Distribution Center Location Research Articles

Two-phase semi-Lagrangian relaxation for solving the uncapacitated distribution centers location problem for B2C E-commerce

This paper develops a mixed integer programming model for determining uncapacitated distribution centers location (UDCL) for B2C E-commerce. Based on the characteristics of distribution system for B2C E-commerce firms, the impact of supply cost of multi-commodity is considered in this model. Combining with the properties of the semi-Lagrangian relaxation (SLR) dual problem in the UDCL case, a two-phase SLR algorithm with good convergency property is furthermore developed for solving the UDCL problem. For the sake of contrastive analysis, this paper has performed computational experiments on 15 UDCL instances by the mixed integer programming solver, CPLEX, and the approach obtained by combining two-phase SLR algorithm with CPLEX, respectively. The numerical results show that the two-phase SLR algorithm not only can improve the solving speed of the CPLEX solver, but also can provide better results in reasonable time for most instances.

Location Problem of Distribution Center Based on Baumer Walvar Model: Taking Jiaji Logistics as an Example

Jiaji Logistics is one of the biggest logistics enterprises in our country, but the survey found that there are still many problems in it, which limits the development of Jiaji logistics. One of the important factors is the location of the distribution center. In order to overcome this difficulty, the location of Jiaji logistics is used scientifically, and it can also help the development of logistics industry in China. First of all, in this paper, by analyzing distribution center location of the relevant literature both at home and abroad, according to oneself the circumstance of master data, choose the Baumov Model for the theme as a solution. Then, the factors affecting the distribution center of Jiaji logistics are selected, and the optimal solution is obtained by the successive approximation method. The final result will be the distribution center capacity, location, volume allocation and so on. The improved model of the Baumov Model was a great help to the location of the Jiaji logistics, which reduced the cost of the Jiaji logistics.

A Hybridization of Cuckoo Search and Differential Evolution for the Logistics Distribution Center Location Problem

The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search‐differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with the mutation operation of DE to generate solutions. In addition, the mutation operation of DE is modified dynamically. The mutation operation of DE varies under different searching stages. The proposed CSDE algorithm is tested on 10 benchmarking functions and applied in solving a logistics distribution center location problem. The performance of the CSDE is compared with several metaheuristic algorithms via the best solution, mean solution, and convergence speed. Experimental results show that CSDE performs better than or equal to CS, ICS, and some other metaheuristic algorithms, which reveals that the proposed CSDE is an effective and competitive algorithm for solving the logistics distribution center location problem.

Research and application on logistics distribution optimisation problem using big data analysis

The logistics distribution contains wok with large data. To excavate valuable information from the data to improve the reduction and efficiency of logistics distribution cost, the optimisation of logistics distribution centre location is discussed under the environment of big data. The features of logistics distribution and algorithm design idea are provided using basic platform of MapReduce and integrated with data mining clustering algorithm. It is verified to provide a decision scheme for any logistics route optimisation in logistics distribution chain according to the size of the granularity of space division. Compared to traditional clustering, the computation overhead is much less that by Dijkstra distance, The mode using MapReduce to summarise the represents nodes performs parallel computing optimise the efficiency. The scheme also shows practicality and reliability to assist the logistics enterprises with lower cost and better benefit.

Robust location-allocation network design for earthquake preparedness

We develop robust models for earthquake preparedness by optimizing the number, location, and capacity of distribution centers (DCs). The goal is to minimize the total social costs, which include setup and initial supplies, as well as the deprivation costs associated with delayed access to supplies. The models incorporate various earthquake magnitude-specific uncertainties, such as facility damage, casualty by severity, and travel time. Examining the concept of social costs in light of an emerging concern in humanitarian logistics - the robustness of relief networks, we model two types of robustness: parameter uncertainty within a scenario and relative regret across scenarios. This unique approach reveals (1) the magnitude of social costs in the aftermath of an earthquake; (2) the hidden risks associated with inaccurate modeling of deprivation costs; and (3) the impact of budgetary constraints. We demonstrate the applicability of our approach via a case study featuring the Northridge region in California, which experienced two of the strongest earthquakes recorded in North America in 1971 and 1994.

