Logistics Distribution Network Research Articles

A customer satisfaction-based optimization model for the charging and discharging path and battery swapping stations’ site selection of electric vehicles

This paper studies the problem of considering customer satisfaction in the no-battery-swap mode and in the power-swap mode. First, with the goal of maximizing customer satisfaction, the total cost of charging and discharging and the minimum construction cost of swapping stations, the customer time window, and the load constraints of electric vehicles are considered. A model of electric vehicle charging and discharging route optimization and replacement station location without battery swapping behavior, considering customer satisfaction, is established, and then, a two-stage improved ant colony–genetic algorithm is designed to solve the model, and finally, the comparative analysis considers customer satisfaction. Based on the path optimization results and location decisions considering the cost of charging and discharging, the following conclusions are obtained: 1) electric vehicle route optimization and swap station location planning considering customer satisfaction can not only effectively reduce logistics distribution costs and replacement costs but also improve customer satisfaction levels. 2) Reducing the number of route crossings in the process of logistics distribution routes can save electricity costs for electric vehicles and logistics distribution costs, and help reduce the total cost of the entire logistics distribution network. 3) The gradient setting of the electricity price for electricity exchange will reduce the cost of electricity exchange, improve the utilization efficiency of the battery, reduce the cost of logistics and distribution, and improve the electricity exchange revenue of the electricity exchange station.

Birleşik Krallık'ta tedarik zinciri kesintilerine tanıklık eden stratejik satın almadaki en iyi uygulamalara ilişkin bir araştırma

This study aims to identify best practices in strategic procurement to alleviate the impact of supply chain disruptions around the world. The UK and similar countries rely significantly on global procurement, and the disruptions caused by events like the COVID-19 outbreak and Brexit have severe impacts. The study is conducted in two phases, including a literature review and actual research with an apt methodology. The primary findings of this research are the identification of best practices and looking at the procurement function as a strategic function integrated with supply chain management to make supply chain resilient organizations. This study contributes to the expanding literature concentrating on evaluating the best practices for a sourcing organization to become supply chain resilient during significant supply chain disruptions.

Retracted: An Optimization Model for Logistics Distribution Network of Cross-Border E-Commerce Based on Personalized Recommendation Algorithm

Retracted: An Optimization Model for Logistics Distribution Network of Cross-Border E-Commerce Based on Personalized Recommendation Algorithm

RETRACTED ARTICLE: Effectiveness of Mixed Fuzzy Time Window Multi-objective Allocation in E-Commerce Logistics Distribution Path

The study of logistics distribution network under e-commerce environment is conducive to the establishment of efficient logistics distribution system, but also to promote the further development of e-commerce and improve social benefits of great significance. This study considers multiple fuzzy factors and introduces a customer fuzzy time window with variable coefficients, establishes a multi-objective set allocation integrated multi-level location path planning model, and proposes an archive type multi-objective simulated annealing improvement algorithm based on master–slave parallel framework embedded taboo search to solve the model. Tabu search and large-scale neighborhood algorithm are used to solve the initial solutions of the first level network and the second level network respectively, and archival reception criterion is introduced to deal with the multi-objective problem. The results of the proposed algorithm for the two-level site-routing problem are less than 6% different from the internationally known optimal solution. The master–slave parallel computing framework improves the efficiency of the algorithm by about 6.38%. The experimental results prove the effectiveness and necessity of the improved optimization. In addition, this study simulates the site-routing problem model constructed by the study by extending the data of standard examples. The experimental results prove the correctness and reference significance of the multilevel site-routing problem model with multiple fuzzy factors.

An improved discrete particle swarm optimization approach for a multi-objective optimization model of an urban logistics distribution network considering traffic congestion

To optimize urban logistics networks, this paper proposes a multi-objective optimization model for urban logistics distribution networks (ULDN). The model optimizes vehicle usage costs, transportation costs, penalty costs for failing to meet time windows, and carbon emission costs, while also considering the impact of urban road traffic congestion on total costs. To solve the model, a DPSO (Discrete Particle Swarm Optimization) algorithm based on the basic principle of PSO (Particle Swarm Optimization) is proposed. The DPSO introduces multiple populations to handle multiple targets and uses a variable neighbourhood search strategy to improve the search ability of particles, which helps to improve the local search ability of the algorithm. Simulation results demonstrate the effectiveness of the proposed model in avoiding traffic congestion, reducing carbon emissions costs, and time penalty costs. The optimization comparison results between DPSO and PSO also verify the superiority of the DPSO algorithm. The proposed model can be applied to real-world urban logistics networks to improve their efficiency, reduce costs, and minimize environmental impact.

