Cross-docking Center Research Articles

Using cross-docking operations in a reverse logistics network design: a new approach

Increasing development of competitive market has forced organizations to make great efforts in supplying, production and distribution of goods in their company so that they are capable of responding the customers different needs at the minimum delivery time and lowest cost. Cross-docking is a practical strategy in distribution cycle which has significantly attracted the attention of experts and industrialists in different areas. In this paper, the problem of designing a multi-echelon reverse logistics network with applying cross-docking centers is presented—as the first attempt to propose the new approach of using cross-docking centers in reverse logistics network. In this regard, a mixed-integer linear programming is utilized to model the problem for the goals of increasing shipment rate, decreasing fixed, variable costs and better management of returned products. Finally, the validation and sensitivity analysis are done by using the GAMS software. Considered the above requirements, the model facilitates objective-oriented reverse logistic performance.

The Optimal Allocation of Unloading/Loading Ramps in Distribution Centers

Warehousing enables the consolidation of shipments to clients by assembling full truck loads from goods stored in warehouse or distribution center. Storage can be efficiently replenished by ordering full truck loads from suppliers. In order to reduce freight storage and handling costs from distribution centers, cross-docking technique represents a logistic solution which is more and more used. Thus, the optimization of simultaneous unloading/loading operations constitutes an important issue for the distribution center owner/administrator. This paper presents a model based on genetic algorithms optimization techniques for solving the problem of unloading/loading ramps allocation. The efficiency of a cross-docking center is conditioned practically by finding an allocation and an optimal arrangement of ramps. This implies the allocation of destinations to unloading/loading ramps of distribution centers to minimize the total distance performed by the the cargo handling equipment. The case study presents a cross-docking platform with single handling equipment which is assigned to 10 unloading/loading ramps. By applying the solution of the ramp allocation generated through simulation, the total distance performed by the handling equipment is reduced which means lower handling cost for the distribution center owner/administrator.

A hybrid meta-heuristic algorithm for vehicle routing and packing problem with cross-docking

Cross-docking is a relatively new logistics strategy that has a great potential to eliminate storage cost and speed up the product flows. This paper considers the vehicle routing and packing problem with cross-docking and presents a mixed integer linear mathematical model. In the model, a set of trucks are used to transport products from suppliers to customers through cross-docking centers. Each supplier and customer node can be visited only once and directly shipping is not allowed from suppliers to customers. Moreover, truck capacities are identified with physical dimensional limits on the contrary of weight or amount of load. The objective of the study is to determine the vehicle routes that minimize the total distance. Due to the complexity of the mathematical model, a hybrid meta-heuristic algorithm (HMA), which integrates tabu search (TS) algorithm within simulated annealing (SA) algorithm, is proposed to solve the problem. Proposed HMA is tested on a well-known benchmark problem data set and compared with the SA and TS solutions. Results show that proposed HMA can produce effective solutions and outperforms the SA and TS especially for the large-sized problems.

A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment

One of the most important problem in supply chain management is the design of distribution systems which can reduce the transportation costs and meet the customer's demand at the minimum time. In recent years, cross-docking (CD) centers have been considered as the place that reduces the transportation and inventory costs. Meanwhile, neglecting the optimum location of the centers and the optimum routing and scheduling of the vehicles mislead the optimization process to local optima. Accordingly, in this research, the integrated vehicle routing and scheduling problem in cross-docking systems is modeled. In this new model, the direct shipment from the manufacturers to the customers is also included. Besides, the vehicles are assigned to the cross-dock doors with lower cost. Next, to solve the model, a novel machine-learning-based heuristic method (MLBM) is developed, in which the customers, manufacturers and locations of the cross-docking centers are grouped through a bi-clustering approach. In fact, the MLBM is a filter based learning method that has three stages including customer clustering through a modified bi-clustering method, sub-problems’ modeling and solving the whole model. In addition, for solving the scheduling problem of vehicles in cross-docking system, this paper proposes exact solution as well as genetic algorithm (GA). GA is also adapted for large-scale problems in which exact methods are not efficient. Furthermore, the parameters of the proposed GA are tuned via the Taguchi method. Finally, for validating the proposed model, several benchmark problems from literature are selected and modified according to new introduced assumptions in the base models. Different statistical analysis methods are implemented to assess the performance of the proposed algorithms.

