Cross-docking Center Research Articles

Evolutionary algorithm framework for optimizing truck scheduling in multi-dock truck cross-docking centers

Evolutionary algorithm framework for optimizing truck scheduling in multi-dock truck cross-docking centers

A novel approach for multi-objective truck scheduling problems in a cross-docking center

A novel approach for multi-objective truck scheduling problems in a cross-docking center

Optimization of Logistic Solutions for Incoming and Outgoing Trucks System Using a Simulated Cross-Docking Centre Environment

Optimization of Logistic Solutions for Incoming and Outgoing Trucks System Using a Simulated Cross-Docking Centre Environment

A new gray optimization model for disposing or re-commercializing unsold goods in reverse logistics networks with a cross-docking center

PurposeThis paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.Design/methodology/approachA new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.FindingsAccording to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.Originality/valueAn integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

Designing a new multi-objective model for the vehicle routing scheduling at a cross-docking center in mitigating CO2 emissions at green supply chain under uncertainty

Designing a new multi-objective model for the vehicle routing scheduling at a cross-docking center in mitigating CO2 emissions at green supply chain under uncertainty

Pallet Distribution Affecting a Machine’s Utilization Level and Picking Time

Space and labor are the two internal resources within a warehouse or cross-dock center which seek attention. Meaningful efforts in optimizing these two resources can reduce the operational cost or time of the goods delivery. The timely allocation of resources to order picking not only reduces the makespan and operational time but can also evade delay. In decentralized settings, where all the information is not properly shared between the players of the supply chain, miscommunication results in delays in product delivery. In this study, efforts were made to determine the pallet quantity of different product types in an order quantify when there is a gap in information shared and, based on that, the allocation of material handling devices or pickers was conducted. Each handling device is bounded by a workload to eliminate the option of idle resources and ensure it is utilized properly. A mixed integer linear programming model was formulated for this study and was solved using Lingo. Numerical experiments were performed under varying resource numbers and pallet quantities to investigate the circumstances where the number of pallet types and allocation of machines have the highest benefit. The results confirm that a change in the pallet quantity of the products increases the total picking time. However, an increase in the number of handling devices minimizes the level of over-utilization of a particular machine.

Allocation of products to a heterogeneous fleet of trucks in a cross-docking center based on carbon emissions and costs in food and beverage industry: Novel uncertain solution approaches

The threat of climate change continues to grow, which calls for strategies to reduce emissions. Carbon emissions from transportation are among the highest in the world, so it is essential to improve its efficiency. Cross-docking is a smart way to improve the efficiency of transportation operations through the optimal use of truck capacity. This paper develops a novel bi-objective mixed integer linear programming (MILP) model to determine which products should be shipped together, select the most appropriate truck among the available ones, and schedule them. It reveals a new class of cross-dock truck scheduling problems, in which products are not interchangeable and are sent to different destinations. The first objective is to minimize overall system costs, while the second is to minimize total carbon emissions. To deal with uncertainties in factors, such as costs, time, and emission rate, these parameters are considered interval numbers. Furthermore, innovative uncertain approaches are introduced under interval uncertainty based on optimistic and pessimistic Pareto solutions for solving MILP problems via epsilon-constraint and weighting methods. The proposed model and solution procedures are used for planning an operational day at a regional distribution center (RDC) of a real food and beverage company, and results are compared. The results show that the proposed epsilon-constraint method outperforms the other implemented methods in terms of quantity and variety of optimistic and pessimistic Pareto solutions. Using the newly developed procedure, the amount of carbon produced by trucks could decrease by 18% under optimistic assumptions and 44% under pessimistic assumptions. As a result of the proposed solution approaches, managers can observe how their optimism level and the importance of objective functions influence their decisions.

Building A More Complex Simulation Model – Cross-Docking Center With Pallet and Box Handling

The paper focuses on the possibility of using simulations in supply chain management. Specifically, the simulation of logistics technology cross-docking, i.e. transhipment without warehousing, is investigated. The paper examines the suitability of the simulation software Simul8 (Professional version) for modeling a cross-docking center. After theoretical research on the topic, the specified problem and the simulation solution are presented.

Mathematical estimation for maximum flow of goods within a cross-dock to reduce inventory.