Branch-and-price for routing with probabilistic customers

We study the Vehicle Routing Problem with Probabilistic Customers (VRP-PC), a two-stage optimization model, which is a fundamental building block within the broad family of stochastic routing problems. This problem is mainly motivated by logistics distribution networks in which customers receive frequent delivery services, and by the last mile problem faced by companies such as UPS and FedEx. In a first stage before customer service requests realize, a dispatcher determines a set of vehicle routes serving all potential customer locations. In a second stage occurring after observing all customer requests, vehicles execute planned routes skipping all locations of customers not requiring service. The objective is to minimize the expected vehicle travel cost assuming known customer realization probabilities. We propose a column generation framework to solve the VRP-PC to a given optimality tolerance. Specifically, we present two novel algorithms, one that under-approximates a solution’s expected cost, and another that uses its exact expected cost. Each algorithm is equipped with a route pricing mechanism that iteratively improves the approximation precision of a route’s reduced cost; this produces fast route insertions at the start of the algorithm and reaches termination conditions at the end of the execution. Compared to branch-and-cut algorithms for arc-based formulations, our framework can more readily incorporate sequence-dependent constraints, which are typically required in routing problems. We provide a priori and a posteriori performance guarantees for these algorithms, and demonstrate their effectiveness via a computational study on instances with realization probabilities ranging from 0.5 to 0.9.

Efficient Mathematical Lower Bounds for City Logistics Distribution Network with Intra-Echelon Connection of Facilities: Bridging the Gap from Theoretical Model Formulations to Practical Solutions

Focusing on the dynamic improvement of the underlying service network configuration, this paper aims to address a specific challenge of redesigning a multi-echelon city logistics distribution network. By considering the intra-echelon connection of facilities within the same layer of echelon, we propose a new distribution network design model by reformulating the classical quadratic assignment problem (QAP). To minimize the overall transportation costs, the proposed model jointly optimizes two types of decisions to enable agile distribution with dynamic “shortcuts”: (i) the allocation of warehouses to supply the corresponding distribution centers (DCs), and (ii) the demand coverage decision from distribution centers to delivery stations. Furthermore, a customized branch-and-bound algorithm is developed, where the lower bound is obtained by adopting Gilmore and Lawler lower Bound (GLB) for QAP. We conduct extensive computational experiments, highlighting the significant contribution of GLB-oriented lower bound, to obtain practical solutions; this type of efficient mathematical lower bounds offers a powerful tool for balancing theoretical research ideas with practical and industrial applicability.

Collaborative multidepot electric vehicle routing problem with time windows and shared charging stations

Logistics distribution using electric vehicles (EVs) is gradually becoming popular in urban logistics networks with the promise of carbon peak and carbon neutrality. A collaborative mechanism among multiple depots can effectively increase the efficiency and sustainability of a logistics network. Sharing charging stations (CSs) is presented as a critical strategy to improve the efficiency of logistics networks and facilitate collaboration further. Solving a collaborative multidepot EV routing problem with time windows and shared charging stations (CMEVRPTW-SCS) involves centralized transportation services among multiple depots, coordinated delivery services among multiple depots and the charging needs of EVs. A bi-objective nonlinear programming model for CMEVRPTW-SCS is formulated to minimize the total operating cost and number of EVs, wherein the operating cost contains transportation costs among depots and vehicle routing costs in each depot. Gaussian mixture clustering algorithm is developed to cluster customers and assign them to depots probabilistically to reduce the computational complexity, and an improved multi-objective genetic algorithm with tabu search (IMOGA-TS) is designed to optimize the EV routes and charging locations in CMEVRPTW-SCS. With the combination of local and global searches and the improvement of solutions at each iteration by TS, the proposed hybrid algorithm can significantly accelerate the efficient exploration of optimal solutions. The comparative results of the IMOGA-TS with multi-objective genetic algorithm, non-dominated sorting genetic algorithm-II and multi-objective harmony search algorithm show the superiority of the proposed algorithm in solving CMEVRPTW-SCS. Then, a Shapley value model based on game theoretical methods is proposed to find the best profit allocation scheme. A real-world case study of CMEVRPTW-SCS in Chongqing City, China is conducted, and three scenarios of various collaborative alliances and 11 schemes with and without shared CSs are further discussed. The experimental results show that the proposed methods are of practical significance in reducing operating costs, improving transportation efficiency, and achieving sustainable operation in EV-based logistics distribution networks.

