Distribution Center Location Research Articles

Solving Multiple Distribution Center Location Allocation Problem Using K-Means Algorithm and Center of Gravity Method Take Jinjiang District of Chengdu as an example

In the logistics system, logistics nodes play an important role. The centre-of-gravity method is one of the most basic methods in solving the problem of single facility location. The thesis first uses the K-means clustering algorithm to divide the demand points into k clusters, and the k value is selected by the elbow method. Then, the k clusters are regarded as a single facility location problem and analyzed by the center of gravity method. In practical applications, the discrete model analysis can be cited to further obtain the optimal solution.

A case-based reasoning approach to solve the vehicle routing problem with time windows and drivers’ experience

In last-mile delivery, on-line retailers deliver goods from local distribution centers to endpoint customers using a fleet of vehicles. This problem is often related to vehicle routing problems with time windows (VRPTWs) in the literature. For an on-line retailer in China, it was found that experienced drivers could often find better routes rather than relying on computerized tools using state-of-the-art algorithms. Therefore, the focus of this paper is to generate routes based on experience. To do so, we propose a methodology based on case base reasoning (CBR). The methodology designs new routes to fulfill orders by retrieving and adapting routes previously performed from a repository named case base. A mechanism is also developed to maintain good quality routes in the case base. The methodology is first tested on problem instances generated using a construction heuristic. Other tests are also performed using real data from an on-line retailer in China. Results show that the CBR methodology designed can effectively generate routes to solve new problems similar to routes previously performed. A comparison to the BoneRoute algorithm show that the solutions obtained with CBR are in average 18.4% longer. However, this result does not take into consideration the time required by the drivers to adapt to a very different route.

Fresh Agricultural Products Cold Chain Location Selection in Context of Big Data

In the domestic fresh market, agricultural products are one of the major consumer goods. The rational optimization of the fresh agricultural products cold chain distribution center location is of great significance in enhancing the efficiency of the entire cold chain logistics. Therefore, in the context of big data, this paper chooses the grey forecasting method with a higher prediction accuracy, applied GM(1,1) prediction model for fresh agricultural products demand forecast under the premise of comparing different forecast methods’ accuracy; Basing on the demand forecast, this paper formulates fresh agricultural products distribution center location model with the minimum total cost objective. In this objective, the thesis considers the cost of cargo damage and the penalty cost of violating the time window in order to this order to improve customer satisfaction and loyalty, which is considered by a few researchers. Finally, a specific case study is designed for Q enterprise to solve the optimal the result and test the validity of the model algorithm.

A unified typology of urban logistics spaces as interfaces for freight transport

ABSTRACT Performant planning and integration of Urban Logistics Spaces (ULS) in logistics networks are key to optimising the circulation of goods in cities. During the last three decades, several documents on ULS have been published, most of them proposing different methodologies for the design and location of Urban Distribution Centres (UDC). Until now, there is no international consensus on the definitions of ULS nor any apparent uniform classification. In this paper, a systematic literature review on ULS is presented. The objective is to identify their characteristics, criteria, and particularities to propose a unified taxonomy. By searching the papers from 1990 to 2019, a total of 216 publications were selected and classified. This review finds predominant orientation towards UDCs, considering high rotation storage, with specialised ULS being less studied. A general typology of ULS is proposed, adaptive to any urban context. Lines for future research are proposed to help both practitioners and scholars to provide insights into all types of ULS.

A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning

Natural disasters such as earthquakes always threaten human societies. Therefore, preparedness planning seems to be necessary to reduce casualties and accelerate relief efforts. Decisions in the preparedness phase include determining the optimal location for distribution centers and suppliers, allocating them to affected areas, and determining the amount of inventory of distribution centers. In this research, a robust simulation-optimization approach is presented for the planning in the preparedness phase. Although the demand for relief commodities has been considered as a given parameters. Due to our best knowledge there is no research in which the demand for relief commodities including drinking water, food, blood, medicine, tents and non-drinking water, during the disaster is related to the interactive relations of critical infrastructures of the city affected by the earthquake, and the power of the earthquake. Most of the unplanned occasions are occurred when co-incidences of probabilities are happen together. When a critical urban infrastructure fails due to earthquake it may cause some difficulties in the other infrastructures. Co-incidences may cause some horrible tragedy. First, the critical infrastructures of the city affected by the earthquake are identified and the interactions of these infrastructures are simulated. Considering the interactions of critical infrastructures in a city, various scenarios for earthquake are designed and tested through simulation approach in order to estimate the mean and variance of demand for relief commodities. The stochastic demand behavior which is the output of the simulation approach is assumed as a stochastic parameter of a mathematical model for multi-period location–allocation–Inventory problem. Robust optimization approach is used to deal with uncertainty. The mathematical model determines the location of distribution centers and suppliers, and how they are allocated to the affected areas. The proposed mathematical model is solved using a customized genetic algorithm for a case study in Tehran.

