Multiple Distribution Centers Research Articles

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

An electric vehicle routing model with charging stations consideration for sustainable logistics

PurposeRecently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.Design/methodology/approachThis study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.FindingsThe result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.Research limitations/implicationsThe optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.Originality/valueIn prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

A Blockchain-Based Certificateless Aggregate Signature Scheme for Fog-Enabled Smart Grid Environment

The smart grid-based power grid systems face many challenges like-higher computational power requirements, higher latency, security and privacy issues, and single point of failure. In the power grid systems, multiple distribution centers are responsible for supplying electricity in an area. Thus, to address the issues such as single point of failure and higher latency, this paper integrates fog computing and blockchain into the smart grid utilizing distribution centers as blockchain peers and proposes a distributed smart grid environment. Moreover, to address the security and privacy issues, the existing signature schemes face issues like-certificate management, key escrow, higher computational cost, etc. Therefore, this paper employs a certificateless approach and proposes a blockchain-based certificateless aggregate signature scheme that provides authentication, integrity, and privacy in the proposed smart grid environment. Next, a thorough security analysis has been done to prove the security strength of proposed scheme against different types of attackers and attacks. Finally, the performance analysis based on Hyperledger Fabric and cryptography libraries proves the computational and communication efficiency of the proposed scheme.

A decomposition and coordination method for optimising (Q, S ) policies in a two-echelon distribution system with joint replenishment

A continuous-review, two-echelon distribution system with one central distribution centre (CDC) and multiple regional distribution centres (RDCs) is studied. The CDC jointly replenishes its inventories of multiple items from an external supplier, while each RDC replenishes its inventories of the items from the CDC. Each RDC faces a Poisson demand for each item, and the inventories of each stock in the system are controlled by a (Q, S ) policy. Under this policy, an order is placed by a stock whenever its aggregate demand since the last order reaches a given quantity, and the inventory position of each item is raised up to its order-up-to level after the order placement. The objective is to optimise these (Q, S ) policies so that the expected total cost of this system is minimised. We propose a decomposition and coordination method for this optimisation after deriving analytically the cost function of the system. Our extensive numerical experiments demonstrate the effectiveness of the proposed method. Furthermore, a parameter sensitivity analysis is conducted to analyse the impacts of some key system parameters on the performance of the method, and managerial insights are provided for optimising distribution systems with joint replenishment and real applications of the method.

AP clustering algorithm for analysis of the impact of cold chain distribution center location on logistics costs

ABSTRACT The distribution center is a transit place for goods in the logistics network, used to achieve the distribution of goods. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness due to the low temperature or ultra-low temperature requirements of transport objects. Aiming at the problems of high cost and low efficiency of cold chain distribution center location, a new location model of cold chain distribution center is developed. The Affinity Propagation (AP) clustering algorithm is used to simplify location selection. And combine the binary semantics with entropy to reduce the subjectivity of the binary semantics in the process of experiment. The results show that the location selection using the research method is optimal, and the problem that multiple secondary distribution centers are the same retailer will not appear. The research method is more objective and scientific for the location of cold chain distribution centers.

Optimization of emergency allocation of necessities of life based on fractal perspective.

The supply and reserves of emergency necessities of life are important for emergency management in disaster events. The scope of the necessities of life changes with social development, and their reserves and allocation in sudden disaster events continually face new challenges. Timely distribution of the necessities of life during disasters is critical to saving lives and maintaining social order. Therefore, this study proposes a fractal multi-level distribution network (FMDN) optimization model with multiple warehouse points, multiple emergency distribution centers, and multiple disaster points from the perspective of fractal theory. The FMDN model considers the influence of road damage on vehicle speed and the dynamic change in demand at the affected points. The FMDN model aims to minimize the operating costs of a distribution network, including the cost of building emergency reserve points, transportation costs, and penalty costs for lack of demand. Numerical experiments verify the feasibility of the model. The FMDN model is solved using LINGO software programming, and an optimal distribution path and quantity are obtained. Analyzing the numerical example results shows that the model is suitable for emergencies and has good applicability.