DETERMINATION OF PHARMACEUTICAL INDUSTRIAL DISTRIBUTION CENTER LOCATION USING CENTER OF GRAVITY METHOD: CASE STUDY AT PT JKT

Distribution center has an important role in an industry's supply chain management to facilitate logistics management requirements so that the product distribution process becomes smooth and fast. The purpose of this study is to determine the best location that can be used in determining the location of the distribution cente in pharmaceutical industry. This research uses the center of gravity method to calculate optimal location that will be considered. From the results of this study, the calculation using the Center of Gravity method found that the location of the selected distribution center was at the coordinates (-6.257108; 106.7315), the settlement area of the village of Jurangmangu Timur. The available area according to warehousing rules does not allow the construction of the distribution center in residential area of Jurangmangu Bintaro village, empty area is available around 4 KM west of T8 Pakulonan Alam Tangerang Selatan, this area provides warehousing by offering a combined trading center, office and integrated food and beverage plaza with shelter have advantages as distribution center. The new proposed distribution center location is located 14 KM south and 13 KM from the north of the available warehouse, the new suggested location is closer to and located between current distribution locations.

A Maximal Covering Problem in Supply Chain Considering Variable Radius and Gradual Coverage with the Choice of Transportation Mode

Today, supply chain management is a fundamental principle of business infrastructure implementation in the world. One of the most important pillars of the supply chain management is distribution network design. A distribution network with appropriate structure reduces costs and enhances customer-perceived service level, thereby improving competitive advantages of the company. In supply chain analysis, it is essential to focus on customers, as they greatly affect the formation of chain. This requires a balance between associated costs and the level of service provided. Variable radius and gradual radius maximal covering problem focus on the service levels in relation to the costs incurred to the system. In this study, a bi-objective mixed-integer nonlinear programming model was presented to determine optimal number, location and capacity of factories, distribution centers, and retailers, select transportation modes between the chain elements, and evaluate coverage radius of retailers, in such a way to minimize total transportation costs, maximize the covered demand, and achieve gradual coverage of facilities in a variable radius scheme. The proposed model was validated on a number of sample problems produced and solved by GAMS optimization software. Being NP-Hard, the problem was proposed to be solved via NSGA-II algorithm when its dimensions go large. Analysis of the computational results and respective comparisons indicated good performance of the presented algorithm.

A Distribution Center Location Problem with the Integration of Upstream Transportation

A Distribution Center Location Problem with the Integration of Upstream Transportation

Long-Distance, Multimodal Freight in a Continental Transportation Model

This paper proposes a model of long-distance freight traffic that is suitable for transportation models covering very large areas. Three challenges are discussed in turn. First, the geographic distribution of trips not only depends on locations of production and consumption, but also on the choice between alternative logistic distribution chains and the locations of intermediate distribution centers. Second, any stage of the distribution chain may combine several modes into a multi-leg transport. Third, the large scale of the model leads to large zones, implying a significant share of intrazonal traffic. The proposed approach adapts the four-stage model by generalizing destination choice into a distribution channel model and by introducing a mode sequence choice model for multimodal transport. A simplified distance band model is applied to intrazonal traffic.

Postharvest Supply Chain with Microbial Travelers: a Farm-to-Retail Microbial Simulation and Visualization Framework.