Prioritizing distribution centers in humanitarian logistics using type-2 fuzzy MCDM approach

PurposeLocating disaster response centers is one of the key elements of efficient relief operations. The location and infrastructure of the candidate facilities usually conform to the required criteria at different levels. This study aims to identify the criteria for the main and local distribution center location problem separately and prioritize each candidate distribution center using a hybrid multiple criteria decision-making approach.Design/methodology/approachThe proposed model incorporates analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) under interval type-2 fuzzy sets (IT2FSs) to overcome the uncertainty of experts` judgments and expressions in the evaluations of candidate distribution centers. In the proposed approach, weights of the criteria are determined using type-2 fuzzy AHP and the candidate distribution centers are prioritized using type-2 fuzzy TOPSIS.FindingsTransportation, cost, infrastructure and security are determined as the main criteria for the main distribution center location criteria. Cost, warehouse facilities and security are the main criteria for local distribution center location selection. Prioritization enables decision-makers to assess each alternative accordingly to be able to select the best locations/facilities for efficient disaster response operations.Originality/valueThis study proposes new multi-criteria decision support models for prioritizing disaster response distribution centers. IT2FSs are used to be able to reflect both the complexity and vagueness of disaster environment and expert opinions. Different support models are suggested for main and local distribution centers considering their different missions. The proposed methodology is applied in Istanbul city, Turkey, where a high-magnitude earthquake is expected.

A Location-Routing Model for Assessment of the Injured People and Relief Distribution under Uncertainty

Throughout history, nature has exposed humans to destructive phenomena such as earthquakes, floods, droughts, tornadoes, volcanic eruptions, and tropical and marine storms. The large scale of damages and casualties caused by natural disasters around the world has led to extensive applied research in the field of preparation and development of a comprehensive system for disaster management to minimize the resulting casualties and financial damages. Based on this motivation and challenges to the field, this research designs an integrated relief chain to optimize simultaneously the preparedness and response phases of disaster management. Decisions to improve the supply chain include locating distribution centers of relief supplies; the amount of inventory stored in facilities in pre-disaster phase, locating temporary care centers and transportation points of the injured, how to allocate relief services to the affected areas, and routing of the vehicles used to distribute relief supplies and evacuate the injured. The results show that decreasing the capacity of distribution centers increases the amount of shortage of supplies and increasing the capacity of these centers reduces the amount of shortage of supplies.

Multiattribute decision method for comprehensive logistics distribution center location selection based on 2-dimensional linguistic information

The comprehensive logistics distribution center location selection (CLDCLS) problem is a multiattribute group decision-making (MAGDM) problem in which multiple commodity preference weights are considered. To better describe the preference information and expert evaluation information, this paper utilizes 2-dimensional linguistic (2DL) information to express the preference information of various commodities and the expert evaluation, which can represent not only the evaluation information of experts but also the reliability of the evaluation information. Additionally, for solving the CLDCLS problem, this paper puts forward improved operational rules a score function, a distance formula and a correlation coefficient measure. Based on the 2DL information and the improved operational rules, we propose a 2-dimensional linguistic similarity-degree-based clustering analysis method, the 2-dimensional linguistic partitioned Maclaurin symmetric mean (2DLPMSM) operator, and the 2-dimensional linguistic weighted partitioned Maclaurin symmetric mean (2DLWPMSM) operator. The corresponding properties and special cases are demonstrated. By using these proposed methods, this paper constructs a MAGDM solution framework for the CLDCLS problem. A practical case of the CLDCLS problem is presented to demonstrate the effectiveness, rationality, robustness and superior performance of the proposed method.