Study on Sustainable Combined Location-Inventory-Routing Problem Based on Demand Forecasting

The sustainable combined location-inventory-routing problem (CLIRP) based on demand forecasting is studied in this paper. Based on the construction of a multi-stage demand forecasting model, five parts of total logistics costs: the costs of trunk transportation and regional transportation, the fixed costs of distribution center construction, the inventory holding costs, shortage costs, and salvage, are comprehensively considered. The existing CLIRP model does not consider the environmental influence. Thus, a sustainable CLIRP model considering carbon emission is established with minimum logistics costs and emission as the objective function. A heuristic algorithm gives the initial solution, and then a hybrid heuristic algorithm combining the tabu search algorithm with the simulated annealing algorithm is proposed to find the global near-optimal solution. Finally, a numerical example of a garment chain enterprise is given to illustrate the solving process of the model. The results show that using the proposed algorithm determines the optimal locations of RDCs, and the transportation routes with each region are obtained with the minimum total logistics costs and carbon emission. The model realizes the combination of location, inventory, and routing problems of the large garment enterprises and finally realizes the goal of optimizing the sustainable logistics distribution network of the garment industry, which verifies the effectiveness of the model. Moreover, a comparison is made to show the efficiency of the proposed algorithm; the results show that the proposed algorithm in this paper optimizes the route and selections of RDCs.

Identification of Urban Air Logistics Distribution Network Concepts

This study attempts to identify urban air logistics (UAL) operational concepts by synthesizing the urban freight transport (UFT) network configurations and urban air mobility (UAM) characteristics. UAL is gaining popularity as a quick and safe mode that has the potential to address several logistics challenges. This study has reviewed scientific papers identified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to understand the features of existing UFT distribution networks, unmanned aerial systems (UAS), and UAM. The cross analysis considered UFT features such as the parcel’s weight, delivery type (time sensitivity, customer-to-customer, business-to-consumer, or business-to-business), and UFT distribution network types and compared them against the features of UAS such as maximum take-off weight, range, and infrastructure requirements (drone ports). The analysis identified three types of UAL operational concepts: door-to-door direct (D2DD), hybrid, and multimodal. Lightweight deliveries can be operated using D2DD and hybrid through point-to-point (P-P) and extended P-P network concepts. The hybrid concept uses an intermediate drop-off location to deliver goods. Hub-and-spoke (H-S), extended H-S, and trunk line with collecting/distribution network designs can be used for heavyweight cargo operations. D2DD versus hybrid/multimodal concepts were compared based on six thematic clusters (societal implications, safety and security, ethics, environmental issues, public acceptance, and urban planning and infrastructure). This study identified that technological development and innovation could reduce social implications and ethical challenges. Furthermore, public willingness and economic feasibility will determine the success of the operational concepts. The identified UAL operational concepts, network designs, and challenges will help to conduct further research in this subject area.

Sustainable Urban Logistics Distribution Network Planning with Carbon Tax

Global warming caused by excessive carbon dioxide emissions is threatening the sustainable development of human society. Considering the upcoming carbon tax policy in China, this paper studies the planning of dual-level urban logistics distribution network in the context of carbon emissions. Based on reasonable assumptions, a bi-objective mixed-integer programming model considering logistics operating costs and carbon emission costs is constructed. Given the problem size, an improved genetic algorithm is designed. Based on a numerical example, the optimization results of the improved genetic algorithm and the GLPK optimization suite are compared to verify the effectiveness of the proposed model and algorithm. Under different carbon tax rates, by adjusting the logistics distribution network along, the logistics operators can achieve a maximum cost savings of 19.4%, while carbon emissions can be reduced by up to 47.8%. The major conclusions include: carbon tax will bring about huge cost burdens for urban logistics operators; the reconfiguration of urban logistics network is a powerful measure to reduce carbon emission with the least extra costs; with problem size rises quickly, the intelligent algorithm as proposed in this article can always find near optimal solutions with acceptable time costs.

Research on Optimization of Cross-Border e-Commerce Logistics Distribution Network in the Context of Artificial Intelligence

The continuous innovation of artificial intelligence technology has led to industrial upgrading and industry transformation in various industries, and e-commerce logistics has borne the brunt. Artificial intelligence relies on the intelligence of a large number of operations and decisions in the process of logistics operations, as well as the integration of transport, storage, distribution, packaging, loading and unloading, and other aspects of the production process and the hierarchy of the system, which has become an important engine to promote the upgrading of logistics equipment and technology, the innovation of production links and processes, the change in the structure of supply and demand of traditional logistics jobs, and the renewal of the traditional form of logistics. The application of artificial intelligence and the development of a new generation of information technology, including big data, has opened the era of intelligent logistics. And the continuous deepening of China’s foreign trade makes the demand for cross-border e-commerce logistics surge, and the importance of cross-border e-commerce logistics has been rapidly highlighted. Hence, it becomes urgent to construct a cross-line Internet business coordinated operations improvement model by consolidating man-made consciousness innovation. In view of investigating the activity techniques of cross-line online business coordinated factors, this paper advances the improvement methodology of cross-line web-based business operations advancement way with regard to man-made reasoning. By concentrating on the ongoing circumstance and issues of cross-line web-based business coordinated factor circulation module and consolidating the idea of wise strategies, the appropriation stage, the way transportation stage, and freight conveyance phase of Internet business coordinated factor dissemination activity are advanced, in order to accomplish the motivation behind lessening the expense of end dispersion, working on the effectiveness of dissemination, and expanding consumer loyalty.