SELECTED ASPECTS OF SIMULATION MODELLING OF INTERNAL TRANSPORT PROCESSES PERFORMED AT LOGISTICS FACILITIES

The transport is an important part of logistic systems. Improper management of transport operations may contribute to the low level of the usage of vehicles and to high transport costs, as well as to the formation of unnecessary high inventory at each location of storage, as well as prolonged time of order realization and not full use of company capacity. It is therefore important the appropriate dimensioning, planning of the transport system and performed transport operations so as to allow the supply of certain goods at the right time and the amount to the appropriate points of the system. The article presents the methods of transport operations modelling, taking into account different criteria based on discrete event simulation. In the article the case study of modelling transport operations in the small cross-docking centre is also presented

Vehicle scheduling and routing at a cross docking center for food supply chains

Cross-docking is increasingly used to reduce inventory holding and the time products spend in the supply chain. It is especially suitable for fresh produce distribution with a short shelf-life. This research studies cross docking operations to ensure food can be delivered just in time and with minimum cost of delivery, including inventory holding and transportation costs, and the penalty costs of early or tardy deliveries. The research focuses on the integration of vehicle scheduling and routing in one comprehensive model, which have traditionally been modeled separately. The integrated model also considers product consolidation at the warehouse and respects delivery time windows specified by the customer. The VRSP system is modeled as a mixed-integer linear program in CPLEX. Experiments reveal that it can be solved in a reasonable amount of time only for small sized problems. To reduce the size of the solution space, the concept of customer zones is introduced and hard instead of soft time windows are applied (VRSP-CZHTW). Extensive experiments are conducted with different size problems. The VRSP-CZHTW problem can still be modeled using CPLEX but can now be solved in a short time even for real-life size problems. The research is the first to include all the operations performed at a cross-dock warehouse in a single comprehensive model, and incorporates practical constraints for customer delivery zones and delivery time-windows. The novelty is in modeling food distribution efficiency, and in supporting food distributors in providing better delivery service for the time sensitive items at lowest logistics cost.

Simulated Annealing Approach for Transportation Problem of Cross-docking Network Design

Cross-docking is one of the effective methods applied in supply chain management in order to minimize total transportation costs while satisfying the customer demands. This paper addresses the transportation problem of cross-docking network design where products are transferred from suppliers to customers through cross-docking centers without storing them for a long time. Two- dimensional truck loading constraints are taken into account for different sized products in order to find exact capacity of each truck. This assumption allows decision makers to find idle vehicle capacities and evaluate the capacity utilizations as a cost factor. The problem is formulated using mixed integer programming and solved using simulated annealing (SA) meta-heuristic algorithm which has great potential to solve NP-Hard problems. The proposed algorithm is performed for several randomly generated examples and compared with the optimum solutions. Results show that proposed algorithm demonstrates effective and efficient solutions to reduce the total transportation costs in cross-docking network design.

A Mathematical Model for Two Dimensional Loading Problem in Cross-Docking Network Design

In this paper, the transportation problem of cross-docking network is taken into account. Cross-docking centers differ from distribution centers or warehouses in that the loads are transported from origins to destinations through cross-docking centers without storing them for a long time. Goods are unloaded from incoming trucks and reloaded immediately onto outgoing trucks in these cross-docking centers. This problem is formulated using a mixed integer programming with two-dimensional loading constraints. The two-dimensional shapes are applied for both trucks and loads in order to find exact capacity of each truck in basis of each product. The illustrative problems are solved and results show that the proposed model finds practical solutions with two- dimensional loading constraints.

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.

Application of cultural algorithm in cross-docking scheduling

This paper studied on the operational scheduling problem in a cross-docking center of a single receiving and a single shipping door with finite temporary storage.A dynamic programming model was built with the objective to minimize the costs including additional handing,temporary storage and truck replacement cost.A cultural algorithm with two layer evolutionary mechanism was proposed to solve the problem.The evolution of population space adopted genetic algorithm,and the belief space received good individual from population space to form knowledge which was used to guide evolution in turn.Numerical experiments under small and big scale situations prove the validity of proposed cultural algorithm.