Analytic Hierarchy Process Modelling of Location Competitiveness for a Regional Logistics Distribution Center Serving Northeast Asia

Purpose - As the global product network expands through both internationalization and diversification of the multimodal transportation system, corporate strategies have shifted to emphasize the importance of a high value-added international logistics system. To guide policies and strategies to attract relevant industries, this study aims to analyze the location competitiveness of regional logistics distribution center to serve Northeast Asia. Design/methodology - Multi-criteria techniques are considered to offer a promising framework for evaluating decision-making factors. This paper employed an analytic hierarchy process to analyze the hierarchal structure of determinants for selecting the location of a regional logistics distribution center. Adopting both qualitative and quantitative evaluations, this study suggest political implications for a regional logistics distribution center development, such as the direction of political support, service differentiation and infrastructure development. Findings - This study developed a location competitiveness evaluation model, based on the case study of the major port-cities in Northeast Asia. Evaluation model incorporates five factors underpinning 17 components extracted using factor analysis. The results revealed that the logistics factor is the most significant factor for evaluating the competitiveness of a regional logistics distribution center. The remaining factors were market, costs, and services environment. Comparing qualitative and quantitative evaluations, results provide useful insights for a regional logistics distribution center development in Northeast Asia. Originality/value - This study revealed differences between qualitative and quantitative evaluations. The finding implies that prior works on evaluation models of competitiveness has not successfully measured the gap between quantitative data and expert’ evaluations. To overcome this limitation, this paper considered both actual data such as actual distance, cost, the number of companies located, and expert opinions.

Two-stage mean-risk stochastic mixed integer optimization model for location-allocation problems under uncertain environment

The problem of the optimal location-allocation of processing factory and distribution center for supply chain networks under uncertain transportation cost and customer demand are studied. We establish a two-stage mean-risk stochastic 0-1 mixed integer optimization model, by considering the uncertainty and the risk measure of the supply chain. Given the complexity of the model this paper proposes a modified hybrid binary particle swarm optimization algorithm (MHB-PSO) to solve the resulting model, yielding the optimal location and maximal expected return of the supply chain simultaneously. A case study of a bread supply chain in Shanghai is then presented to investigate the specific influence of uncertainties on the food factory and distribution center location. Moreover, we compare the MHB-PSO with hybrid particle swarm optimization algorithm and hybrid genetic algorithm, to validate the proposed algorithm based on the computational time and the convergence rate.

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

Multi-objective model for the location of distribution centres with cost evaluation, road access and insecurity

This paper presents an alternative to the location of distribution centres considering a multi-objective model minimising insecurity cost and road access for a case study in the City of Barranquilla (Colombia). For the solution of the model, 15 different scenarios of variation of the importance of the proposed objectives are considered. As a basis for the design of the modelling, the characterisation and classification of the variables with the highest incidence in the location of distribution centres or warehouses are posed. It is possible to demonstrate the impact of the evaluation and analysis of the mathematical model for the location of this type of facility.

An improved particle swarm optimization algorithm in selection of e-commerce distribution center

Under the influence of COVID-19, the efficiency of e-commerce distribution in public health emergencies has become the key to ensuring people’s normal lives. With the development of e-commerce, the location of distribution center is becoming more and more important, which is related to the healthy and long-term development of e-commerce. However, the traditional location efficiency is low, which cannot play an immediate role in the development of e-commerce. This paper combines weiszfeld algorithm, improves particle swarm optimization algorithm, improves the operation efficiency and location accuracy, and finally gets a relatively satisfactory result. Results data analysis shows that the improvement and integration of the algorithm is reasonable and effective. This method has important reference value for e-commerce location.

