Vehicle Routing Optimization Research Articles

Application of the Logistics Management System Based on Cloud Computing Technology

In order to solve the vehicle routing optimization problem of cold chain logistics distribution, this paper proposes a vehicle routing optimization method of cold chain logistics distribution based on cloud computing technology. This paper analyzes the advantages of processing the real-time routing of distribution vehicles under the cloud computing mode and establishes the application service architecture of vehicle routing optimization in cold chain logistics; under this architecture, multisource real-time traffic information is obtained, the vehicle distribution time and comprehensive cost are analyzed, the vehicle routing optimization model of cold chain logistics distribution is constructed, and the coarse-grained parallel genetic algorithm is used to solve the model in the cloud computing environment. The experimental results show that if W 1 = 0 , W 2 = 1 , the optimal path is 0-6-7-3-1-4-2-8-5, the total cost is 1620 yuan, the total mileage is 72 km, the vehicle driving time is 2.17 hours, and the calculation time is 10.79 s; let W 1 = 1 , W 2 = 0 , the optimal path is 0-5-6-7-2-4-3-1-8, the vehicle travel time is 1.84 hours, the total cost is 1736 yuan, the total mileage is 76 km, and the calculation time is 11.56 s. The optimal path is obtained in the effective time range. Conclusion. The real-time route optimization method of refrigerated vehicles in the cloud computing environment is effective. This method has practical significance to realize fine control of cold chain logistics distribution cost and improve distribution service efficiency.

Research on Emergency Logistics Vehicle Route Scheduling and Optimization Method Based on Multi-Intelligent Decision System

Logistics distribution vehicle planning is an important issue in logistics transportation activities, and it is also a research hotspot in theoretical circles at home and abroad. At present, many studies have focused on establishing vehicle planning models and optimizing vehicle planning in different environments and have achieved rich results. As an important part of transportation production process, the efficiency of logistics distribution is very important to the whole production process. Especially for emergency logistics, every minute is very critical for emergency situations such as disaster relief. In order to improve the efficiency of emergency logistics, this paper applies multiagent technology to emergency logistics and puts forward an integrated modeling method of enterprise macromodeling, business process mesomodeling, and micromodel design. Using the agent-oriented system development method, an emergency logistics distribution vehicle planning model system is established. The development process of multiagent automatic trading system is described. The results show that it is feasible and effective to use multi-intelligent fuselage technology for emergency logistics distribution vehicle planning and decision-making. The algorithm proposed in this paper has advantages over the container order sequence processing scheme, and the total cost of order acceptance decreases sharply in the initial stage, which shows the practical convergence of the algorithm. The adjacency search method and Tabu search method deal with the calculation of total labor cost, and the Tabu neighborhood search algorithm obtains better results with lower labor cost.

Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model

The energy consumption of electric vehicles is closely related to the problems of charging station planning and vehicle route optimization. However, due to various factors, such as vehicle performance, driving habits and environmental conditions, it is difficult to estimate vehicle energy consumption accurately. In this work, a physical and data-driven fusion model was designed for electric bus energy consumption estimation. The basic energy consumption of the electric bus was modeled by a simplified physical model. The effects of rolling drag, brake consumption and air-conditioning consumption are considered in the model. Taking into account the fluctuation in energy consumption caused by multiple factors, a CatBoost decision tree model was constructed. Finally, a fusion model was built. Based on the analysis of electric bus data on the big data platform, the performance of the energy consumption model was verified. The results show that the model has high accuracy with an average relative error of 6.1%. The fusion model provides a powerful tool for the optimization of the energy consumption of electric buses, vehicle scheduling and the rational layout of charging facilities.

EXPLORING QUANTUM COMPUTING POTENTIALS IN SOLVING A COMBINATORIAL OPTIMIZATION PROBLEM TO MINIMIZE EXPOSURE TO COVID-19 DURING A CITY JOURNEY

Abstract. COVID-19 is an airborne virus that can be spread directly or indirectly from one person to another. Spreading the virus strongly depends on the location and time and hence, a Spatio-temporal event. Moreover, traffic congestion will increase the spread of the virus not only because of the vicinity but also because of increased temperature and humidity in these spaces for a short or long time. This paper introduces a vehicle routing optimization model to reduce COVID-19 exposure risk during a city journey by solving it as a quadratic unconstrained binary optimization problem on a quantum annealing computer. Indeed, the objective of the COVID-19 prevention optimization problem is to minimize the risk of exposure for a given set of road users between origins and destinations. Microsoft Taxi data from the city of Beijing have been used to simulate road users’ movement. The problem has been run onto three different solvers. One of the solvers is executed on classical computers, and two other solvers are executed on hybrid quantum solvers. Hybrid solvers return the solution within less than 0.03 seconds on quantum processing unit time. However, the results will be returned at least 5 seconds after the execution in the classical solver. It is worth mentioning that, as there is no direct access to the quantum computers, it is hard to compare the results on the same scale as the queries will go on a queue in D-wave quantum computers. Applying the proposed model on the trajectory data shows a better distribution of the vehicles on the road network.

Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows

Dynamic customer demands impose new challenges for vehicle routing optimization with time windows, in which customer demands appear dynamically within the working periods of depots. The delivery routes should be adjusted for the new customer demands as soon as possible when new customer demands emerge. This study investigates a collaborative multidepot vehicle routing problem with dynamic customer demands and time windows (CMVRPDCDTW) by considering resource sharing and dynamic customer demands. Resource sharing of multidepot across multiple service periods can maximize logistics resource utilization and improve the operating efficiency of delivery logistics networks. A bi-objective optimization model is constructed to optimize the vehicle routes while minimizing the total operating cost and number of vehicles. A hybrid algorithm composed of the improved k-medoids clustering algorithm and improved multiobjective particle swarm optimization based on the dynamic insertion strategy (IMOPSO-DIS) algorithm is designed to find near-optimal solutions for the proposed problem. The improved k-medoids clustering algorithm assigns customers to depots in terms of specific distances to obtain the clustering units, whereas the IMOPSO-DIS algorithm optimizes vehicle routes for each clustering unit by updating the external archive. The elite learning strategy and dynamic insertion strategy are applied to maintain the diversity of the swarm and enhance the search ability in the dynamic environment. The experiment results with 26 instances show that the performance of IMOPSO-DIS is superior to the performance of multiobjective particle swarm optimization, nondominated sorting genetic algorithm-II, and multiobjective evolutionary algorithm. A case study in Chongqing City, China is implemented, and the related results are analyzed. This study provides efficient optimization strategies to solve CMVRPDCDTW. The results reveal a 32.5% reduction in total operating costs and savings of 29 delivery vehicles after optimization. It can also improve the intelligence level of the distribution logistics network, promote the sustainable development of urban logistics and transportation systems, and has meaningful implications for enterprises and government to provide theoretical and decision supports in economic and social development.

АВТОМАТИЗАЦИЯ СИСТЕМЫ РАСЧЕТОВ ПРИ ПОИСКЕ ОПТИМАЛЬНЫХ МАРШРУТОВ ДВИЖЕНИЯ ГРУЗОВЫХ АВТОМОБИЛЕЙ

An example of the development of an automated system for calculating optimal vehicle routes is considered

Fuzzy Demand Vehicle Routing Problem with Soft Time Windows

Considering the vehicle routing problem with fuzzy demand and fuzzy time windows, a vehicle routing optimization method is proposed considering both soft time windows and uncertain customer demand. First, a fuzzy chance-constrained programming model is established based on credibility theory, minimizing the total logistics cost. At the same time, a random simulation algorithm is designed to calculate the penalty cost of delivery failures caused by demand that cannot be satisfied. In order to overcome the shortcomings of GA, which easily falls into the local optimum in the process of searching, and the slow convergence speed of SA when the population is too large, a hybrid simulated annealing–genetic algorithm is adopted to improve the solution quality and efficiency. Finally, the Solomon standard example is used to verify the effectiveness of the algorithm, and the influence of decision-makers’ subjective cost preference is analyzed.

Research on Logistics Distribution Vehicle Path Optimization Based on Simulated Annealing Algorithm

With the continuous development of Internet technology, e-commerce has gradually become a popular field. As an important part of China’s tertiary industry, the rapid development of e-commerce has played a huge role in promoting the development of the domestic economy. At the same time, e-commerce has also greatly contributed to the development of the logistics industry. Hence, with the rapid growth of the economy and the rapid development of e-commerce, the construction of logistics and distribution systems plays an essential role in the process of urban development. A perfect logistics and distribution system can promote the overall economic efficiency of the city and facilitate people’s daily lives. In addition, the development of logistics systems can meet the escalating needs of consumers and the increasingly competitive market environment, thus promoting the transformation of e-commerce retail enterprises to high-quality development. The simulated annealing algorithm is an effective approximation algorithm for solving optimization problems, and the application of this algorithm to path optimization problems can be of practical value in solving problems in urban road traffic and logistics distribution. Therefore, exploring logistics distribution vehicle route optimization is of great practical significance to optimize the allocation of resources in cities, improve the economic efficiency of enterprises, and solve the problems of urban traffic congestion and can provide reference for relevant departments in their decision-making. This paper focuses on how to optimize vehicle paths in the logistics distribution process and constructs a path optimization model that satisfies the multiobjective premise. The path problem is solved using the simulated annealing algorithm, and the results are analyzed.

