management cost, and customer delivery lead-time. In the large distribution network in which products are transferred through one or more cross docks, the decision of whether to synchronise inbound and outbound trucks for the whole network is crucial since it significantly impacts on supply chain performance. This study presents the novel mathematical model of truck scheduling problem in the cross docking network consisting of several cross docks and addresses the importance of synchronising the truck schedule in a network as a whole. The model is formulated as mixed integer programming (MIP) with an objective to find the optimal truck schedule and product transshipment to minimise the makespan. The results demonstrate that simultaneously optimising truck schedule throughout the network improves the solution quality compared to those obtained from models, that optimise the truck schedule in each cross dock sequentially.

Cross docking is a logistic management strategy for reducing storage level, inventory management cost, and customer delivery lead-time. In the large distribution network in which products are transferred through one or more cross docks, the decision of whether to synchronise inbound and outbound trucks for the whole network is crucial since it significantly impacts on supply chain performance. This study presents the novel mathematical model of truck scheduling problem in the cross docking network consisting of several cross docks and addresses the importance of synchronising the truck schedule in a network as a whole. The model is formulated as mixed integer programming (MIP) with an objective to find the optimal truck schedule and product transshipment to minimise the makespan. The results demonstrate that simultaneously optimising truck schedule throughout the network improves the solution quality compared to those obtained from models, that optimise the truck schedule in each cross dock sequentially.

In this paper, we present the research issues, the representation models, as well as the methodological and algorithmic approaches that have been proposed for cross-docking systems in the literature. More specifically, we have conducted a systematic literature review so as to analyze the contribution of Artificial Intelligence (AI), and more generally AI-based techniques, to cross-docking systems. One immediate interest of this work is that it allows us to identify some new potential uses of AI-based techniques for solving cross-docking problems. To conduct our analysis, we have extended the standard approach of systematic literature review called SALSA; e-SALSA is the novel derived and enhanced approach we propose in our study. It consists of seven steps for carrying out a systematic literature review based on a meta-analysis of the available data on the subject of AI-based techniques applied to the domain of cross docking (AI4 CD: Artificial Intelligence for Cross-Docking). In the light of the results of our review and analysis, several new scientific issues are identified on AI4 CD, giving us the opportunity of suggesting some new directions of research.

Optimizing truck sequencing and truck dock assignment in a cross docking system

Cross docking warehousing has become an important logistics strategy in the supply chain management for many distributions industries. The idea of cross docking is to eliminate the storage and order picking function in the warehouse while still allowing it to serve its receiving and shipping function. In the current literature, many cross docking models have been proposed and these models vary based on the cross docking setting and problem tackled. This study presents the gap analysis on the existing cross docking models pertaining to the operational scenarios and features addressed in solving short-term cross docking problem. On top of that, the main concept of cross docking and planning decision level are discussed. The contributions of this study are to identify the gap of knowledge of the cross docking short-term planning and point out future research direction in cross docking.

Today’s customer-driven economic environment shifts from “supply chain” to “demand chain”. To successfully compete, companies are faced with the challenge of minimizing inventory and reducing warehouse costs whenever possible while keeping retailers’ shelves full. Cross Docking is a strategy whose ability to reduce lead times, control inventory carrying costs and deliver just-in-time service with a minimal investment makes it an attractive option for many companies responding to economic pressures. In a cross docking strategy, warehouses and distribution centers serve as transfer points for inventory, but no inventory is held at these transfer points. One of the keys to keeping a Cross Docking facility synchronized is to design a system and schedule that are obtainable, taking into account travel times and expectations for how much freight will actually fit on a truck; therefore, Vehicle Routing should be considered. This research examines a Vehicle Routing Problem with Cross Docking (VRPCD) considering cross docking operational characteristics such as assignment restrictions, deadlines and door environment. A sequential operational VRPCD model is presented. Its objective is to determine the best route that minimizes operational costs of shipment, and the minimum operational cost within the cross docking facility. A two-phase algorithm using Variable Neighborhood Search (VNS) has been proposed. The experimental results show that the VNS algorithm is capable of finding good solutions in a much shorter computation time when compared with Gurobi Optimizer solutions. Good quality of solutions is obtained in all the experiments developed.

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.