Vehicle Routing Optimization System with Smart Geopositioning Updates

The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to comprehensively analyze and summarize the literature related to VRP from 1959 to 2022 in the Web of Science (WoS) database. Firstly, according to the basic statistical information of the literature, the annual publications, the authors, their institutions and countries, the keyword co-occurrence, and the literature co-citation network are analyzed to comprehensively summarize and generalize the research on VRP and predict its future development trend. The results show that, in the past 60 years, there have been abundant changes in the research on VRP. The United States and China have made the most important contributions in the field of VRP. According to the WoS literature retrieval results and classification methods, the VRP models and their solutions are comprehensively classified, and the model solving algorithms are divided into exact algorithms, heuristic algorithms, metaheuristic algorithms, hyper-heuristic algorithms, machine learning, etc. The results show that the development of information computing technology plays an important role in research on the VRP problem, and dynamic VRP, hyper-heuristic algorithms, deep reinforcement learning, etc. are the future development directions of the VRP model and its optimization. The results of this research can provide help and guidance for beginners and scholars outside the industry to comprehensively understand the development and research hotspots of VRP.

The Vehicle Routing Problem (VRP) is a key to the efficient transportation management and supply-chain coordination. VRP research has often been too focused on idealized models with non-realistic assumptions for practical applications. Nowadays the evolution of methodologies allows that the classical problems could be used to solve VRP problems of real life. The evolution of methodologies allows the creation of variants of the VRP which were considered too difficult to handle by their variety of possible restrictions. A VRP problem that includes the addition of restrictions, which represent the variants in the problem, is called Rich VRP. This work presents an algorithm to optimize the transportation management. The authors are including a case of study which solves a real routing problem applied to the distribution of bottled products. The proposed algorithm shows a saving in quantity of vehicles and reduces the operation costs of the company.

With the current economic improvement, the logistics industries have been developed quickly. The logistics distribution of inter-city has been widely used in people's life. To shorten the delivery time and improve the economic benefits, how to choose the right path for the delivery personnel has become the most urgent problem. To solve this problem, the model of Vehicle Routing Problem (VRP) was proposed. Many researchers have applied various algorithms to solve VRP problems. However, because of the NP-hard characteristics of VRP problems, there is still have some room to improve the algorithms under the criteria of quality and time solutions. At the same time, there are many branches of VRP subproblems, among which the most common one is the VRP problem with capacity constraint (CVRP). As the CVRP problem belongs to the Combinatorial Optimization Problems (COPs), we select the ant colony algorithm to improve the CVRP. In this paper, based on the Max-Min Ant System (MMAS) algorithm, the utilization of mining information is strengthened by the elite ant path. The adaptive parameters are added to adjust the exploration and exploitation ability of the algorithm. Moreover, some appropriate constraints are added to the pheromone of the ant system to ensure the effectiveness of the algorithm. Based on the above strategy, the Adaptive Parameter for Max-Min Elite Ant System (APMMEAS) algorithm is proposed in this paper. The experimental results from the CVRP benchmark showed that the proposed APMMEAS algorithm is more competitive in quality and time solutions than in other ant colony series algorithms.

A new solution to vehicle routing problem (VRP) is proposed in order to overcome the disadvantages that the exact algorithms and heuristics algorithms are difficult to solve vehicle routing problem with multiple constraints. A simulation model of vehicle routing problem is built by combining the object-oriented technology and discrete event simulation technology. In this model, the events of distribution process and resources are described by the object-oriented approach. A variety of constraints which VRP problem involves can be processed in the simulation. As a result, the model has truly simulated complicated vehicle distribution process and simulation results can provide a basis for various optimization algorithms. The experimental results have validated the feasibility and effectiveness of this new method. As the simulation results can document the whole distribution process in detail, this method will have some significance to the practical application of vehicle routing problem.

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper presents a novel approach consisting of a two-stage optimization system to solve the problem of the last mile distribution of goods in the context of an Intelligent Transportation System (ITS). In particular, we deal with the Vehicle Routing Problem with Simultaneous Pickup and Delivery in conjunction with Door-to-Door operations (VRPSPD-D2D) to use vehicles travelling in the urban network to serve point-to-point shipping requests. The optimization problem is subject to some real-life constraints like vehicles’ capacity, urban traffic flows, pickup and delivery sequences. The proposed optimization algorithm is based on two honey bee-inspired metaheuristics: Artificial Bee Colony (ABC) at the first stage; Bee Colony Optimization (BCO) at the second stage. This approach proposes an efficient way in achieving the main objective of this research work: the minimization of the global transportation cost, subject to the demands of the customers and operational constraints. To test our approach, new instances of the VRPSPD-D2D are proposed, based on the Sioux-Falls City network. Results of the proposed Bee System are compared with two Genetic Algorithm-based approaches.

In response to the rapid increase in e-commerce, distribution services, and smart technology, businesses need to solve the vehicle routing problem (VRP) and its variants to improve service quality and cut operating costs. In this paper, we introduce the VRP with time windows and time costs (VRPTWTC), which is a new but practical VRP variant that adds time windows of customers and time costs of service constraints into the optimization of the traditional VRP problem. Furthermore, we propose a masking algorithm and combine it into our proposed end-to-end and data-driven deep reinforcement learning framework to solve the VRPTWTC. Experimental results show a shorter tour length and reasonable processing time than benchmarks both on a simulated dataset and a real enterprise-level dataset. Besides, violation of time window and total time cost, which is one of the most important criterions in our problem formulation, never occurred in our experiment under the real road connection conditions when using a real map API. To the best of our knowledge, our framework is the first to effectively and efficiently solve a VRP variant problem with practical time windows and time costs constraints with a real map API to calculate routes of VRP considering real road connection conditions on an enterprise-level dataset.

