Distribution Center Location Research Articles

Modified K-Means Clustering for Demand-Weighted Locations: A Thailand’s Convenience Store Franchise - Case Study

This research applies and modifies K-means clustering analysis from Data Mining to solving the location problem. First, a case study of Thailand’s convenience store franchise in locating distribution centers (DCs) is conducted. Then, the final centroids are served at suggested DC locations. Besides the typical distance, Euclidean, used in K-means, Manhattan, and Chebyshev, is also experimented with. Moreover, due to the stores’ different demands, a modification of the centroid calculation is needed to reflect the center-of-gravity effects. For the proposed centroid calculation, the above three distance metrics incorporating the demands as weights give rise to another three approaches and are thus named Weighted Euclidean, Weighted Manhattan, and Weighted Chebyshev, respectively. Besides the optimal locations, the effectiveness of these six clustering approaches is measured by the expected total distribution cost from DCs to their served stores and the expected Davies–Bouldin index (DBI). Concurrently, the efficiency is measured by the expected number of iterations to the final clusters. All these six clustering approaches are then implemented in the case study of locating eight DCs to distribute to 260 convenience stores in Eastern Thailand. The results show that though all approaches yield locations in close proximity, the Weighted Chebyshev is the most effective one having both the lowest expected distribution cost and lowest expected DBI. In contrast, Euclidean is the most efficient approach, with the lowest expected number of iterations to the final clusters, followed by Weighted Chebyshev. Therefore, the DC locations from Weighted Chebyshev could, ultimately, be chosen for this Thailand’s convenience store franchise.

Last-Mile Drone Delivery: Past, Present, and Future

Sustainable green products and services garner more attention from companies and enterprises that aim to succeed and grow in highly competitive markets by imposing less harms on the environment and ecosystems. Last-mile delivery from local distribution centers to customers plays an essential role in the retail business. Retail companies are leaning towards implementing green, efficient transportation methods, such as drones, in their last-mile delivery operations to conserve ecosystems. Accordingly, researchers have documented numerous research findings on last-mile drone delivery in recent years. This literature review selected a collection of articles mostly from 2011 to 2022 and reviewed them in terms of key technical challenges, such as routing, cargo distribution optimization, battery management, data communication, and environmental protection. These challenges are interrelated in a sense of achieving eco-friendly, efficient, lean, last-mile drone delivery. The selection of these technical challenges is based on the top challenges discussed in the literature.

Two-Stage Multi-Objective Stochastic Model on Patient Transfer and Relief Distribution in Lockdown Area of COVID-19.

The outbreak of an epidemic disease may cause a large number of infections and a slightly higher death rate. In response to epidemic disease, both patient transfer and relief distribution are significant to reduce corresponding damage. This study proposes a two-stage multi-objective stochastic model (TMS-PTRD) considering pre-pandemic preparedness measures and post-pandemic relief operations. The proposed model considers the following four objectives: the total number of untreated infected patients, the total transfer time, the overall cost, and the equity distribution of relief supplies. Before an outbreak, the locations of temporary relief distribution centers (TRDCs) and the inventory levels of established TRDCs should be determined. After an outbreak, the locations of temporary hospitals (THs), the locations of designated hospitals (DHs), the transfer plans for patients, and the relief distribution should be determined. To solve the TMS-PTRD model, we address an improved preference-inspired co-evolutionary algorithm named the PICEA-g-AKNN algorithm, which is embedded with a novel similarity distance and three different tailored evolutionary strategies. A real-world case study of Hunan of China and 18 test instances are randomly generated to evaluate the TMS-PTRD model. The finding shows that the PICEA-g-AKNN algorithm is better than some most widely used multi-objective algorithms.

Hub and Distribution Center Location for a Third-Party Logistics service Provider: A Case Study at Tehran

The rapid growth of the transportation industry and the advance of the e-commerce have opened opportunities for boom delivery services companies. Although, intense competition between shipping companies has obliged many to amend their shipment networks. In this setting, we aim to provide high-quality shipping services for a third-party logistics company in Iran. Currently, in the selected company, one hub is in the west part of Tehran that gives services to all customers. There are some difficulties in shipping services to the eastern part of Tehran city. Therefore, in this paper, we find the location of another hub center to streamline the shipping process. Moreover, to promote our service levels, some Distribution Centers (DCs) are going to be set up. Due to the lack of shipment data like pick up and delivery data, we tried to find the locations of the hub and the DCs based on Tehran population's data. We clustered 122 regions of Tehran based on population, economic index, accessibility to the internet, and the number of business units. Since some remote regions (which are not in the same vicinity) belong to the same cluster, we defined a relative distance criterion to avoid making remote regions in the same cluster. In each cluster, a fixed or movable DC can be set up to service the regions of its cluster. Moreover, to find the best candidate locations for the hub, each zone of Tehran was assessed based on some criteria like land cost, accessibility to the highways, and distance to its nearest bus terminal. Based on these criteria, some zones have been dominated by others, and the remains were considered as candidate locations in a hub location model. By considering the DCs as spokes in a hub and spoke model, the optimal location for the eastern hub establishment was determined.