The Vehicle Routing Optimization Problem of Refrigerated Vehicle with Temperature Compartments under Consistent Delivery Model

The lower utilization rate of refrigerated vehicles leads to higher distribution cost. Therefore, this paper proposes a model of refrigerated vehicles with multi temperature area to realize consistent delivery of different product groups at the same customer point. Based on the objective function of loading cost, distribution cost, unloading cost and penalty cost of failure to perform on time, the model is solved by adaptive large neighborhood search (ALNs) algorithm, and the distribution route is optimized by designing ring destroying operator, repair operator and adaptive process. An example is given to analyze the impact of the capacity of refrigerated vehicles with multi temperature compartments on distribution time, cost and average utilization compartments. Finally, an appropriate refrigerated vehicle with multi temperature compartments is selected for delivery according to the distribution characteristics of customers.

An augmented Lagrangian relaxation method for the mean-standard deviation based vehicle routing problem

With the ever-increasing trip demand and limited transportation resource, traffic congestions remain a tricky problem. Under traffic congestions, travel times are unstable and variable in a real-world network. When we conduct many time-sensitive vehicle routing optimizations such as vaccine transportation, it is essential to consider the travel time variability in these optimizations. However, most previous vehicle routing studies neglected the travel time variability and adopted the expected travel times or lengths of links. The historical travel times of multiple days allow us to access and describe the travel time variability. We can easily obtain the travel time means and standard deviations of links from these data. This study applies these two statistical indicators to vehicle routing optimization and concentrates on a mean-standard deviation based vehicle routing model. This model has a nonlinear and concave objective function that dramatically increases the problem complexity. We develop a novel augmented Lagrangian relaxation approach for this problem. A lower bound is constructed for this problem by applying the Lagrangian relaxation and decomposition technique twice. To break the inherent symmetry of this problem, we use the augmented Lagrangian relaxation method with block coordinate descent, linearization, and Lagrangian substitution techniques to achieve high-quality feasible solutions. Thus, the relative gap between the lower and upper bounds can be evaluated and reduced in the solving procedure. We implement the proposed method on the datasets modified from the standard benchmark instances. The average relative gaps of instances in sets A and P are 8.8% and 7.6%, respectively.

A Mathematical Model for the Routing Optimization Problem with Time Window

Abstract This paper establishes a mathematical model for the routing optimization problem. Then, the model is solved by a genetic algorithm. Finally, to verify the proposed method, a case study is carried out. According to the results, the proposed model and algorithm can be used to solve the problem and obtain the optimal vehicle route with minimum time cost and travel distance.

Multi-objective vehicle routing with automated negotiation

This paper investigates a problem that lies at the intersection of three research areas, namely automated negotiation, vehicle routing, and multi-objective optimization. Specifically, it investigates the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads for one another, in order to improve efficiency and reduce the distance they drive. In order to do so, these companies need to find ways to exchange their truck loads such that each of them individually benefits. We present a new heuristic algorithm that, given one set of orders for each company, tries to find the set of all truck load exchanges that are Pareto-optimal and individually rational. Unlike existing approaches, it does this without relying on any kind of trusted central server, so the companies do not need to disclose their private cost models to anyone. The idea is that the companies can then use automated negotiation techniques to negotiate which of these truck load exchanges will truly be carried out. Furthermore, this paper presents a new, multi-objective, variant of And/Or search that forms part of our approach, and it presents experiments based on real-world data, as well as on the commonly used Li & Lim data set. These experiments show that our algorithm is able to find hundreds of solutions within a matter of minutes. Finally, this paper presents an experiment with several state-of-the-art negotiation algorithms to show that the combination of our search algorithm with automated negotiation is viable.