The open vehicle routing problem: Algorithms, large-scale test problems, and computational results

Stochastic vehicle routing, which deals with routing problems in which some of the key problem parameters are not known with certainty, has been an active, but fairly small research area for almost 50 years. However, over the past 15 years we have witnessed a steady increase in the number of papers targeting stochastic versions of the vehicle routing problem (VRP). This increase may be explained by the larger amount of data available to better analyze and understand various stochastic phenomena at hand, coupled with methodological advances that have yielded solution tools capable of handling some of the computational challenges involved in such problems. In this paper, we first briefly sketch the state-of-the-art in stochastic vehicle routing by examining the main classes of stochastic VRPs (problems with stochastic demands, with stochastic customers, and with stochastic travel or service times), the modeling paradigms that have been used to formulate them, and existing exact and approximate solution methods that have been proposed to tackle them. We then identify and discuss two groups of critical issues and challenges that need to be addressed to advance research in this area. These revolve around the expression of stochastic phenomena and the development of new recourse strategies. Based on this discussion, we conclude the paper by proposing a number of promising research directions.

This paper study the vehicle routing problem with time windows based on using a novel optimization algorithm (ICA). The vehicle routing problem with time windows is a combinatorial optimization problem. It copes with route scheduling and the distribution of goods from the distribution center to geographically dispersed customers by a fleet of vehicles with constrained capacities. We first build the mathematic model for the vehicle routing problem with time. Then apply the imperialist competitive algorithm to solve the specific problem, comparing the results produced by the PSO and GA algorithms, which proved the effectiveness of the ICA.

As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. Two of its important variants are the Dynamic Vehicle Routing Problem (DVRP) and the Green Vehicle Routing Problem (GVRP). The first one has become a challenging research topic in the last two decades, in which not all informations are known in advance, but are revealed as the system progresses. The second one is seen as a new application and solution in new logistics patterns, more specifically, for finding routes of vehicles to serve a set of customers while minimizing the total amount of CO 2 emissions, by increasing the loading rate and reducing the number of empty trips could reduce from 10% to 40% km traveled and therefore CO 2 emissions [1]. In this paper, we combine these two variants (DVRP and GVRP), where we try to minimize, in the dynamic environment, the greenhouse gas especially the carbon dioxide CO 2 , which is the immediate consequence of the depletion of the ozone layer and time duration. We will call this combination the Dynamic Green Vehicle Routing Problem (DGVRP). To study this new problem, we present the technique employed to estimate the amount of CO 2 emissions, the emission matrix and integrate them into the DGVRP model, then we propose the hybridization based on Ant Colony Optimization (ACO) algorithm with a Large Neighborhood Search (LNS) algorithm to solve our problem. The effectiveness of this approach is tested on a set of the dynamic green problems instances, which are adopted in this work from the static GVRP benchmark datasets.

In today's competitive market, manufacturers and service providers are continuously seeking ways to reduce costs and save time to gain a competitive edge. One of the most significant challenges they face is the vehicle routing problem (VRP), which is crucial due to its direct impact on the delivery time of services or products. Efficient vehicle routing not only enhances delivery performance but also optimizes the overall network, resulting in reduced operational costs. This study focuses on evaluating the VRP specifically for trucks while incorporating sustainability indicators into the analysis. The key sustainability indicators considered include social, economic, and environmental aspects. By integrating these indicators, the study aims to address multiple objectives simultaneously: reducing delivery time, minimizing costs, and mitigating the environmental impact of vehicle operations.The primary objective of this research is to minimize overall costs, fuel consumption, and route complexity associated with truck deliveries. Given the growing concern over environmental issues, there is a strong emphasis on improving methods to reduce greenhouse gas (GHG) emissions and streamline logistics processes. The research addresses these concerns by proposing a model that not only aims to enhance operational efficiency but also contributes to environmental protection and social responsibility.To achieve these objectives, the study employs advanced optimization techniques, specifically the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). These methods are utilized to solve the VRP while balancing the trade-offs between various objectives, such as cost reduction, fuel efficiency, and route optimization.The results of the study indicate that the proposed model successfully improves aspects of environmental protection and social responsibility while simultaneously addressing economic concerns. The integration of sustainability indicators into the vehicle routing problem provides a comprehensive approach to optimizing logistics operations, highlighting the importance of considering environmental and social factors alongside economic performance.Overall, this research contributes to the field by offering a refined model for tackling the VRP, with a focus on sustainability. The findings underscore the potential for optimization algorithms to drive improvements in both operational efficiency and environmental stewardship, ultimately supporting more sustainable and socially responsible practices in the transportation and logistics industry.

Facility location problem (FLP) and vehicle routing problem (VRP) are two of the most challenging issues in logistics. This paper presents an exploration of the multinode facility location-routing problem with realistic conditions. The disposal centers, transfer stations, connected collection sites, and unconnected collection sites are built into a new hierarchical model which is solved by Generate Algorithm (GA). Model costs include node construction cost, pipeline construction cost, transport cost, and transfer cost. This paper considers that the transportation is a bidirectional flow not a single flow; each pairs node in the area needs transportation; the dynamic routing selection method is used to determine the routes of unconnected collection sites. FLP and VRP can be both solved in this model. To illustrate the applicability of the model, a case study is presented and the results are discussed. The model in this paper can reduce the cost of the traditional underground logistics system by 6%~8% in experiments.

A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery