Logistics Distribution Center Location Research Articles

Location strategy for logistics distribution centers utilizing improved whale optimization algorithm

Abstract An enhanced whale optimization algorithm is introduced to address the challenges associated with the logistics distribution center location problem, aiming to overcome the traditional heuristic algorithm’s susceptibility to local optima and enhance the overall efficiency of the logistics system. This improvement involves the integration of stochastic sinusoidal inertia weights and a variational strategy to augment the global search capability and convergence accuracy of the conventional algorithm. The results of experimental simulations indicate that the upgraded whale algorithm exhibits superior computational performance compared to other heuristic algorithms. Furthermore, it demonstrates the ability to effectively compute optimal distribution center locations, thereby enhancing the delivery efficiency of the logistics distribution system.

A multi-objective optimization model for the location of cold chain logistics distribution center based on trust domain optimization algorithm

Cold chain transportation refers to a logistics method that transports fresh, perishable, and perishable items from the place of production, processing or warehouse to the place of consumption under certain temperature conditions. As consumers have higher and higher requirements for food safety and quality, the cold chain logistics industry has also developed rapidly. However, the high cost of cold chain logistics, the difficulty of technology, the difficulty of ensuring service quality and other problems have also arisen, and how to optimize cold chain transportation has become a hot issue in the cold chain logistics industry. Cold chain transportation optimization refers to the premise of ensuring the quality and safety of goods, through reasonable transportation paths, transportation methods, temperature control and other measures, to minimize the cost of cold chain transportation, improve efficiency and service quality. In order to achieve the optimization of cold chain transportation, it is necessary to establish a reliable cold chain logistics distribution center to ensure the safety and quality of goods during transportation. In order to solve the problem of location selection of cold chain logistics distribution center, this paper first defines the service reliability and calculation method of cold chain distribution center. Then, the location model of the cold chain logistics distribution center and the trust domain optimization algorithm model were established. The model aims to minimize the total cost, while considering multiple factors such as transportation distance, temperature control, facility construction, etc., to ensure the reliability and operational efficiency of the cold chain logistics distribution center. Finally, this paper introduces an example for calculation to prove the feasibility and universality of the model in the location problem of cold chain logistics distribution center. Through the calculation of examples, it can be seen that the model can effectively reduce the cost of cold chain transportation and improve the service quality and efficiency of cold chain logistics distribution center. In summary, this paper proposes a reliable location model and trust domain optimization algorithm model for cold chain logistics distribution centers to solve the challenges faced by the cold chain logistics industry. This model can provide guidance for cold chain logistics companies to gain a greater advantage in a highly competitive market.

Environmental cold chain distribution center location model in the semiconductor supply chain: A hybrid arithmetic whale optimization algorithm

This study addresses the high cost and high carbon emissions associated with the transportation sector. An environmental cold chain logistics distribution center location model has been designed by considering various constraints such as demand, time windows, and cost factors associated with cargo damage, refrigeration of cold chain cargo, and carbon emissions. An environmental cold chain logistics distribution center location model is designed for the raw materials of low-temperature storage and transportation in the semiconductor supply chain to achieve sustainable development by reducing carbon emissions. This study effectively reduces transportation costs and carbon emissions. To address the issues of slow convergence speed and susceptibility to local optima in the conventional whale optimization algorithm (WOA), a hybrid arithmetic whale optimization algorithm (HAWOA) is proposed by combining arithmetic optimization and the honey badger algorithm. Simulation analysis shows that the HAWOA is capable of obtaining the location scheme with the minimum distribution cost under the same number of iterations, which provides a substantial advantage compared to other algorithms.

Distribution Center Location Selection in Humanitarian Logistics Using Hybrid BWM–ARAS: A Case Study in Türkiye

Abstract This study investigates the criteria affecting the location of humanitarian logistics distribution centers in the Sakarya province of Turkey, an area prone to natural disasters. The study identifies potential distribution center locations and uses the Best-Worst Method (BWM) to determine criteria such as population, distance to major highways and airports, public transportation availability, natural disaster risk, and suitable infrastructure. BWM is used to assign weights to each criterion and rank them based on their importance. The Additive Ratio Assessment (ARAS) method is then used to evaluate potential distribution center locations based on the established criteria. Disaster management experts and academicians provide their opinions through an online and face-to-face survey. Based on the results, Adapazarı is identified as the most suitable district for a humanitarian logistics distribution center. The study highlights the importance of considering multiple criteria when selecting distribution center locations and provides a framework for using multi-criteria decision-making methods in logistics planning. Disaster managers and policymakers can use the results to make informed decisions about the location of humanitarian logistics distribution centers.