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 Research of Cross-Border Logistics Based on Cloud Distribution Model

Cross-border logistics is an important support for the rapid development of cross-border e-commerce. This paper expounds cross-border e-commerce and its development status and explores the problems existing in cross-border e-commerce logistics distribution, such as high cost, low level of information, e-commerce credit evaluation system being not perfect, and professional personnel shortage. Advanced cloud distribution mode is introduced into cross-border e-commerce logistics, and cloud logistics distribution network model is built using the precise center-of-gravity method. Based on the cloud distribution, the paper puts forward some suggestions for the development of cross-border logistics, such as optimizing the path to reduce transshipment links, optimizing the level of logistics informatization, improving the market supervision system and credit evaluation mechanism, and improving the talent training mechanism, in order to improve the stability and security of cross-border logistics distribution, reduce logistics costs, and improve service quality.

A multi-product multi-layer urban freight distribution problem solved using a hybrid metaheuristic procedure

The pick-up and delivery routing problem has received special attention thanks to its application to urban freight distribution processes. However, due to the multiple levels involved in those processes, modeling and analyzing urban distribution networks in urban contexts are complex tasks. As a result, efficient and robust solution methods should be proposed according to the dynamic and uncertain conditions that characterize this type of problems. This article presents a new formulation for the pick-up and delivery problem in a logistics distribution network composed of 3 levels: n: 1: m (n suppliers, 1 urban consolidation center, and m customers). In addition, an algorithm based on GRASP heuristic and 2-opt algorithm was implemented here to find solutions to problem, which were compared with the results of the same algorithm for a two-layer vehicle routing problem in several instances. Thus, the proposed procedure achieved a 22% improvement over such algorithm.

THE COMPLEXITY OF SUPPLY CHAIN SYSTEM’S LOGISTICS FINANCIAL MANAGEMENT AND DYNAMIC NONLINEAR SYSTEM

The aim of this paper is to discuss the accidental events’ impacts on the complicated supply chain systems and reveal the dynamic nonlinear system’s role in enterprises’ logistics financial management. First, the logistics distribution network and nonlinear system are analyzed and introduced, revealing the complexity of enterprises’ supply chain systems. Second, considering the situation of enterprises’ financial management, four major entities, the supplier, the distributor, the retailer, and the manufacturer, are introduced to construct a dynamic nonlinear model for the supply chain system. Finally, the model before and after accidental events are simulated and analyzed using the AnyLogic software. Results demonstrate that in normal states, each node’s inventory in the system changes periodically, while the distributor’s state is the most stable. A notable decrease in market demand has the largest impact on the distributor. Once the market demand is recovered, the decrease in the distributor’s inventory is the most apparent, and the inventories of the supplier, the retailer, and the manufacturer also decrease. The supplier has the highest cost at the beginning of system operation, and the distributor’s cost is the highest after the operation becomes stable. Once an accidental event caused a sharp decrease in market demand, the costs of all major entities in the system show an increasing trend. Once the market demand is recovered, the costs of different node entities will increase instead. As for the profits, the distributor has the highest profit initially, which is significantly influenced by the accidental events. The results provide a reference for revealing the changing laws of complicated supply chain systems and promoting the development of enterprises’ financial management.

Two-Stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS Algorithm

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the area scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam graph search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

Optimization of Location-Routing Planning in Logistics Distribution Network for E-Commerce Enterprises Using Dpc-Dnspso Algorithm

Optimization of Location-Routing Planning in Logistics Distribution Network for E-Commerce Enterprises Using Dpc-Dnspso Algorithm

Optimization of the distribution logistics network: a case study of the metalworking industry in Colombia

Logistics management is a key factor for business competitiveness because of the variety of strategies available to improve the distribution of goods through the supply chain, enabling the optimization of the resources assigned to this effect. A key aspect for good logistics management is the distribution network, where the location of the network components plays a key role, especially in the case of distribution centers, which are platforms through which the goods received from factories or suppliers are redistributed to the various points of sale. In the above context, this case study proposes a solution for selecting two locations from among four possible sites for construction of distribution centers for a company in the metalworking industry, aimed at minimizing operating costs through the design of a logistics distribution network, using as a tool the GUSEK software. To this effect, the aspects taken into consideration include vehicle capacity, the costs associated with opening a distribution center, types and volume of the products to be shipped, costs associated with the travel distances, the production capacity of the factories, and customer demand, among others. Execution of the model produces the best locations for the distribution centers and the types of vehicles to be used based on their capacity and the optimal routes for transporting a company’s goods.

Optimization of Location-Routing Planning in Logistics Distribution Network for E-Commerce Enterprises

Optimization of Location-Routing Planning in Logistics Distribution Network for E-Commerce Enterprises