Supply chain management has recently renovated its strategy by implementing a cross-docking scheme. Cross-docking is a calculated logistics strategy where freight emptied from inbound vehicles is handled straightforwardly onto outbound vehicles, eliminating the intermediate storage process. The cross-docking approach thrives on the minimum storage time of goods in the inventory. Most of the cross-docks avail temporary storage docks where items can be stored for up to 24 hours before being packed up for transportation. The storage capacity of the cross-dock varies depending on the nature of ownership. In the rented cross-docks center, the temporary storage docks are considered of infinite capacity. This study believes that the temporary storage facilities owned by the cross-dock center are of finite capacity, which subsequently affects the waiting time of the goods. The flow rate of goods within the cross-docks is expected to be maximum to avoid long waiting for goods in the queue. This paper uses a series of max-flow algorithms, namely Ford Fulkerson, Edmond Karp, and Dinic's, to optimize the flow of goods between the inbound port and the outbound dock and present a logical explanation to reduce the waiting time of the trucks. A numerical example is analyzed to prove the efficacity of the algorithm in finding maximum flow. The result demonstrates that Dinic's algorithm performs better than the Ford Fulkerson and Edmond Karp algorithm at addressing the problem of maximum flow at the cross-dock. The algorithm effectively provided the best result regarding iteration and time complexity. In addition, it also suggested the bottleneck paths of the network in determining the maximum flow.

Integrating vehicle scheduling and open routing decisions in a cross-docking center with multiple docks

Society is facing a series of challenges, as the growth in urban population, the expansion of e-commerce, the pandemic moment (COVID-19), and many others leading to changes in companies’ supply chain, like reducing product delivery time and attention to consumer welfare, the environmental impact, to mention a few. The efficient management of logistic solutions such as cross-docking can contribute to improving the supply chain performance. Here we focus on the integration of scheduling of trucks and routing decisions; the integration of these strategies can significantly reduce costs and help organize the distribution centers and the customers’ services. This article analyzes the integrated problem in which trucks’ scheduling in a cross-docking center with multiple docks is combined with the associated open vehicle routing problem, called Open Vehicle Routing Problem With Cross-Docking (OVRPCD). This approach aims to minimize penalties caused by delays in servicing customers. First, a mixed-integer linear programming model is proposed to solve small instances optimally. Next, two heuristics are proposed to contribute to the solution of the two problems in an integrated way. These heuristics are: the Vehicle Routing Cross-Docking Heuristic (VRCDH) and the Cross-Docking Vehicle Routing Heuristic (CDVRH), each focusing on one of the problems. We also propose a Prioritization Lagrangian Heuristic (PLH) based on a model decomposition to improve the solutions found. These three heuristics are compared, considering two search approaches (i) a constructive version (HC) using the swap heuristic; and (ii) a version using the Variable Neighborhood Search (VNS) metaheuristic framework. The VNS-enhanced versions of the heuristics outperform the previous ones. Still, the same relation holds regarding the three heuristics, i.e.: the PLH heuristic outperforms the VRCDH one, while the latter outperforms the CDVRH one. Finally, we propose a polynomial-time framework, called Robust Dynamic Prioritization Lagrangian Heuristic (RDPLH), which extends PLH, considering trucks’ release dates and travel times uncertainties, approximating our problem to a real cross-docking center. The framework’s simplicity and the quality of the results allow us to assert that this approach can be used in real cross-docking centers (CDCs).

Modeling and Solving the Cross-Docking Centers Location and Vehicle Scheduling Problem in a Multi-Product Supply Chain with Discrete Pick-up and Delivery

در این پژوهش مسائل مکان‌یابی انبارهای متقاطع، مسیریابی و زمان‌بندی وسایل نقلیه را به‌طور هم‌زمان در یک زنجیره تأمین سه‌­سطحی با امکان برداشت و تحویل گسسته، با هدف کمینه‌سازی مجموع هزینه‌ها (هزینه احداث انبارهای متقاطع، هزینه‌های ثابت و متغیر حمل‌ونقل و جریمه تأخیر و تعجیل)، موردمطالعه قرار گرفته و یک مدل برنامه‌ریزی ترکیبی عدد صحیح غیرخطی برای آن ارائه شده است. در این مدل تصمیم‌گیری در خصوص تخصیص وسایل نقلیه ناهمگن به فرآیند برداشت و تحویل و انتخاب مکان و تعداد انبارهای متقاطع برای احداث از میان مکان‌های بالقوه موجود پس از حل مدل صورت می‌گیرد. فرض چندمحصولی‌­بودن شامل تک‌تک تأمین‌کنندگان، انبارهای متقاطع و مشتریان می‌شود. برای تحویل هر نوع از کالاها در محل هر یک از مشتریان یک پنجره زمانی نرم در نظر گرفته شده است و علاوه بر جریمه تأخیر، جریمه تعجیل در تحویل کالاها متناسب با مدت‌زمان و مقدار کالای مواجه‌­شده با تأخیر/ تعجیل محاسبه می­شود. سه دسته مسئله در ابعاد کوچک، متوسط و بزرگ به‌صورت تصادفی تولید و با استفاده از الگوریتم شبیه‌سازی تبرید حل شده‌اند. برای مسائل کوچک، جواب حاصل از روش‌های حل دقیق با نتایج الگوریتم شبیه‌سازی تبرید مقایسه شده است.