Two-Stage Optimization Model of Agricultural Product Distribution in Remote Rural Areas

Due to the lack of systematically optimized logistics networks in many remote rural areas, the online sales of agricultural products in these areas have the disadvantages of high cost, high damage and slow speed. To address these logistic problems, this paper proposes a two-stage layout optimization model of agricultural product joint distribution centres based on the geographical features of remote rural areas. The number and location of distribution centres are selected in two stages to optimize the logistics network. Chengkou County, located in Chongqing city, China, has been selected as the study area. In the first stage, AP clustering is carried out using the distance between the logistics nodes of villages and towns, and the transit station of the source of agricultural products is obtained using the correlation between the nodes, which are regarded as alternative locations for the second-level logistics nodes. In the second stage, a joint distribution centre optimization model with the lowest cost is constructed. A fruit fly optimization algorithm is used to select the optimal locations from the alternative locations as the second-level logistics nodes and obtain the specific delivery path. Optimizing the logistics network of remote rural areas can reduce the logistics costs of agricultural products in these areas, promote online sales of agricultural products, and provide the government and logistics companies with theoretical references to open up new markets in remote areas.

Joint Distribution Center Location Problem for Restaurant Industry Based on Improved K-Means Algorithm With Penalty

The location of joint distribution center (JDC) is of great importance in urban freight system. This work aims to minimize the total cost, including the fixed cost, the transportation cost, and the penalty cost for the missed deliveries, considering the joint distribution willingness (JDW) of restaurant and the coverage radius of JDC. The integer programming model is formulated to select the optimal location of JDC and opens no more than k-JDCs. To solve the problem, an improved K-means (I-K-means) algorithm combining local search with penalty is proposed. The delivery problems obtained by freight survey from 114 restaurants in Beijing, China are taken as a case study. The formulation in this paper can reduce the cost of deliveries in restaurant industry, decrease the number of freight vehicles on road, and promote the joint distribution mode in urban freight system.

Distribution center location selection using a novel multi criteria decision-making approach under interval neutrosophic complex sets

Distribution center location selection using a novel multi criteria decision-making approach under interval neutrosophic complex sets

Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm

Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm

A New Hybrid Algorithm for Cold Chain Logistics Distribution Center Location Problem

According to the perishable characteristics of refrigerated food and the objective of minimizing the total cost, the mathematical optimization model of cold chain logistics distribution center location problem is established by introducing such constraints as the freshness and time window. In order to solve the problems of slow convergence and easy to fall into local optimal solution in the process of the traditional wolf colony optimization, an immune wolf colony hybrid algorithm is proposed to solve the location problem of distribution center. In this hybrid algorithm, the idea of vaccination of immune algorithm is introduced into the wolf colony algorithm. By adjusting the antibody concentration and selecting immune operator, the diversity of the wolf colony algorithm is improved, and then the search space of the solution is expanded; the convergence speed and solution accuracy of the wolf colony algorithm are improved by using immune memory cells and immune vaccine. The simulation results show that the immune wolf colony algorithm can quickly converge to the global optimal solution and optimize the location model of logistics distribution center. The algorithm has good feasibility and robustness.

Coordination and Profit Optimization by Producer-Distributor System of Agricultural Products in Bangladesh

This study, presents three different mathematical models: Producer, Distributor and Coordination modelwhich negotiate with a Producer-Distributor system for producing and distributing ofagricultural products in Bangladesh. In this paper, we investigated supply chain network (SCN) are two distinct freelance supply organizations. SCN management has the difficulties for the disconnected and freelance economic people. Further, fast technological changes and high fight build SCN a lot of complicated. The problem of locating distribution centers (DCs) is one among the foremost necessary problems in design of SCN. Current study, SCN was modeled using a formulation in mixed integer linear programming (MILP) problem, in which the facilities are coordinated by mutually sharing information with each other between producer and wholesaler. We think, this research presents a real life coordination optimization problem. The formulated MILP model is solved by using a mathematical programming language (AMPL) and results obtained by appropriate solver MINOS.

Determination of the optimal distribution centre location with gravity location model

Distribution cost for an animal feed company in Deli Serdang is relatively difficult to reduce because the location of the distribution centre is not strategic and far from several distribution locations. The purpose of this research is to determine the optimal distribution centre location to reduce distribution costs. In determining the proposed distribution centre location, Gravity Location Model is used. The research results are the new coordinates as the proposed distribution centre location, namely (98.888504; 3.419406) and the total distribution costs of initial distribution centre location are Rp 7.135.828, while the total distribution cost of proposed distribution centre location is Rp 6.965.143. The results obtained show that the total distribution costs can be reduced by 2.39% per day.