Optimization of heterogeneous vehicle logistics scheduling with multi-objectives and multi-centers

Industrial enterprises have high requirements on timeliness and cost when delivering industrial products to their customers. For this reason, this paper studies the vehicle routing problem (VRP) of different vehicle models in multiple distribution centers. First of all, we consider the multi-dimensional constraints in the actual distribution process such as vehicle load and time window, and build a multi-objective optimization model for product distribution with the goal of minimizing the distribution time and cost and maximizing the loading rate of vehicles. Furthermore, an Improved Life-cycle Swarm Optimization (ILSO) algorithm is proposed based on the life cycle theory. Finally, we use the order data that Yunnan Power Grid Company needs to deliver to the customer (municipal power supply bureau) on a certain day to conduct a dispatching experiment. The simulation and application results show that the transportation cost of transportation obtained by the ILSO algorithm is reduced by 0.8% to 1.6% compared with the other five algorithms. Therefore, ILSO algorithm has advantages in helping enterprises reduce costs and improve efficiency.

Research on Cold Chain Logistics Transportation Scheme under Complex Conditional Constraints

A mathematical model is proposed to minimize the sum of vehicle fixed cost, fuel cost, carbon-emission cost, cooling cost, time-penalty cost and split-compensation cost, on the basis of considering the three-level cold-chain-logistics network of manufacturer, distribution center, and seller. The model is constructed based on the constraints of customer time window, vehicle load, demand-splitable, and semi-open driving of multiple distribution centers. We to divide the customer areas according to geographical locations and to carry out the transportation processes in stages. The target solution, which includes vehicle routing, service time and type, cargo details, etc., has been formulated. A two-stage hybrid-heuristic-path-scheme solution algorithm that combines a taboo table, a genetic algorithm, an optimal-path-generation algorithm, a load-capacity-constraint algorithm, and a time-window-constraint algorithm is designed in view of the complexity of the model and the uniqueness of the solution scheme. This paper aims to reasonably plan the resource allocation of cold chain logistics enterprises, reduce the comprehensive cost of cold chain transportation, improve customer satisfaction, and respond to the green logistics policy advocated by the state by reducing vehicle transit time and fuel consumption, and promote energy conservation and emission reduction.

Constrained multi‐location assortment optimization under the multinomial logit model

AbstractWe study the assortment optimization problem in an online setting where a retailer uses multiple distribution centers (DC) to fulfill orders from multiple regions. Customer choice in each region follows a multinomial logit model. Each DC can carry up to a pre‐specified number of products. Outbound shipping cost to a region depends on the DC that ships the order. The problem is to determine which products to carry in each DC and which products to offer for sale in each region to maximize the expected profit. We first show that the problem is NP‐complete. We develop a conic quadratic mixed integer programming formulation and suggest a family of valid inequalities. We also show that a special case with identical choice models can be solved as a linear program. This LP solution approach can be used to develop heuristics for the general case. Numerical experiments show that our conic approach outperforms the mixed integer linear programming formulation and enables us to solve moderately sized instances optimally. The experiments also show that not allowing cross‐shipments or not considering them in assortment decisions may lead to substantial losses and LP‐based heuristics can be effective in practice.

Path Planning Algorithm for the Multiple Depot Vehicle Routing Problem Based on Parallel Clustering

It is necessary to study the problem of vehicle routing in multidistribution centers to improve the speed, time, and cost thereof. It is preferable to use as few vehicles as possible to complete the delivery of goods and minimize the total mileage. With the development of artificial intelligence technology, machine learning is usually used to solve the problem of k shortest paths in multiple distribution centers. User needs are constantly changing; the iterative convergence speed of traditional machine learning methods is low and cannot meet the requirements of path planning in a big-data environment. Aiming at difficult problems in multipath planning, the parallel characteristics of traditional machine learning algorithms are fully exploited; k-means clustering and simulated annealing algorithms are improved through the distributed computing; and the multiple depot vehicle routing problem clustering analysis and path planning under the framework of Spark distributed computing are proposed. Through 30 simulation experiments on the TSPLIB dataset, the optimal solution is obtained with a 100% accuracy rate in problem solving. Experimental comparison and analysis show that the algorithm proposed in this article can solve the problem at least twice as fast as other parallel algorithms. This finding verifies that this method can effectively solve the multipath planning problem, thus greatly improving the quality and efficiency of path planning in large-scale logistics.

Optimal path planning and data simulation of emergency material distribution based on improved neural network algorithm.

Today, data storage technology is also gradually improving. Various industries can store massive amounts of data for analysis. The global climate change and the bad ecology led to frequent occurrence of natural disasters. Therefore, it is necessary to establish an effective emergency materials distribution system. The neural network model is used to calculate and the optimal emergency distribution route is analyzed according to the historical information and the data. Considering backpropagation, this paper further disposing a method to further improve the calculation of neural network algorithm. From the perspective of structural parameters of neural network algorithms, this paper uses genetic algorithms to construct predictions, and combines the actual purpose of material distribution after disasters. Considering the capacity constraints of distribution centers, time constraints, material needs of disaster relief points and different means of transportation, a dual-objective path planning with multiple distribution centers and multiple disaster relief points with the shortest overall delivery time and lowest overall delivery cost is constructed. By establishing an emergency material distribution system, it can maximize the prompt and accurate delivery after a natural disaster occurs, and solves the urgent needs of the people.

Study on optimization of multi-UAV nucleic acid sample delivery paths in large cities under the influence of epidemic environment.

In the context of global novel coronavirus infection, we studied the distribution problem of nucleic acid samples, which are medical supplies with high urgency. A multi-UAV delivery model of nucleic acid samples with time windows and a UAV (Unmanned Aerial Vehicle) dynamics model for multiple distribution centers is established by considering UAVs' impact cost and trajectory cost. The Golden Eagle optimization algorithm (SGDCV-GEO) based on gradient optimization and Corsi variation is proposed to solve the model by introducing gradient optimization and Corsi variation strategy in the Golden Eagle optimization algorithm. Performance evaluation by optimizing test functions, Friedman and Nemenyi test compared with Golden Jackal Optimization (GJO), Hunter-Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA) and Golden Eagle Optimization (GEO), the convergence performance of SGDCV-GEO algorithm was demonstrated. Further, the improved RRT (Rapidly-exploring Random Trees) algorithm is used in the UAV path planning, and the pruning process and logistic chaotic mapping strategy are introduced in the path generation method. Finally, simulation experiments are conducted based on 8 hospitals and 50 randomly selected communities in the Pudong district of Shanghai, southern China. The experimental results show that the developed algorithm can effectively reduce the delivery cost and total delivery time compared with simulated annealing algorithm (SA), crow search algorithm (CSA), particle swarm algorithm (PSO), and taboo search algorithm (TS), and the developed algorithm has good uniformity, robustness, and high convergence accuracy, which can be effectively applied to the multi-UAV nucleic acid sample delivery path optimization in large cities under the influence of an epidemic environment.

Semi-Open Multi-Distribution Center Path Planning with Time Windows

A well-planned robot dispatching platform reduces costs and increases efficiency for companies while also reducing carbon emissions and achieving sustainable development. At the moment, the solution to the difficulty of warehouse logistics is use of multiple distribution centers with autonomous mobile robots (AMR). To solve this problem, this paper establishes a semi-closed model of multiple distribution centers, considering the number of cycles and the number of vehicles. An improved ant colony algorithm is proposed to improve the heuristic function based on the node distance relationship to improve the quality of path search. Dynamic variable pheromone concentration and volatility factors are set to accelerate the convergence speed of the algorithm while effectively reducing the problem of the premature algorithm. The traditional ant colony algorithm and the improved ant colony algorithm are used to solve the established model. In addition, the results show that the traditional ant colony algorithm has a certain rate of dominance in the single-day cost of the closed distribution model, but the overall comprehensive cost is lower than that of the improved ant colony algorithm. The single-day cost of the semi-open multi-distribution center logistics and distribution model is lower than that of the closed multi-distribution center logistics and distribution model, and the 7 day average cost is reduced by 12%. The improved ant colony algorithm can save about 119 kWh of electricity under the same target volume requirement, which achieves the company’s goals of cost reduction and increased efficiency, as well as green and sustainable development.