Fresh produce supply chains present variable and diverse conditions that are relevant to food quality and safety because they may favor microbial growth and survival following contamination. This study presents the development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through postharvest supply chain processes. The postharvest supply chain with microbial travelers (PSCMT) tool provides a modular process modeling approach and graphical user interface to visualize microbial populations and evaluate practices specific to any fresh produce supply chain. The resulting modeling tool was validated with empirical data from an observed tomato supply chain from Mexico to the United States, including the packinghouse, distribution center, and supermarket locations, as an illustrative case study. Due to data limitations, a model-fitting exercise was conducted to demonstrate the calibration of model parameter ranges for microbial indicator populations, i.e., mesophilic aerobic microorganisms (quantified by aerobic plate count and here termed APC) and total coliforms (TC). Exploration and analysis of the parameter space refined appropriate parameter ranges and revealed influential parameters for supermarket indicator microorganism levels on tomatoes. Partial rank correlation coefficient analysis determined that APC levels in supermarkets were most influenced by removal due to spray water washing and microbial growth on the tomato surface at postharvest locations, while TC levels were most influenced by growth on the tomato surface at postharvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how postharvest supply chain practices influence the fate of microbial contamination on fresh produce.IMPORTANCE Preventing the contamination of fresh produce with foodborne pathogens present in the environment during production and postharvest handling is an important food safety goal. Since studying foodborne pathogens in the environment is a complex and costly endeavor, computer simulation models can help to understand and visualize microorganism behavior resulting from supply chain activities. The postharvest supply chain with microbial travelers (PSCMT) model, presented here, provides a unique tool for postharvest supply chain simulations to evaluate microbial contamination. The tool was validated through modeling an observed tomato supply chain. Visualization of dynamic contamination levels from harvest to the supermarket and analysis of the model parameters highlighted critical points where intervention may prevent microbial levels sufficient to cause foodborne illness. The PSCMT model framework and simulation results support ongoing postharvest research and interventions to improve understanding and control of fresh produce contamination.

Capacitated closed-loop supply chain network design under uncertainty

This study optimizes the design of a closed-loop supply chain network, which contains forward and reverse directions and is subject to uncertainty in demands for new & returned products. To address uncertainty in decision-making, we formulate a two-stage stochastic mixed-integer non-linear programming model to determine the distribution center locations and their corresponding capacity, and new & returned product flows in the supply chain network to minimize total design and expected operating costs. We convert our model to a conic quadratic programming model given the complexity of our problem. Then, the conic model is added with certain valid inequalities, such as polymatroid inequalities, and extended with respect to its cover cuts so as to improve computational efficiency. Furthermore, a tabu search algorithm is developed for large-scale problem instances. We also study the impact of inventory weight, transportation weight, and marginal value of time of returned products by the sensitivity analysis. Several computational experiments are conducted to validate the effectiveness of the proposed model and valid inequalities.

A bi-objective multi-echelon supply chain model with Pareto optimal points evaluation for perishable products under uncertainty

Selecting the most suitable optimal point among the Pareto optimal points could help the experts to make an appropriate decision in an uncertain and complex situation. In this paper, an evaluating and ranking approach is proposed based on hesitant fuzzy set environment to assess the obtained Pareto optimal points from the proposed bi-objective multi-echelon supply chain model with locating distribution centers. In this respect, the proposed model has elaborated for perishable products based on fuzzy customers' demand. To address the issue, the possibilistic chance constraint programming approach has manipulated based on the trapezoidal fuzzy membership function. Moreover, the proposed hesitant fuzzy ranking approach is constructed based on group decision analysis and the last aggregation approach. Thereby, the last aggregation approach by aggregating the experts' opinions in last step could prevent the data loss. However, a case study about the perishable dairy products is considered to indicate the applicability of the proposed bi-objective multi-echelon supply chain model with locating distribution centers. Finally, a comparative analysis is provided between the obtained results and the current practice to show the feasibility and efficiency of the proposed model.