Vehicle Routing Optimization Based on Multimedia Communication and Intelligent Transportation System

With the maturity and popularity of network technology and multimedia technology, more and more communication tools and means have entered people’s lives. The usual communication tools are often limited to the ability to transmit sound signals. This method does not well express the information between the two parties in some special occasions, such as a noisy environment, where both parties have language or hearing impairments. The multimedia communication system is a technology that combines network communication and multimedia. It utilizes the efficiency of data transmission over the network and the diversity of information in multimedia, making communication between people faster, clearer, and more intuitive. The multimedia communication system is an important part of the application to the intelligent transportation system. Intelligent transportation system is a systematic, real-time, accurate, interactive, and extensive traffic management system established by the comprehensive use of modern high and new technology in the transportation system. With these characteristics, intelligent transportation system has increasingly become an important means to solve modern traffic problems. Based on multimedia communication and intelligent transportation system, this article optimizes the vehicle path and realizes the application of intelligent transportation. The proposed intelligent transportation system realizes functions, such as vehicle speed detection, vehicle behavior semantic analysis, license plate recognition by processing video data, and image data acquired based on multimedia communication, and can capture illegal vehicles and then identify illegal vehicles. The license plate is conducive to promoting the management and control of intelligent transportation and reducing the occurrence of traffic accidents.

Logistics Distribution Path Optimization Using Support Vector Machine Algorithm under Different Constraints

Accelerating product flow, improving service level, lowering logistics costs, reducing the possibility of product losses in circulation, and thus optimizing the logistics distribution system are the issues that enterprise managers should consider in logistics distribution. Traditional algorithms can only solve simple problems, while intelligent algorithms can solve the most complex combinatorial optimization problems. The optimization problem of logistics vehicle scheduling path with different constraints is studied in this paper using the SVM algorithm, and the improved algorithm is simulated to verify its effectiveness. The simulation results show that the logistics distribution path optimization method based on the SVM algorithm has good global searching ability, effectively avoids the algorithm falling into local optimum, and reduces total distribution cost, proving the algorithm’s effectiveness. This scheme can optimize vehicle routes, increase distribution efficiency, and reduce logistics costs, and it can be used in a wide range of logistics distribution route optimization applications.

The vehicle routing problem of intercity ride-sharing between two cities

In this paper, we consider that a private company has developed a platform to provide intercity ride-sharing (IRS) services for riders between two cities. The riders between the two cities need to provide their travel information to the platform hours in advance. The company adopts commercial vehicles to pick up riders from one city and deliver them to the other city. The vehicle routing problem of IRS (VRP-IRS) is one of the core problems in the platform's decision making process. Due to the private nature of IRS platform, it is assumed that the platform aims to maximize the total profit of the IRS system by optimizing vehicle routing. As the IRS is long-distance travel, in order to ensure driving safety, each driver has to take a break after completing a long-distance trip. In this paper, the VRP-IRS is defined on a directed graph and formulated as a mixed integer linear programming problem. As the VRP-IRS is NP-hard, we propose a variable neighborhood search algorithm to solve the VRP-IRS. According to the characteristics of the feasible solutions to the VRP-IRS, a greedy sequential route construction method is developed to generate the initial solutions. Four trip-based neighborhood operators and four rider-based local search operators are proposed to shake the current solution to a new neighborhood and find better solutions based on the new neighborhood, respectively. Finally, numerical examples are provided to illustrate the performance of the proposed algorithm and the properties of the proposed model.

Collaborative multicenter vehicle routing problem with time windows and mixed deliveries and pickups

This study focuses on the collaborative multicenter vehicle routing problem with time windows and mixed deliveries and pickups (CMVRPTWMDP), which is a variant of the vehicle routing problem (VRP) with mixed deliveries and pickups, and VRP with simultaneous deliveries and pickups and time windows. Collaboration and transportation resource sharing are adopted to optimize vehicle routes in the CMVRPTWMDP, to integrate the delivery and pickup services with time windows, and to construct open–closed mixed vehicle routes. First, the CMVRPTWMDP is formulated as a mixed-integer programming model to minimize logistics operating costs. The effect of transportation resource sharing on reducing the number of needed vehicles and the maintenance cost is considered in the model formulation. Second, a two-stage hybrid algorithm combining customer clustering and vehicle routing optimization is designed to solve the CMVRPTWMDP. An improved 3D k-means clustering algorithm based on space–time distances and the customer demand is proposed to reassign customers to logistics facilities (e.g., delivery or pickup centers). Furthermore, a hybrid heuristic algorithm that combines the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, called GA–PSO, is designed to optimize the vehicle routes. A coordination operator between GA and PSO is designed to allow particles and chromosomes to interact, increasing the diversity of particle swarms and the possibility of finding a feasible solution. Third, the performance and effectiveness of the proposed approach are tested by comparing them with the CPLEX solver using 30 small-scale instances and other existing algorithms using 25 benchmark instances. Fourth, the minimum costs-remaining savings (MCRS) model is adopted to design a fair and reasonable profit allocation scheme for participants in the collaborative alliance and maintain alliance stability. Finally, the optimization results of a real-world case study from Chongqing, China, show that transportation resource misuse and logistics operating costs are significantly reduced, demonstrating the proposed approach’s effectiveness and applicability. This study provides insights for logistics enterprises and transportation departments on effectively allocating and utilizing the transportation resources and optimizing the local logistics network.