ВИЗНАЧЕННЯ ОПТИМАЛЬНИХ КООРДИНАТ РОЗМІЩЕННЯ РОЗПОДІЛЬЧИХ ЦЕНТРІВ НА ВІДРІЗКУ ДОРОГИ

One of the optimization tasks in logistics chains is determining the optimal number and location of distribution centers, which allows companies to reduce delivery costs, shorten delivery time, and ensure more reliable and stable delivery of goods to consumers. This, in turn, increases customer satisfaction and contributes to the formation of high customer loyalty to the business. Additionally, optimal placement of distribution centers helps reduce road congestion and decrease emissions of harmful substances, promoting sustainable development and environmental preservation. The study analyzed the peculiarities of the methods used in sales logistics for determining the optimal location of distribution centers. The limitations of existing methods for determining the optimal coordinates for locating distribution centers on a road segment were substantiated. The validity of the constraint that the coordinates of distribution centers should coincide with the coordinates of certain customers of the distribution centers was confirmed. A suitable method for solving practical problems was proposed for finding optimal solutions regarding the service area and location of two or more distribution centers on a road segment. The proposed method assumes that the coordinates of the distribution centers coincide with the coordinates of individual consumers. The functionality of the method was demonstrated through a case study. The method was tested on thirty-three examples where eleven customers of distribution centers were located on a 100 km road segment. Each example contained unique values of indicators such as "customer's cargo turnover for a period" and "freight transportation tariff." In all cases, the results of the proposed method in the study matched 100% with the results of the method of complete recalculation (MCR). The method presented in the study for determining the optimal coordinates for locating distribution centers on a road segment is considered reliable. Further research should involve testing the method on examples with a larger number of customers and a larger number of distribution centers.

Exploring the unnoticed holes: Implementation challenges of social safety-nets in coastal Bangladesh

Like other coastal countries, Bangladesh is also facing the numerous adversities raised from natural disasters and climate change hazards. The present study examines the implementation challenges in disaster affected areas and identifies the possible ways to make the social safety-nets (SSNs) more effective in coastal Bangladesh. Using a qualitative approach, data were collected from Raruli and Vobanipur villages at Paikgaccha Upazilla in Khulna district through Focus Group Discussions (FGDs), Key Informant Interviews (KII) and observation. This study found that Bangladesh provides these (SSNs; e.g. VGF, GR, TR, CoDRH, FFW/FW and EGPP) in the coastal areas during the religious festivals and normal and emergency period as well to alleviate poverty, decrease food crisis, reduce disaster risks and climate vulnerabilities. Additionally, the traditional 'top down' approach for selecting beneficiaries and the existing distribution process creates obstacles to find out the actual targeted people. Findings show there are some challenges (e.g. faulty beneficiary targeting process, complex selection criteria, excessive political interference, mal-functioning of local committee, location of the support distribution center etc.) especially during the implementation phases. This study argues that as a good initiative of the government, the local people do not get the utmost benefits from the SSNPs and there is discrimination in the community in selecting beneficiaries. The study suggests that, to achieve the ultimate goals of SSNPs, such as reducing corruption and political interference, the local beneficiary selection committee should function properly.

Site-selection method of agricultural products logistics distribution centre based on blockchain

In order to overcome the problems of low on-time delivery rate and high distribution cost existing in the existing location methods of agricultural products logistics distribution centre, this paper proposes a new location method of agricultural products logistics distribution centre based on blockchain. Based on the analysis of the basic problems affecting the location of agricultural products logistics distribution centre, combined with the blockchain technology, the location model of agricultural products logistics distribution centre based on input-output ratio was constructed. Combining the idea of mountain climbing algorithm and particle swarm optimisation algorithm, the hybrid particle algorithm is used to solve the location model of agricultural products' logistics distribution centre, and the optimal location scheme is obtained. The experimental results show that the proposed method can effectively improve the on-time delivery rate, customer satisfaction, and reduce the logistics distribution costs. The maximum on-time delivery rate is 97.4%.