Location planning of urban distribution center under uncertainty: A case study of Yogyakarta Special Region Province, Indonesia

Purpose: The paper aims at proposing a framework and demonstrating application of the framework to evaluate and select the appropriate location for Urban Distribution Center (UDC) in Yogyakarta Special Region Province, Indonesia.Design/methodology/approach: The proposed framework consists two steps of analysis. First, spatial analysis to generate alternative locations using weighted Geographical Information System (GIS) data; second, multi-criteria decision-making to select of the best location. Multi-criteria decision-making method based on fuzzy logic was applied to deal with uncertainty. Accessibility, security, connectivity of multi-modal transport, costs, environmental impact, proximity to customers, proximity to suppliers, resource availability, expansion possibility, service quality, are taken as the decision criteria.Findings: The proposed framework has been able to explain the unsuitability of Jogja Inland Port. Results further indicate that Jogja Inland Port appears to be the worst alternative, whereas the best location for UDC is located in Berbah (Sleman district) where a joint warehouse managed by a group of private companies has existed.Research limitations/implications: As the study has involved the government, experts, and practitioners, future research could engage local residents as decision makers to refine the results, as various stakeholders may have different preferences.Practical implications: From a practical point of view, the present study provides insights to the government to the application of combined approach in determining UDC location as well as evaluation toward the acceptability of Jogja Inland Port.Social implications: The unsuccessful establishment of Jogja Inland Port implies that associated stakeholders, i.e., government, practitioners, residents, should be taken into consideration when planning Urban Distribution Center (UDC) establishment.Originality/value: The research proposes a framework to evaluate and select the appropriate location for Urban Distribution Center (UDC) through combined approach of spatial analysis and multi-criteria decision-making which involves relevant stakeholders.

Micro Super Logistics System

The development background of the ultra micro as a starting point,the construction of an ultra micro environmental are analyzed, the pilot micro over the layout of the service function of positioning, the SLP and internal operation unit mutual relationship analysis method of pilot of an ultra micro location, location layout, internal environment were analysis and design, through the establishment of distribution center to solve the distribution of the micro - expansion stage, route optimization problem, by center of gravity method combined with practice of distribution center location, distribution center management process optimization, carried on the design to the information system. Through a series of methods for the final completion of micro super logistics system integration design.

Location Decision of Logistics Distribution Centers Based on Artificial Neural Network

With the never-ending changes and improvements of science and technology and the rapid development of economy, modern logistics plays an increasingly important role in social and economic development. As an important link between enterprises and consumers, logistics distribution is critical in the smooth development of logistics activities, and the reasonable location of logistics distribution centers is directly related to the distribution efficiency, logistics cost and consumer satisfaction of enterprises. Therefore, this paper studies the location decision of logistics distribution centers, and with the aid of artificial neural network, which can simulate the neural network of human brain and reproduce the process of brain thinking decision, establishes the BP neural network evaluation model, and evaluates the merits and demerits of several location plans with examples. The results show that the method based on artificial neural network is effective and practical, and provides a new idea for the location decision of logistics distribution centers.

Research on Location-Allocation Problem of Emergency Logistics Based on Supply Chain Collaboration

The location of relief distribution centers and allocation of relief commodities are two of the most challenging issues in emergency logistics. This paper develops a multi-objective robust stochastic optimization model to determine the optimal location-allocation for emergency logistics problem considering the priority of demand points, the equity level between two demand points and the average removal time and cost for each relief commodity. In our model, not only demand, but also supplies and the state of roads in the post-disaster phase are considered as uncertain parameters. The proposed model simultaneously attempts to minimize the average of the weighted response times and the sum of the expected value and the variance of the total cost in the preparedness and response phase. Considering the global evaluation of two objectives, a compromise programming model is formulated and solved to obtain a non-dominating compromise solution. A case study of our robust stochastic optimization approach for disaster planning for the Great Sichuan Earthquake in China is presented to demonstrate that the proposed model can help in making decisions on both facility location and resource allocation in cases of disaster relief efforts.