An adaptive large neighborhood search heuristic for multi-commodity two-echelon vehicle routing problem with satellite synchronization

<p style='text-indent:20px;'>In considering route optimization from multiple distribution centers called depots via some intermediate facilities called satellites to final customers with multiple commodities request, we introduce the Multi-Commodity Two-Echelon Vehicle Routing Problem with Satellite Synchronization (MC-2E-VRPSS). The MC-2E-VRPSS involves the transportation from multiple depots to satellites on the first echelon and the deliveries from satellites to final customers on the second echelon. The MC-2E-VRPSS integrates satellite synchronization constraints and time window constraints for satellites on the two-echelon network and aims to determine cost-minimizing routes for the two echelons. The satellite synchronization constraints which trucks from the multiple depots to some satellites need to be coordinated guarantee the efficiency of the second echelon network. In this study, we develop a mixed-integer programming model for the MC-2E-VRPSS. For validating the model formulation, we conduct the computational experiments on a set of small-scale instances using GUROBI and an adaptive large neighborhood search (ALNS) heuristic which we develop for the problem. Furthermore, the computation experiments for evaluating the applicability of the ALNS heuristic compared with large neighborhood search (LNS) on a set of large-scale instances are also conducted, which proved the effectiveness of the ALNS.</p><p style='text-indent:20px;'> </p><p style='text-indent:20px;'>Addendum: The Acknowledgement “This study was supported by the Humanities and Social Sciences Foundation Program of the Ministry of Education (Project No.19YJA630122), and the "National Key R&D Program of China"(Grant No.2020YFB1712900)” is added. We apologize for any inconvenience this may cause.</p>

Research on Cold Chain Routing Optimization of Multi-distribution Center Considering Traffic Performance Index

In response to the practical situation where traffic congestion factors reduce the distribution efficiency and increase the cost of cold chain logistics in the actual transportation process, a mathematical model for optimizing the cold chain path of multiple distribution centers is established, with the goal of minimizing the comprehensive cost and incorporating a traffic congestion index. By analyzing routine factors such as customer requested time windows, loading and unloading time, and road congestion, an improved particle swarm algorithm that introduces exchange offspring is proposed. The proposed method is tested through experimental simulations and the results are compared with traditional methods. The results show that considering the traffic congestion index saves 7.12% of the comprehensive cost compared to not considering the traffic congestion index, and completes the delivery service with a shorter path length.

Optimization of a Logistics Transportation Network Based on a Genetic Algorithm

In order to solve the problem of genetic algorithm, the author proposes a research on optimization of a logistics transportation network. First, combined with the distribution characteristics of logistics, starting from the relevant research theories of vehicle routing problems, the vehicle travel distance is considered in the basic model of vehicle routing optimization, a vehicle routing model with soft time window is established to minimize the total cost, respectively, and a vehicle routing model with multiple distribution centers. Second, on this basis, according to the actual problems and model needs of the research, using the basic principle of genetic algorithm, the solution algorithm design of the built model is carried out and we use MATLAB for programming, so that the solution of the built model is finally realized. We set T = 100, S = 50, Pc = 0.95, Pm = 0.1. β1 = 0.005, β2 = 1, and β3 = 0.005. Finally, the actual distribution data of the G supermarket chain in the region are used as the experimental data; by picking two shipping instances, the two constructed models and the designed genetic algorithm are verified, respectively.