Design of logistics distribution center location method based on particle swarm optimization

At present, the traditional method of logistics distribution center location is mainly achieved through dynamic planning of location. When the scale of the problem is large, it is easy to produce large deviation, and the optimization effect is not ideal. For this reason, a method design of logistics distribution center location based on particle swarm optimization is proposed. This method effectively improves the learning factor and location formula of particle swarm optimization, and establishes a mathematical model of logistics distribution center location. The experimental results show that the proposed method has higher convergence and better search ability.

Application of cloud computing and big data in three-stage dynamic modeling of disaster relief logistics and wounded transportation: a case study.

Collecting and sharing information about affected areas is an important activity for optimal decision-making in relief processes. Defects such as over-sending some items to affected areas and mistakes in transferring injured people to medical centers in accidents are due to improper management of this information. Because cloud computing as a processing and storage platform for big data is independent of the device and location and can also perform high-speed processing, its use in disasters has been highly regarded by researchers. In this environment, a three-stage dynamic procedure for evacuation operations and logistics issues is presented. The first stage of the proposed model is image processing and tweet mining in a cloud center in order to determine the disaster parameters. In stage II, a mixed-integer multi-commodity model is presented for the relief commodity delivery, wounded people transportation with capacity constraints, and locating of the possible on-site clinics and local distribution centers near disaster areas. In stage III, by using a system of equations, detailed vehicle load/unload instructions are obtained. Finally, the effectiveness of the proposed model on the data of an earthquake disaster in Iran is investigated. The results of comparing the proposed approach with a two-stage algorithm show that the total number of unsatisfied demand for all types of commodities in the proposed approach was better than the other. Also, the number of survivors in the three-stage model is significantly higher than in the two-stage one. The better performance of the proposed algorithm is due to the fact that online data is continuously available and that decisions such as sending relief items and dispatching are made more effectively.

Конфигурирование терминально-складской инфраструктуры транспортного узла на основе развития метода экономико-географического разграничения грузопотоков

Purpose: To formulate proposals for configuring terminal-warehouse infrastructure for the placement of storage facilities inside a transport hub. Methods: Basing on the analysis of pub-lications in the trend of the research data, optimization geometric Euclidean model of freight process, embodied on the territory of a transport hub, was applied to find optimal cargo flow distribution by dividing the flow into parts representing “influence areas”. Results: optimiza-tion algorithm for freight process was developed in the article on the basis of classical integral calculus methods, the algorithm allows to discover those ones among many distributive center possible locations which allows in view of restriction complex to determine “influence area” of distributive center with the largest area in comparison with the initial area. Practical signifi-cance: Economic effect was obtained from base rental rate reduction that is due to possible change of logistic freight distribution center location outside dense urban development territo-ry where base rental rate is higher.

Time-Dependent Green Location-Routing Problem under Carbon Cap-and-Trade Policy

Considering carbon emissions in location-routing decisions can contribute to the promotion of sustainable development in the field of transportation. This paper investigates the time-dependent green location-routing problem (TDGLRP) incorporating carbon emissions. The TDGLRP model is formulated as an optimization problem with the goal of minimizing the total cost under the carbon cap-and-trade emission policy. The total cost considered consists of that caused by the construction of distribution centers, the fuel consumption of vehicles, carbon emissions under the carbon cap-and-trade policy, vehicle use, labor, and dispatch of vehicles. A two-stage hybrid heuristic algorithm that integrates the customized greedy algorithm and the ant colony algorithm is proposed to solve this problem. The simulation results illustrate that the proposed model together with the intelligent optimization algorithm could calculate the location of distribution centers and vehicle routing efficiently by alleviating traffic congestion and reducing the total logistics cost and carbon emissions.

An Improved Sparrow Search Algorithm for Location Optimization of Logistics Distribution Centers

In modern society where business development is booming, it is of great importance to investigate location optimization of logistics distribution centers. In order to achieve greater efficiency of distribution system, save the distribution costs and the construction cost of logistics distribution center, good point set method and decreasing nonlinear inertia weight are proposed to improve sparrow search algorithm (SSA) to solve the mathematical model for logistics distribution center location. First, in order to prevent SSA falling in local optimal, while improving the convergence speed and efficiency of SSA, good point set method and decreasing nonlinear inertia weight are applied to improve the SSA. The test results of eight benchmark functions show that the fitness value and convergence speed of the proposed ISSA are smaller and faster. Second, compared with SSA, WOSA and PSO, the ISSA has the lowest total cost for solving the mathematical model of logistics distribution center.