Truck Scheduling for Cross-Docking of Fresh Produce with Repeated Loading

Due to mismanagement of supply chain operations, fresh produce, which deteriorates highly depending on time and operating environment (including temperature and humidity), will suffer huge losses in transit, resulting in substantial monetary losses. Cross-docking, as an efficient logistics operation strategy, has been widely used in fresh produce distribution in the cold supply chain, whereas it has not received adequate attention in the scientific literature. In order to improve the efficiency of fresh produce distribution, this study formulates a novel mixed-integer mathematical formulation model that allows repeated loading of outbound trucks to minimize the total deterioration (TD) of all the fresh produce in the cross-docking center. To solve this problem, an advanced genetic algorithm is proposed based on a constructional mixed chromosome with two parts and three levels. The numerical analyses are conducted on 10 typical instances under different combinations of parameters. Results show that our proposed model based on the repeated loading mode can effectively decrease the total deterioration compared with the traditional nonrepeated loading mode. And this superiority becomes more significant, as the value of truck changeover time and lot loading quantity (called lot size in the text) decrease. In particular, when the truck changeover time equals 0, the total deterioration obtained under repeated loading mode will be more than 31.8% on average smaller than that under nonrepeated loading mode.

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

PurposeThe 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/approachThis 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.FindingsTheory 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/implicationsThe 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/valueFor 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.

An efficient parameterized approximation scheme for the Star k-Hub Center

In the Star k-Hub Center (SkHC), given a connected edge-weighted graph G, a center c ε V(G) and integers k, r > 0, one wants to select a set of hubs H ⊆ V(G)\\{c} of size k and an assignment from vertices to hubs. The goal is to find a solution in which the length of the longest path connecting each pair of vertices through the assigned hubs and the center is at most r. This problem appears in many areas, such as telecommunication, aviation and logistics, where hubs represent cross-docking centers that consolidate demand between pairs of clients, and the network has a centralized design. This paper investigates the parameterized complexity of the underlying graph connectivity problem. First, we note that finding a (1.25 - ε-approximation is W[2]-hard, for ε> 0, when the parameter is the number of hubs k. Moreover, we show that the problem is W[1]-hard even when parameterized by k plus the vertex cover number of the graph. While this rules out an algorithm parameterized by the treewidth tw of the graph, as a positive result, we present an efficient parameterized approximation scheme. Namely, for every ε> 0, we have a (1 + ε-approximation that runs in time 0*((tw/e)0(tw)).

Routing cross-docking depots, considering the time windows and pricing routes (case study: container transportation of Chabahar port)

In this study, we develop a model for routing cross-docking centers considering time windows and pricing routs. In this model picking and delivery in several times is permitted and each knot can be serviced by more than one vehicle. Every truck can transport one or more product, in other words, we consider compatibility between product and vehicle. This model includes two goals: reducing the total cost and reducing the cost of carrying goods (freight fare). The total cost includes the cost required to traverse between the points, the cost of traversing the routes between the central cross-docking center and the first points after moving, and the cost to traverse the routes between the last points in each route and the depots that must be minimized. In general, the purpose of the model is to obtain the number of cross-docking center, the number of vehicles and the best route in the distribution network. We present a nonlinear programming model for this problem. We have solved the proposed model by GAMS. As the dimensions of the problem increase, the implementation time of the program increases progressively. So, in order to solve the model in medium and large scales, we proposed a genetic meta-heuristic algorithm. The results of examining different issues by the meta-heuristic approach show the very high efficiency of the developed algorithms in terms of the solution time and the answer of the problem.

Cross-Docking Center Location Selection Based on Interval Multi-Granularity Multicriteria Group Decision-Making

Cross-docking is a new logistics model. The location planning of the crossover center is one of the important issues in logistics management. The location of the cross-docking center is not only a technical issue, but also a management issue. This is a decision made by senior leaders after considering various factors. Therefore, considering the decision-making method, a multicriteria group decision-making method based on an interval multi-granularity language model is proposed. It is suitable for non-static frameworks where the decision-making environment changes at any time during the process. Due to the uncertainty of the location information of the cross-docking center, experts can use their favorite language tag set to provide preferences, so a multi-granular interval fuzzy language model is used to enable experts to reliably provide preference values. At the same time, taking into account the formula threshold for decision-making, after a limited round of discussions, decision-making experts, site selection criteria, and site alternatives can be changed arbitrarily so that when the final opinion is reached, the consensus of experts reaches this threshold. Finally, through the numerical calculation of the site selection center, it is found that the experts will reach a higher level of consensus when joining the experts who change their status. The validity of the method is verified, and the feasibility and applicability of the proposed method are shown.