A Queuing Model for Stochastic Location¬-inventory Problem with Waiting Cost Considerations

This paper presents a three-level supply chain model which includes single supplier, several distribution centers and sets of retailers. For this purpose, by adopting the queuing approach, a mixed nonlinear integer programming model is formulated. The proposed model follows minimizing the total cost of the system by determining: 1) the number and location of distribution centers between candidated ones; 2) the possibility of allocating each of the retailers to the distribution centers; 3) the amount of retailers demand; and 4) the policy of distribution centers. In the proposed model, the cost of waiting in queue is also considered. In order to make the problem more realistic, we consider uncertain demand and lead-time, which follow Poisson and Exponential distributions, respectively. Hence, we apply continuous-time Markov process approach to obtain the amount of annual ordering, purchase and inventory. Then, the results are used to formulate the location-inventory problem. Finally, the proposed model is solved using GAMS software version 24.1.3.

Simulation of the Optimal Location for a Distribution Center of E-Commerce Operations in São Paulo State

This paper presents a simulation study about location of distribution center seeking to obtain the optimization of the delivery process in e-commerce. Currently, the delivery time of products to consumers is relatively large. The optimal location of a distribution center provides a strategic character of great importance for most of the companies promoting greater competitiveness between them due to shorter delivery times, ensuring greater customer satisfaction. The proposed simulation model is valid to determine the distribution center location (CD). The simulation rounds produced three scenarios with unique characteristics and different locations for the distribution center. The ideal location for a distribution center of e-commerce operations in the State of Sao Paulo is the city of Jau because it has lower total mileage and lower shipping cost. Regarding infrastructure, the city of Jau features more suitable conditions than the other two CDs suggested by simulation rounds, facilitating installation and logistics process.

Planning urban distribution center location with variable restocking demand scenarios: General methodology and testing in a medium-size town

The paper proposes a methodology for the evaluation of an Urban Distribution Center (UDC) location in order to pursue sustainability goals. The methodology integrates indications deriving from public policies (objectives and constraints) and efficiency needs of carriers and retailers. For this reason, two planning levels are integrated into the methodology: an outer level in which a set of feasible UDC locations is defined; an inner level in which, for each location, the carrier behaviour in delivering freight to a set of retailers, characterized by variable restocking demand, is simulated (by means of a vehicle routing problem). The aim of the paper is to support the planning of city logistics measures by means of quantitative evaluations; that could be carried out by public authority to verify sustainability of logistic operations.A laboratory test is implemented in the town of Reggio Calabria (Southern Italy) to assess the effects of a combination of two city logistics measures: a nodal physical measure, such as an UDC, and an equipment measure, specifically Fully Electric Vehicles (FEVs).

Factors determining distribution structure decisions in logistics: a literature review and research agenda

ABSTRACTDistribution structures, as studied in this paper, involve the spatial layout of the freight transport and storage system used to move goods between production and consumption locations. Decisions on this layout are important to companies as they allow them to balance customer service levels and logistics costs. Until now there has been very little descriptive research into the factors that drive decisions about these structures. Moreover, the literature on the topic is scattered across various research streams. In this paper we review and consolidate this literature, with the aim to arrive at a comprehensive list of factors. Three relevant research streams were identified: Supply Chain Management (SCM), Transportation and Geography. The SCM and Transportation literature mostly focus on distribution structure including distribution centre (DC) location selection from a viewpoint of service level and logistics costs factors. The Geography literature focuses on spatial DC location decisions and resulting patterns mostly explained by location factors such as labour and land availability. Our review indicates that the main factors that drive decision-making are “demand level”, “service level”, “product characteristics”, “logistics costs”, “labour and land”, “accessibility” and “contextual factors”. The main trade-off influencing distribution structure selection is “service level” versus “logistics costs”. Together, the research streams provide a rich picture of the factors that drive distribution structure including DC location selection. We conclude with a framework that shows the relative position of these factors. Future work can focus on completing the framework by detailing out the sub factors and empirically testing the direction and strength of relationships. Cooperation between the three research streams will be useful to further extend and operationalize the framework.