A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability

Logistics center location optimization is one of the core issues in the study of logistics networks. A sensible logistics center distribution can improve the efficiency of goods transportation and the operation efficiency of logistics enterprises. In this paper, we study the problem of optimizing the location of logistics distribution centers in logistics networks with the objective of minimizing the operating costs of logistics distribution centers. Then, we presents a distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability. Firstly, the future goods quantity prediction data of each node is obtained through the grey-residual Markov chain prediction model. Then, a logistics center location optimization model with three node expansion mechanisms is developed with the objective of minimizing the objective function. Finally, by using the forecast data of goods demand obtained by the grey-residual Markov chain prediction method, we conducted simulation experiments on the cost-minimizing logistics center location optimization model under three different node expansion methods. The corresponding simulation results are obtained by particle swarm optimization algorithm to prove the effectiveness of the model.

Designing a sustainable bioethanol supply chain network: A combination of machine learning and meta-heuristic algorithms

Bioethanol demands have increased during the last decade due to unexpected events worldwide. It is among the renewable energy sources that are utilized to replace fossil-fuel-based energy. Designing an efficient, Sustainable Bioethanol Supply Chain Network (SBSCN) is a critical task for the government and communities to manage the bioethanol demands appropriately. The contribution of this study is threefold. First, this study project the demand using three popular machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ensemble Learning (Adaboost) methods, to find the best one to be utilized as an input to the proposed mathematical to find optimal values for strategic, planning, and tactical decision variables. Second, the unemployment rate is considered an important parameter of the model to maximize the social effects. Third, since the proposed model of this study is NP-hard, to solve the problem, the CPLEX solver is applied for small size and two meta-heuristic algorithms, including Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Invasive Weed Optimization (MOIWO) are considered for medium and large size problems to find Pareto optimal solutions. Due to the sensitivity of two meta-heuristics algorithms, the Taguchi method, such as Max Spread (MS) and Mean Ideal Distance (MID), is utilized to control the parameters of those two applied algorithms. Since North Dakota (ND) is among the states with the most potential to produce bioethanol due to its vast land area, including marginal cropland and Conservation Research Program (CRP), the proposed model has been evaluated and validated based on the ND case study. The results show that the MOIWO algorithm outperforms NSGA-II based on the proposed model and the case study of this paper. Also, this algorithm is more reliable in terms of solution quality to tackle the problem. Finally, some research directions are discussed for future studies, and managerial insights are provided. • Bioethanol consumption experienced an increasing due to unexpected worldwide event. • Utilizing machine learning approaches to estimate the bioethanol demand in N.D. • Designing sustainable bioethanol supply chain by two meta-heuristic algorithms. • Optimal locations of biorefinery, pre-processing, distribution, and demand centers. • Gradual replacement of liquid fossil fuels with bioethanol to reduce air pollutions.

Rafflesia Optimization Algorithm Applied in the Logistics Distribution Centers Location Problem

<p>Intelligent evolutionary algorithm is an important method to solve optimization problems. Most of their inspiration comes from the laws of nature and biology. This paper proposes a new intelligent evolutionary algorithm based on the life habits of Rafflesia, which is called Rafflesia Optimization Algorithm. It mainly consists of three stages: attracting insects, swallowing insects, and spreading seeds. In the first stage, the ROA algorithm performs the local search to find the optimal solution. In the second stage, it improves execution efficiency and solution accuracy by reducing the number of individuals. In the third stage, it performs the global search to jump out of the local optimal position. In the experimental part, this paper uses numerical functions (the CEC2013 benchmark function set) and practical application problems (the logistics distribution centers location problem) to test the performance of the ROA algorithm, and compares it with seven meta-heuristics algorithms. The experimental results prove the effectiveness and practicability of the ROA algorithm.</p> <p> </p>

Research on Logistics Distribution Center Location Based on Hybrid Beetle Antennae Search and Rain Algorithm.

The location of logistics distribution centers is a crucial issue in modern logistics distribution systems. In order to obtain a more reasonable solution, an effective optimization algorithm is essential. This paper proposes a new hybrid method, named the beetle antennae search-rain algorithm (BRA), for the problem of logistics distribution centers' location. The innovation of the BRA is embodied in three aspects. Firstly, the beetle antennae search (BAS) algorithm is embedded into the rain algorithm (RA); thus, the BAS is improved from an individual search to a swarm intelligent search and the global search ability is improved. Secondly, the search direction strategy of the BAS algorithm is incorporated into the RA, which can improve response speed while ensuring optimization performance. Finally, the search precision is improved by the mechanism of eliminating the inferior solution and generating a new solution. The BRA is tested on 10 benchmark functions and applied to solve the logistics distribution centers' location problem. The performance of the BRA is compared to that of several classical heuristics by using relevant evaluation indexes and dynamic optimization convergence graphs. Experimental results show that the BRA outperforms the BAS algorithm, the RA and some other classic heuristics. It is also revealed that the BRA is an effective and competitive algorithm for logistics distribution centers' location.