Research on Multi-Center Mixed Fleet Distribution Path Considering Dynamic Energy Consumption Integrated Reverse Logistics

The fleet operation model in which electric vehicles coexist with traditional vehicles is becoming increasingly popular. Because electric vehicles have certain disadvantages and usage limitations, the multi-center management of the distribution of mixed fleets is very complex. There is no research on the multi-center mixed vehicle routing problem based on the integration of reverse logistics and dynamic energy consumption. In response to this challenge, this study proposes a solution to the multi-center mixed vehicle routing problem considering dynamic energy consumption and integrated reverse logistics. Specifically, three studies were carried out: (1) Considering the influencing factors of the operating cost system of the mixed fleet, a system dynamics model was constructed. (2) On the basis of considering delaying the aging of electric vehicle batteries, a new charging station insertion strategy was designed. (3) Based on a novel charging station insertion strategy, a fast non-dominated sorting multi-objective genetic algorithm with an elite strategy was designed to solve this problem. We designed 15 groups of examples to prove the effectiveness of the model and algorithm. The experimental results show that 46.67% of the cases have more than 60% customer satisfaction. The average expenditure cost of 15 groups of cases is CNY 2018.33, which can improve the average customer satisfaction by 22.94%. This method helps companies to formulate transportation plans according to the actual situation, including providing a cost model that considers multiple influencing factors and improving the average customer satisfaction while reducing the total cost expenditure. We believe that the results of this research can provide methods and ideas for logistics companies with multiple distribution centers to formulate large-scale distribution plans.

Logistics Network Distribution Optimization Based on Vehicle Sharing

The development of the sharing economy has provided new ideas for a vehicle-sharing urban logistics network cooperative distribution strategy. In view of the lack of dispatching capacity or transportation capacity of logistics enterprises with multiple distribution centers, this paper proposes a vehicle-sharing urban logistics network cooperative distribution strategy. Based on the comprehensive consideration of a multi-distribution center, multi-model, rental vehicle, load, speed, fuel consumption, and other factors, the calculation method of vehicle energy consumption is introduced, the network collaborative distribution model with vehicle sharing is established, and an adaptive genetic algorithm combined with a scanning algorithm is designed. Finally, the validity and reliability of the mathematical model and algorithm are validated and analyzed by an example. The research results show that vehicle sharing can improve the efficiency of the distribution network and effectively reduce costs.

Distribution Path Optimization for Intelligent Logistics Vehicles of Urban Rail Transportation Using VRP Optimization Model

Path optimization of logistics distribution vehicles is researched to improve the efficiency of intelligent logistics systems. The logistics distribution system based on urban rail transportation is analyzed. A mathematical model is constructed for the Vehicle Routing Problem (VRP) of multiple distribution centers. A novel Concentration-Immune Algorithm Particle Swarm Optimization (C-IAPSO) is proposed based on the respective advantages of C-IA and PSO in vehicle path optimization combining the concept of antibody concentration. C-IAPSO first calculates the concentration selection probability of particles in the swarm and updates the immune memory bank as per the optimal particle retention strategy to ensure the diversity of the antibody swarm. To assess the performance of C-IAPSO, seven standard test functions are selected for comparison experiments; results prove that it can provide the fastest convergence rate. On unimodal functions Sphere and Quadric, the accuracy of C-IAPSO gets improved significantly. The specific conditions of the distribution center and the demand spots are analyzed; the vehicle travels 508.40 km in total under the optimal distribution path calculated by C-IAPSO, which is a notable decrease compared with the 600.40 km of Adaptive PSO (APSO). To sum up, applying C-IAPSO to vehicle path optimization of intelligent logistics systems can improve transportation efficiency and reduce transportation costs.

Optimization of Urban Unmanned Logistics Distribution Path with Multiple Distribution Centers

Optimization of Urban Unmanned Logistics Distribution Path with Multiple Distribution Centers