Reliable cross-docking location problem under the risk of disruptions

This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a recovery policy can be a feasible strategy to handle disruptions. Hence, in this study, a recovery policy has been addressed in the form of reallocating suppliers to alternative cross-docks or altering the transportation strategy to move shipments. In addition to cross-dock location design, the optimum capacity of opened cross-docks will be determined considering the loads that will be served by each cross-docking center under regular and disruption conditions. A mixed integer nonlinear programming formulation is presented for the problem and is then linearized to present an efficient model. In order to solve it, two Lagrangian relaxation algorithms are designed and tested on 40 problem instances with different values of parameters. The results achieved by GAMS/CPLEX are compared with those of two algorithms and some analyses are performed on the solutions. Moreover, as the case study, the focus has been placed on logistic part of a car-manufacturing company with a vast supply chain network, containing more than 600 suppliers. The logistic strategies have been applied in order to reduce the transportation cost through the supply chain network and diminish the disruption subsequences in such a network. Based on the results, some managerial recommendations are presented.

A Lagrangian heuristics for the truck scheduling problem in multi-door, multi-product Cross-Docking with constant processing time

The paper describes a Lagrangian heuristic algorithm for a cross-docking problem, where given amounts of several products must be directly transshipped from a given set of inbound trucks to a given set of outbound trucks. The cross-docking centre is equipped with some inbound and outbound doors (or gates), where the discharging/loading activities, which are assumed to require a constant time for each truck, take place. The objective is to schedule the activities and to design the transshipment plan, while minimizing the ending time of the whole process. Moving along a research line recently traced for the single-door case, the main contribution of the paper is the Lagrangian decomposition scheme for the structured integer linear model of the problem. In particular, decomposition in three subproblems is provided. For all such problems effective solution algorithms are proposed. Two repairing heuristics are embedded into the algorithm for tackling the Lagrangian dual, thus allowing calculation of both lower and upper bounds on the optimal objective function. The performance of the algorithm is evaluated through extensive computational experiments on instances of different typologies in terms of number of gates, trucks and products.

Parallel-machine scheduling methodology for a multi-dock truck sequencing problem in a cross-docking center

Cross-docking is a logistics procedure aiming at minimizing the storage and picking operations of a warehouse. It consists of unloading cargos from trucks and immediately load them into outbound trucks, with limited or no storage in between. An efficient transshipment operation is a critical factor in this strategy. Hence, we study the multi-dock truck sequencing problem in a cross-docking center with the objective of minimizing the makespan. We explore the parallel machine scenario instead of the traditional flow shop setting and propose a polynomial parallel machine based heuristic that outperforms time-indexed mathematical formulation and state-of-the-art heuristics for small, moderate, and large size instances. Our results suggest that the consideration of a parallel machine environment could bring a new light in modeling the truck sequence in a cross-docking facility, opening new research directions in the field.

Truck-to-door sequencing in multi-door cross-docking system with dock repeat truck holding patter

Cross-docking is a logistics strategy that consolidates the products of different inbound trucks according to their destinations in order to reduce the inventory, order picking, and transportation costs. It requires a high level of collaboration between inbound trucks, internal operations, and outbound trucks. This article addresses the truck-to-door sequencing problem. Truck-to-door sequencing has been studied by some researchers in different titles such as scheduling and sequencing of inbound and outbound trucks of the cross-dock center. However, previous studies have not considered repeat truck holding pattern. Therefore, it is important to determine the doors and the sequence of the inbound and outbound trucks that should be assigned in a cross-dock center. This paper focuses on optimizing truck-to-door sequencing with consideration of repeat truck holding pattern in inbound trucks in order to minimize makespan. Two methods are considered to solve this problem, including mathematical modeling and a heuristic algorithm. In the first method, a mixed integer-programming model is developed to minimize the makespan. Then, GAMS software is used to solve small-scale problems. In the second approach, a heuristic algorithm is developed to find near-optimal solutions within the shortest time possible and the algorithm is used to solve large-scale problems. The results of the mathematical model and the heuristic algorithm are slightly different and show the good quality of the presented heuristic algorithm.