Cross-docking Distribution Research Articles

Streamlining logistics: Innovating cross-docking distribution systems for operational excellence

A logistics service company in Batam faces challenges related to warehouse load fulfillment and sorting inaccuracies. This study aims to identify proposed efficiency improvements to the goods distribution system using the cross-docking method. The research method chosen is cross-docking, a technique that eliminates the storage process in the warehouse, thus saving time and cost. The research findings show significant benefits, especially in achieving zero inventory efficiency. Data processing and discussion revealed that efficiencies were apparent by increasing the sorting tables from 1 to 6, with an output of 90,000 kg during aircraft loading and unloading (compared to approximately 77,000 kilograms). This efficiency arises from the larger output of the sorting tables compared to the input, eliminating the need for warehousing and adding ten trucks. As a result, the shipment can be completed in one trip, with no goods stored in the warehouse. The analysis shows that implementing cross-docking in the company increases efficiency in distributing goods to forwarding partners.

A multi-stage stochastic model for designing a linked cross-docking distribution network with heterogeneous trucks

A multi-stage stochastic model for designing a linked cross-docking distribution network with heterogeneous trucks

An order picking operation improvement in a cross-docking distribution centre using simulation: A case study of a chilled distribution centre in Thailand

An order picking operation improvement in a cross-docking distribution centre using simulation: A case study of a chilled distribution centre in Thailand

A developed nonlinear model for the location-allocation and transportation problems in a cross-docking distribution network

The paper addresses the location-allocation and transportation problems in designing a cross-docking distribution network that consists of suppliers, cross-docks, and plants. A developed mixed-integer non-linear model is proposed for a post-distribution cross-docking strategy with multiple cross-docks and products that cross-docks can be connected. The objective function is to minimise the total cost comprising the cost of established cross-docks and transportation costs. To obtain this model, first, two models are introduced and compared (basic non-linear model 1 and non-linear model 2 with the possibility of connections between cross-docks). Results indicate that the total cost is decreased when the connection between cross-docks exists. So, model 2 is more efficient and suitable than model 1. Then, consolidation of plant orders is added to model 2, and the developed model is formulated. Finally, some problems with different sizes are generated randomly and solved by GAMS software to evaluate the model accuracy.

IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM IN CROSSDOCKING DISTRIBUTION PROBLEM

In order to increase customer satisfaction and maintain customer loyalty, logistics service providers must pay attention to the quality of service provided, one of which is effecive warehouse management, especially in scheduling the arrival and departure of products transporting vehicles. Therefore, this study discusses warehouse management in form of delivery and pickup scheduling at PT XYZ’s cross-docking warehouse. This study aims to obtain effective delivery and pickup scheduling and minimize operational costs. The Cross-docking Distribution Problem is an np-hard problem, so the Particle Swarm Optimization algorithm is used, which is a metaheuristic method in finding solutions. Based on the result, it was found that effective delivery and pickup scheduling was able to save inventory cost by 3.12% and reduce the percentage of delays from 73% to 0%. The scheduling process using Particle Swarm Optimization requires an average computation time of 26.2 seconds.

A hub-and-spoke architecture for a parcel delivery system using the cross-docking distribution strategy

This article develops a hub-and-spoke architecture for a parcel delivery system using a network that includes a distribution centre and several cross-dock facilities. Several real-world assumptions, including an electric truck fleet, mobile charging station, third-party logistics, capacity constraints, last-mile deliveries and customer dissatisfaction, are incorporated in this problem. Besides, because of the high complexity of the problem, a novel matheuristic algorithm based on the combination of the fix-and-relax algorithm (FARA) and genetic algorithm (GA), namely Mb-FARGA, is developed to solve large-sized instances. A comprehensive computational analysis is accomplished to validate the proposed mathematical models and evaluate the performance of the Mb-FARGA method. According to the results, the Mb-FARGA method demonstrates a very favourable performance. Moreover, the results show that considering a distribution centre with appropriate capacity in the cross-docking system is an excellent strategy to enhance customer satisfaction, improve the system’s performance and reduce capacity shortages.

Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

Cross-docking distribution is becoming increasingly prevalent in supply chain management (SCM) due to the replacement of storage and order-picking with efficient product consolidation. Several successful cross-docking applications have been put into practice by companies such as Wal-Mart, FedEx, and Home Depot. Cross-docking consists of interrelated operations which require proper synchronization. Only a few early attempts have addressed the vehicle routing problem in this context. Moreover, the social consciousness of sustainable development has brought up the issue of green SCM which is not only environment-friendly but also beneficial to business values. This paper first addresses a previously studied problem and then proposes a new problem version for planning the least cost green vehicle routing in which the deployed vehicles transport the final products from suppliers to customers through a cross-dock subject to a CO2 intensity constraint. We develop an adaptive memory artificial bee colony (AMABC) algorithm to tackle both problems. Compared to a Tabu search proposed in the literature, the AMABC algorithm can reach higher fuel efficiency by managing the loading along the route and yield less cost and CO2 intensities. Statistical tests of simulation and geographic data show that the AMABC method is robust against the problem size and convergence of the objective value is guaranteed with high confidence.

A Multi-Objective Model in Cross Docks Location Problems Considering Data Envelopment Analysis

Background/Objectives: This paper deals with a cross docking distribution strategy where the system consists of O suppliers, J potential location for cross docks and K retailers. In addition all vehicles are equal. Methods/Statistical Analysis: To find an effective policy for this system, a mathematical multi-objective model with aim to minimizing the travelled distance costs between origins and facilities and also between facilities and destinations, minimizing the fixed cost of constructing a cross-dock and maximizing the benefits of being close to the facilities is formulated. Findings: The previous models for problem at hand don’t consider cross docks location technical efficiency so it may led to selecting inappropriate sites. To deal with this dilemma, data envelopment analysis approach added to model as a new objective function and several constraints makes the model more applicable to real-world systems. The mathematical model of this problem belongs to nonlinear mixed integer-programming and hence a parameter-tuned simulated annealing is proposed to solve it efficiently. The results of solving numerical examples show the applicability of the proposed methodology clearly. Application/Improvements: Because of considering technical efficiency for each dock, mathematical model is closer and more applicable to real-world environments than the other existing ones.

Particle swarm optimization for the multi-period cross-docking distribution problem with time windows

Cross-docking has emerged as a new technique in supply chain management to replace the warehouse concept in the retail industry. This paper proposes a multi-period cross-docking distribution problem that consists of manufacturers, cross-docks and customers. This model is formulated for cases that consider multiple products, consolidation of customer orders and time windows that are available in multiple periods. The objective function is to minimise the total cost, which includes transportation cost, inventory cost and penalty cost. The penalty cost arises when demand remains in each period that cannot be satisfied. To deal with the complexity of the problem, an algorithm is developed based on particle swarm optimisation (PSO) with multiple social learning terms, GLNPSO, with two solution representations. The solution representations are a one-period solution representation (OP-SR) and a multi-period solution representation (MP-SR). The GLNPSO-based algorithm performs well in solving this problem. Moreover, both representations are proven effective when comparing the solution quality and computational time with those results obtained from CPLEX. In terms of quality, the MP-SR solution is better than the OP-SR solution for both stable and fluctuating demand instances. However, MP-SR requires more computational effort than OP-SR.

Two-agent scheduling with agent specific batches on an unbounded serial batching machine

We study a scheduling problem, in which jobs of two agents are performed in agent specific batches on the same serial unbounded batching machine. On this machine, jobs of the same batch complete simultaneously, and the batch processing time is equal to the total processing time of its jobs plus a setup time. Each agent aims at minimizing a function which depends only on the completion times of its jobs. The problem is to find a schedule that minimizes the objective function of one agent, subject to the objective function of the other agent does not exceed a given threshold. The problem appears in optimizing product consolidation operations of a cross-docking distribution center. Polynomial and pseudo-polynomial dynamic programming algorithms are derived for settings with various combinations of the objective functions.