Solving Capacitated Vehicle Routing Problem by an Improved Genetic Algorithm with Fuzzy C-Means Clustering

Aiming at solving the vehicle routing problem, an improved genetic algorithm based on fuzzy C-means clustering (FCM) is proposed to solve the vehicle routing problem with capacity constraints. On the basis of genetic algorithm, the FCM algorithm is used to decompose the large-scale vehicle routing optimization problem into small-scale subproblems, which can effectively improve the efficiency of the algorithm. At the same time, a generation method of the initial solution to CVRP problem is designed. The improved algorithm has good robustness and can also reduce the possibility of falling into local optimization in the search process. Finally, a simulation example is provided to verify the efficiency and superiority of the proposed algorithm.

Vehicle Routing Optimization for Pandemic Containment: A Systematic Review on Applications and Solution Approaches

The global spread of the novel coronavirus (COVID-19) has accounted for many deaths. The effective containment of the current COVID-19 epidemic calls for a fast and sustainable delivery strategy to minimize the impact of this crisis. As such, this study aimed to conduct a comprehensive review of research on the vehicle routing problem (VRP) from a sustainable viewpoint during the pandemic and explore viable delivery solutions that may aid in the containment of the COVID-19 pandemic. Through a systematic review of the selected articles, four broad themes of pandemic containment measures from the delivery aspect were identified: efficient pharmaceutical delivery strategy, contactless distribution, sustainable waste transportation strategy, and isolated and quarantine vehicle scheduling. Following that, the methodology utilized to execute the containment measures were analyzed, research gaps were hightlighted, and possibilities for future studies were suggested. In summary, the goal of this research is to provide an overview of the literature on the application of VRPs in pandemic control and to assist academics and practitioners in learning more about the performance metrics, models, and solution techniques utilized in pandemic control delivery operations.

Industrial information integration method to vehicle routing optimization using grey target decision

Industrial information integration can help companies develop supply chain systems and is a good framework for vehicle routing problem. To address the problems of excessive energy consumption, environmental pollution caused by carbon dioxide emission, and timeliness in the transportation process, a multi-objective vehicle routing optimization model was proposed to minimise transportation, carbon emission, and time window penalty costs. In the proposed model, an elitist nondominated sorting genetic algorithm was used to obtain the Pareto optimal solution. Furthermore, the optimal distribution path was selected by using multi-objective grey target decision-making according to entropy value. Finally, a real distribution case was analysed, and the calculated optimal path was compared with the actual path of the case to verify the feasibility of the proposed model and algorithm. The results showed that the proposed model and algorithm can be effectively used to reduce the target cost.

Sustainable multi-trip periodic redesign-routing model for municipal solid waste collection network: the case study of Tehran.

Daily transportation of wastes due to its environmental, financial, and social aspects has been considered a challenging issue in developing countries' municipal solid waste management systems. The location of transfer stations as intermediate nodes in municipal solid waste management network affects optimal collection frequency. A sustainable multi-period and multi-trip vehicle routing problem integrated with relocation models was developed to redesign the intermediate transfer stations and find optimal vehicle routes and the best collection frequency for each municipal solid waste generation point. Regarding the social aspects of a sustainable solid waste management system, an extended social life cycle assessment methodology for redesign and routing operations was developed based on the UNEP guidelines. The social life cycle assessment methodology evaluated the probable social effects of the system throughout the entire life cycle using an iterative policy. In this study, selected impact subcategories and inventory indicators for the routing and redesign system were utilized to quantify the system social score. Besides, the developed model was solved for different problem instances. The results indicated that system social score was affected by collection frequencies decisions, redesign policy, and the number of demand nodes. Furthermore, the model was applied to a real-world case study resulting in a total cost reduction of 66% that occurred by a 86% reduction in weekly traveled distance and a 12% decrease in routing social score.