Parallel Binary Rafflesia Optimization Algorithm and Its Application in Feature Selection Problem

The Rafflesia Optimization Algorithm (ROA) is a new swarm intelligence optimization algorithm inspired by Rafflesia’s biological laws. It has the advantages of high efficiency and fast convergence speed, and it effectively avoids falling into local optimum. It has been used in logistics distribution center location problems, and its superiority has been demonstrated. It is applied to solve the problem of continuity, but there are many binary problems to be solved in the actual situation. Thus, we designed a binary version of ROA. We used transfer functions to change continuous values into binary values, and binary values are used to symmetrically represent the meaning of physical problems. In this paper, four transfer functions are implemented to binarize ROA so as to improve the original transfer function for the overall performance of the algorithm. In addition, on the basis of the algorithm, we further improve the algorithm by adopting a parallel strategy, which improves the convergence speed and global exploration ability of the algorithm. The algorithm is verified on 23 benchmark functions, and the parallel binary ROA has a better performance than some other existing algorithms. In the aspect of the application, this paper adopts the datasets on UCI for feature selection. The improved algorithm has higher accuracy and selects fewer features.

TODIM-VIKOR method based on hybrid weighted distance under probabilistic uncertain linguistic information and its application in medical logistics center site selection.

At a time of global epidemic control, the location of the medical logistics distribution center (MLDC) has an important impact on the operation of the entire logistics system to reduce the operating costs of the company, enhance the service quality and effectively control the COVID-19 on the premise of increasing the company's profits. Thus, the research on the location of MLDC has important theoretical and practical application significance separately.Recently, the TODIM and VIKOR method has been used to solve multiple-attribute group decision-making (MAGDM) issues. The probabilistic uncertain linguistic term sets (PULTSs) are used as a tool for characterizing uncertain information. In this paper, we design the TODIM-VIKOR model to solve the MAGDM in PULT condition. Firstly, some basic concept of PULTSs is reviewed, and TODIM and VIKOR method are introduced. The extended TODIM-VIKOR model is proposed to tackle MAGDM problems under the PULTSs. At last, a numerical case study for medical logistics centersite selection (MLCSS) is given to validate the proposed method.

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.

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.

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.

Location of Logistics Distribution Center Based on Improved Bald Eagle Algorithm

The location of a logistics distribution center is a complex combinatorial optimization problem, and it is difficult to achieve the best results by traditional mathematical methods. This paper proposes an improved bald eagle search applied to logistics distribution center location selection for the first time, one which uses the chaotic operator to initialize the population, increases the diversity of populations, and introduces a sine and cosine algorithm in the search stage. It increases the global search ability of the algorithm and the ability to jump further out of the local space. Through test function and location simulation experiments, it is proved that the improved algorithm has obvious advantages over other common algorithms in solution accuracy and convergence speed. It can effectively improve the efficiency of logistics distribution when applied to the location of logistics distribution centers. Finally, the improved bald eagle search algorithm is used to optimize the location model of logistics distribution center. The experimental results show that the improved bald eagle search algorithm has good solving ability in this problem, can be obtained to minimize the distribution cost, save the distribution cost effectively and improve the distribution efficiency. It can further optimize the logistics management system and increase the efficiency of logistics enterprises. Compared with similar algorithms, such as WOA, WCA, PSO, the results are improved.

Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm

The basic task of the logistics distribution center is to achieve the storage and distribution of materials, and to plan, implement, and manage the effective flow of materials from the supplying place to the consumption place. Scientific location of logistics distribution center can effectively reduce logistics cost, improve the speed of circulation, increase the profits of enterprises, and enhance the core competitiveness of enterprises. Combining the advantages of K-means clustering algorithm, this paper applies it to the location problem of logistics distribution center and proposes a logistics distribution center location method combining K-means clustering theory and D-S reasoning, which provides a better solution for the location problem of logistics distribution center. Through case analysis, K-means clustering algorithm can obtain reasonable location of logistics distribution center, which can be applied to the location of multilevel logistics distribution network, and has certain practical application value.

Application of FWA-Artificial Fish Swarm Algorithm in the Location of Low-Carbon Cold Chain Logistics Distribution Center in Beijing-Tianjin-Hebei Metropolitan Area

Green development is the hot spot of cold chain logistics today. Therefore, this paper converts carbon emission into carbon emission cost, comprehensively considers cargo damage, refrigeration, carbon emission, time window, and other factors, and establishes the optimization model of location of low-carbon cold chain logistics in the Beijing-Tianjin-Hebei metropolitan area. Aiming at the problems of the fish swarm algorithm, this paper makes full use of the fireworks algorithm and proposes an improved fish swarm algorithm on the basis of the fireworks algorithm. By introducing the explosion, Gaussian mutation, mapping and selection operations of the fireworks algorithm, the local search ability and diversity of artificial fish are enhanced. Finally, the modified algorithm is applied to optimize the model, and the results show that the location scheme of low-carbon cold chain logistics in Beijing-Tianjin-Hebei metropolitan area with the lowest total cost can be obtained by using fireworks-artificial fish swarm algorithm.