A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain

The location and routing scheduling problems with cross-docking can be regarded as new research directions for distribution networks in the supply chain. The aims of these problems are to concurrently design a cross-docking center location and a vehicle routing scheduling model, known as NP-hard problems. This paper presents a two-stage mixed-integer programming (MIP) model for the location of cross-docking centers and vehicle routing scheduling problems with cross-docking due to potential applications in the distribution networks. Then, a new algorithm based on a two-stage hybrid simulated annealing (HSA) with a tabu list taken from tabu search (TS) is proposed to solve the presented model. This proposed HSA not only prevents revisiting the solution but also maintains the stochastic nature. Finally, small and large-scale test problems are randomly generated and solved by the HSA algorithm. The computational results for different problems show that the proposed HSA performs well and converges fast to reasonable solutions.

Vehicle Routing Problem for Fashion Supply Chains with Cross-Docking

Cross-docking, as a strategy to reduce lead time and enhance the efficiency of the fashion supply chain, has attracted substantial attention from both the academy and the industry. Cross-docking is a critical part of many fashion and textiles supply chains in practice because it can help to achieve many supply chain strategies such as postponement. We consider a model where there are multiple suppliers and customers in a single cross-docking center. With such a model setting, the issue concerning the coordinated routing between the inbound and outbound routes is much more complex than many traditional vehicle routing problems (VRPs). We formulate the optimal route selection problems from the suppliers to the cross-docking center and from the cross-docking center to the customers as the respective VRPs. Based on the relationships between the suppliers and the customers, we integrate the two VRP models to optimize the overall traveling time, distance, and waiting time at the cross-docking center. In addition, we propose a novel mixed 0/1 integer linear programming model by which the complexity of the problem can be reduced significantly. As demonstrated by the simulation analysis, our proposed model can be solved very efficiently by a commonly used optimization software package.

Estimating Unloading Time at Cross Docking Centre by Using Fuzzy Logic

Estimating Unloading Time at Cross Docking Centre by Using Fuzzy Logic

A study of China’s logistics network infrastructures and management

Logistics network infrastructures (LNIs) and management are in low growth in the developed countries, in this paper, the theory of LNIs is firstly overviewed. Then a new evaluation index system for LNIs evaluation is set up which contains factors that reflect the scale of logistics industry, economic development level, transportation accessibility and turnover volume of freight traffic. An empirical study is carried out by using principal component analysis (PCA) and data envelopment analysis (DEA) approach to classify LNIs into 4 clusters for 31 regions of China. According to the characteristics of the 4 clusters, suggestions are proposed for improving their LNIs. Finally, this research proposes that different LNIs including hub, central distribution center or cross docking center, regional distribution center or distribution center should be built reasonably in order to meet the customer’s requirement in the four different cluster regions. Key words: Logistics network infrastructures (LNIs), performance, data envelopment analysis (DEA), principal component analysis (PCA).

Research on logistics network infrastructures based on DEA-PCA approach: Evidence from the yangtze river delta region in China

Compared with the extensive research on logistics network infrastructures (LNIs) in the developed world, empirical research is still scarce in China. In this paper the theory of LNIs is firstly overviewed. Then a new evaluation index system for LNIs is set up which contains factors that reflect the economic development level, transportation accessibility and turnover volume of freight traffic. An empirical study is carried out by using data envelopment analysis (DEA) and principal component analysis (PCA) approach to classify LNIs into 4 clusters for 25 cities in the Yangtze River Delta Region of China. According to the characteristics of the 4 clusters, suggestions are proposed for improving their LNIs. Finally, after comparing different LNIs of 25 cities in the Yangtze River Delta Region of China, this paper proposes that different LNIs including hub, central distribution center or cross docking center, regional distribution center or distribution center should be built reasonably in order to meet the customer’s requirement in the four different cluster cities.

Cross-docking centre operation optimization using simulation-based genetic algorithm

The operational improvement of cross-docking centres in the literature involves job scheduling, layout design and dock assignment. The mentioned research is mainly concerned with makespan, number of tardy jobs or travel distance. However, these measures may not fully represent the main advantages of cross-docking operations. Little has been done to optimize the major performance measures of a cross-docking centre, which substantiates the main advantages of cross-docking technology. In this paper, inventory holding cost, transportation cost and backorder penalty cost are aggregated into total cost and a solution framework has been developed by integrating simulation, genetic algorithm (GA) and smart computing budget allocation (SCBA) for a comprehensive total cost minimization problem. This problem has huge search space even for medium-sized problem scenarios. To address this difficulty, the framework employs simulation to estimate the total cost, GA to search for better design and SCBA to efficiently allocate the simulation budget. Numerical experiments show that the proposed framework operates effectively in cases of different problem scales.

Layout and control policies for cross docking operations

Many supply chains strive to shorten the time between a customer’s order and the actual delivery of the ordered goods. Cross docking is one of the options to reduce these response times. Cross docking facilities are dynamic environments where products arrive and leave the same day. To deal with these dynamic aspects, the need for efficient control policies in combination with flexible and easy to reconfigure storage area layouts increases. We present a dynamic design methodology to select control policies and determine layout rules for cross docking facilities. We present both advantages and disadvantages of the proposed layout procedures. We use the cross docking center of the “Royal Horticultural Company Lemkes” in The Netherlands as the basis for a comparative analysis of the procedures presented. We show that, by applying our design methodology, a savings of 16% can be obtained in total travel distances of employees.

Simulation-Based Genetic Algorithm for Cross-Docking Center Operation Optimization under Supply Disruptions

Practical performance optimization of a cross-docking center has been rare in the literature so far. The measures representing operation efficiency are average inventory level and transportation cost rate, while average backorder level represents the customer service level. In this paper, a simulation optimization problem is considered and a solution framework has been developed by integrating simulation, genetic algorithm (GA) and smart computing budget allocation (SCBA) to find an optimized solution. Moreover, supply disruptions are considered in the simulation model. This problem has huge search space even for medium-sized problem scenarios. To address this difficulty, the framework employs simulation to estimate the performance measures, GA to search for better design and SCBA to efficiently allocate the simulation budget.

Research on Logistics Network Infrastructure Based on HCA and DEA-PCA Approach

Logistics Network Infrastructure (LNI) is an important area of Logistics Infrastructure Capability (LIC). The connotation of LNI is analyzed in this paper. Compared with the extensive research on LNI in developed world, empirical work is still rare in China. In this paper the theory of LNI is firstly overviewed. Then a new evaluation index system for LNI evaluation is set up which contains factors that reflect economic development level, transportation accessibility and turnover volume of freight traffic. Thirdly, an empirical study is carried out by using Hierarchical Cluster Analysis (HCA), Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) approach to classify LNI into 4 clusters for 25 cities in Yangtze River Delta Region of China. Fourthly, according to the characteristics of the 4 clusters, suggestions are proposed for improving their LNI. Finally, after comparing different LNI of 25 cities in Yangtze River Delta Region of China, this paper focuses on that different LNI including Hub, Central Distribution Center & Cross Docking Center, Regional Distribution Center or Distribution Center should be build reasonably in order to meet the customer's requirement in the 4 different cluster cities.

Research on Urban Logistics Infrastructure: An Empirical Study of China

Urban Logistics Infrastructure (ULI) is an important area of urban competition capability. The connotation of Urban Logistics Capability (ULC) is analyzed in this paper. Compared with the extensive research on ULC in developed world, empirical work is still rare in China. In this paper the theory of ULC is firstly overviewed. Then a new evaluation index system for ULC evaluation is set up which contains factors that reflect the market supply and demand, economic devel-opment and transportation accessibility. Secondly, an empirical study is carried out by using Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) method to classify ULC into 3 clusters for 30 cities in Peo-ple's Republic of China. Thirdly, according to the characteristics of the 3 clusters, suggestions are proposed for im-proving their ULI. Finally, after comparing different ULC of 30 cities in People's Republic of China, this paper focuses on that different logistics infrastructure including Hub, Central Distribution Center & Cross Docking Center, Regional Distribution Center or Distribution Center should be build reasonably in order to meet the customer’s requirement in the 3 different cluster cities.