Determining optimal locations for blood distribution centers.

Blood banks have to be thoughtful about supply chain decisions to effectively satisfy the blood product demand of hospitals. These decisions include the number and locations of distribution centers (DC), as this has a strong impact on both transportation cost and the ability to deliver emergency orders in time. We propose a mixed-integer linear programming approach to find optimal DC locations for supplying individual hospitals. The model maximizes the number of hospitals reachable from a DC within a given time-limit, and minimizes transportation cost. The minimal amount of data required is a set of hospital locations. The model can be further attuned to the user's needs by adding various model extensions. The model's use is demonstrated by two case studies, considering the blood banks of the Netherlands and Finland. For both case studies re-locating the DCs would result in a reduction of transportation cost of about 10% without affecting the reliability of delivery. In addition, to save facility exploitation costs, the number of DCs may be reduced in both countries while maintaining the reliability of delivery. The model was also shown to be robust against changes in hospital ordering behavior. We demonstrated the general usability and added value of the model by successfully optimizing the blood supply chains of the Netherlands and Finland, which differ substantially. Nonetheless, in both countries potential savings in both transportation and facility exploitation cost could be shown. The model code is open source and freely accessible online.

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the “demand-driven” properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.

Correlation between extreme/ unusual ozone events and corona effect from electric power transmission & distribution grids. Assessing the health and environmental impact of high-voltage direct current (HVDC) grid on ozone ground levels in Sao Paulo metropolitan area, Brazil.

• BACKGROUND AND AIM 1- Ozone ground levels pose a risk for public health 2- The increasing ozone levels measured in parts of the metropolitan area of Sao Paulo have brought the attention of researchers. Proposed mechanisms do not provide a clear outcome for establishing root causes, and for clarifying sources of ozone. Spatial distribution of ozone also depends on wind patterns. 3- As an alternate hypothesis for explaining unusual high levels of ozone, the corona discharge from electric transmission grid, known as corona effect, can play a significative role in the absence of, or in coincidence with other concomitant factors, to explain ozone (O3) and nitric oxide (NO) unusual levels. Negative coronas emits more ozone than Positive coronas. 4- The Environmental Protection Agency- EPA, found in a report of 1973 (EPA-650/4-73-003), following a research carried out in areas of high concentration of transmission lines that "under minimal wind conditions, such transmission line concentrations can produce sizeable local ozone levels.” 5- Research applies geospatial and statistical analysis with the aim of better understanding correlations between exposures and ozone levels. • METHODS Geographic Information System (GIS)-based systems with average grid cell edge length of 10 km minimum. Global InMAP (Intervention Model for Air Pollution) with a variable resolution grid (4 km horizontal grid cell widths) in combination with GEOS- Chem simulations. Data analysis by machine-learning model. • RESULTS Applying Predictive Machine Learning algorithm as regression models, research found that coronas could generate significant ozone levels downwind of the power line. • CONCLUSIONS Evidence of correlation between unusual ozone measurements and location of key power electric grid transmission lines and distribution centers with potential for corona effect are found in Sao Paulo state and Sao Paulo city metropolitan area. • KEYWORDS Corona effect, ozone, electric grids, spatial simulation, wind patterns, air pollution

Designing an efficient vaccine supply chain network using a two-phase optimization approach: a case study of COVID-19 vaccine

The upsurge of the COVID-19 pandemic and its consequences on the world’s population has led to the accelerated production of vaccines across the world. The next challenge in the fight against COVID-19 is to distribute those vaccines to the right person in an optimized way by establishing an efficient vaccine supply network. Consequently, this paper studies an integrated two-phase planning framework for the vaccine distribution network. In the first phase, the population target is classified into several groups to determine their priority for vaccination using a multiple attribute decision making (MADM) technique. The second phase uses a mathematical model to decide on the location of distribution centers, inventory policies, and routing decisions to minimize the total procurement, inventory, and distribution costs. Since the proposed model falls into the category of NP-hard problems, a metaheuristic algorithm entitled population-based simulated annealing (PBSA) is developed to solve the presented model. Besides, the improved grey wolf optimizer (I-GWO) is customized and used as another solution method. A few generated datasets are utilized to evaluate and validate the performance of these solution methods. In addition, this methodology is implemented in a case study of the COVID-19 vaccine supply chain in Iran to demonstrate its practicability.