Research on Location of Cold Chain Logistics Distribution Center with Low Carbon in Beijing-Tianjin-Hebei Area on the Basis of RNA-Artificial Fish Swarm Algorithm

In order to improve the existing problems of the artificial fish swarm algorithm, the fish swarm algorithm is improved by using the characteristics of RNA computing, and the RNA-artificial fish swarm algorithm is proposed. Since cold-chain food has the characteristics of perishable quality and can generate a large number of carbon emissions during its transportation, this paper establishes a model for optimizing cold chain low carbon logistics location in the Beijing-Tianjin-Hebei area, including factors such as food spoilage, refrigeration and carbon emissions. The logistics location optimization model is solved 30 times with the basic fish swarm algorithm and the RNA-artificial fish swarm algorithm respectively. The simulation results prove the effectiveness of the RNA-artificial fish swarm algorithm.

Multi-objective cold chain logistic distribution center location based on carbon emission.

The issues of food safety and environmental protection are attracting more and more attention. Everyday, a large number of cold chain products are delivered from suppliers to customers. The cold chain products require refrigeration equipment in delivery and should be delivered to customers as soon as possible. Therefore, the challenge of reducing carbon emission and improving the customer satisfaction should be solved. This study presents the impact of carbon emission, customer satisfaction, construction cost, and operation cost on the location of cold chain logistics distribution center. A multi-objective location model for cold chain logistics distribution center considering carbon emission is established. The carbon emission equivalent cost model considers the dynamic carbon emission during transportation and the static carbon emission of the distribution center. The penalty cost under the time window is introduced into the penalty cost model of customer satisfaction, which represents a multi-objective mixed-integer linear programming problem. A non-dominated sorting genetic algorithm II (NSGA-II) is used to design the program through double-layer composite coding. NSGA-II uses a fast non-dominated sorting approach to reduce the computational complexity of non-dominated sorting. This algorithm uses the elitist control strategy, which does not need to share parameters and is more efficient in the multi-objective optimization process. The numerical results show that the proposed algorithm can generate appropriate Pareto solutions for all objectives.

Location Model of Fresh Agricultural Products Logistics Distribution Center Based on Network Nodes

The high logistics cost of fresh produce is the main problem that restricts the distribution of produce. How to reduce the logistics cost of fresh agricultural products, improve the utilization rate of logistics facilities, and improve distribution efficiency and service levels has become the key to research. As an important hub for fresh agricultural products, the fresh agricultural product distribution center can effectively integrate supply and demand information and is the key to ensuring the efficiency of fresh agricultural product distribution, especially to meet the urban fresh agricultural product distribution needs. The fresh agricultural product urban logistics distribution center has the characteristics of small service radius, small scale, many distribution batches and high distribution frequency. The service radius and the scale of the distribution center determine the function of the distribution service. In order to save costs while ensuring distribution efficiency and logistics service levels, it is necessary to scientifically and rationally plan the location of urban distribution centers for fresh agricultural products. Despite extensive research on the location of logistics distribution centers, there are still some shortcomings. First, by analyzing and summarizing the existing literature, the theory and research methods of urban logistics distribution centers for fresh agricultural products are summarized, the characteristics of urban logistics distribution centers for fresh agricultural products are analyzed, and the status of logistics centers for fresh agricultural products is summarized. Influencing factors and site selection process introduced the basic principles and steps of immune algorithm. Secondly, based on the location theory of distribution centers, a cost model for the location of urban logistics distribution centers for fresh agricultural products is established. The model takes into account economic and social factors, including distribution costs, fixed costs, operating costs, waste costs and carbon emission costs. In the case of traffic congestion, a carbon emission factor is introduced to calculate the environmental cost of the allocation process. Finally, combined with the calculation steps of the immune algorithm, the solution steps are designed for the location model of the urban fresh agricultural products logistics distribution center, and the actual calculation examples are studied using MATLAB software programming, and the distribution center is obtained based on the low model. Through the method of this article, the transportation speed of fresh agricultural products logistics distribution center location has been increased by 13%.

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.

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.

Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm

Solving Logistics Distribution Center Location with Improved Cuckoo Search Algorithm