Location of cross-docking centers and vehicle routing scheduling under uncertainty: A fuzzy possibilistic–stochastic programming model

The location of multiple cross-docking centers (CDCs) and vehicle routing scheduling are two crucial choices to be made in strategic/tactical and operational decision levels for logistics companies. The choices lead to more realistic problem under uncertainty by covering the decision levels in cross-docking distribution networks. This paper introduces two novel deterministic mixed-integer linear programming (MILP) models that are integrated for the location of CDCs and the scheduling of vehicle routing problem with multiple CDCs. Moreover, this paper proposes a hybrid fuzzy possibilistic–stochastic programming solution approach in attempting to incorporate two kinds of uncertainties into mathematical programming models. The proposed solving approach can explicitly tackle uncertainties and complexities by transforming the mathematical model with uncertain information into a deterministic model. m′ imprecise constraints are converted into 2Rm′ precise inclusive constraints that agree with Rα-cut levels, along with the concept of feasibility degree in the objective functions based on expected interval and expected value of fuzzy numbers. Finally, several test problems are generated to appraise the applicability and suitability of the proposed new two-phase MILP model that is solved by the developed hybrid solution approach involving a variety of uncertainties and complexities.

Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain

We present a solution framework based on discrete-event simulation and enhanced robust design technique to address a multi-response optimization problem inherent in logistics management. The objective is to design a robust configuration for a cross-docking distribution center so that the system is insensitive to the disturbances of supply uncertainty, and provides steady parts supply to downstream assembly plants. In the proposed approach, we first construct a simulation model using factorial design and central composite design (CCD), and then identify the models that best describe the relationship between the simulation responses and system factors. We employ the response surface methodology (RSM) to identify factor levels that would maximize system potential.To make the system immune to factors that could adversely affect performance, we propose a robust design approach by incorporating Latin hypercube sampling and take the noise factors (disturbances) into account. Due to the need of accommodating multiple performance measures and to ensure all responses stay within the desired targets, we adapt Derringer–Suich’s desirability function to determine the optimal operating conditions. Finally, we use bootstrapping to compare the results obtained by the classic RSM and the proposed robust design. The proposed model helps the studied auto parts supply chain to develop insights into the system dynamics, and to identify the operating setting that minimize the impact of supply uncertainty on the performance of the cross-docking facility.

Crossdocking distribution networks with setup cost and time window constraint

In this work, we study a new shipment consolidation and transportation problem in crossdocking distribution networks that considers trade-offs between transportation costs, inventory and time scheduling requirements. Transportation costs include time costs, truck setup costs, and the number of trucks used. The model is formulated as an integer program, and shown to be NP -complete in the strong sense. Moreover, a solution approach is provided which consists of two stages. First, a reduced problem is solved for a truckload transportation plan. This is followed by a heuristic solution approach to the remaining less-than-truckload problem. Computational experiments are conducted to test the effectiveness and efficiency of the heuristics. The various cost parameters and time window settings of the distribution network are also discussed.

The benefits of a cross-docking delivery strategy: a supply chain collaboration approach

In this study, two models are developed to co-ordinate both production and distribution in order to reduce the relevant costs in a supply chain. According to the functions of the distribution centre, there is a distinction between an inventory co-ordination point and an inventory storage point. The former is a cross-docking strategy, and the latter is a traditional warehousing strategy. The manufacturer has distinct production processes when co-operating with the distribution centre. Case study analysis is used to illustrate the models developed, in which the following conclusions can be obtained: the cross-docking distribution strategy will result in tremendous savings in the total system cost for the supply chain. This study makes two important contributions to the production, and logistics literature. First, the range of applications was extended by studying a new combinatorial problem that incorporates cross-docking in a supply chain environment. Second, computational performance with cross docking was evaluated. The benefit delivery policy for a distribution centre was found by means of integrating manufacturer production planning and a distribution centre delivery policy.

Optimisation of a Crossdocking Distribution Centre Simulation Model

This paper reports on continuing research into the modelling of an order picking process within a Crossdocking distribution centre using Simulation Optimisation. The aim of this project is to optimise a discrete event simulation model and to understand factors that affect finding its optimal performance. Our initial investigation revealed that the precision of the selected simulation output performance measure and the number of replications required for the evaluation of the optimisation objective function through simulation influences the ability of the optimisation technique. We experimented with Common Random Numbers, in order to improve the precision of our simulation output performance measure, and intended to use the number of replications utilised for this purpose as the initial number of replications for the optimisation of our Crossdocking distribution centre simulation model. Our results demonstrate that we can improve the precision of our selected simulation output performance measure value using Common Random Numbers at various levels of replications. Furthermore, after optimising our Crossdocking distribution centre simulation model, we are able to achieve optimal performance using fewer simulations runs for the simulation model which uses Common Random Numbers as compared to the simulation model which does not use Common Random Numbers.