Vehicle Routing Problem Problem Research Articles

Decision-Making Support in Vehicle Routing Problems: A Review of Recent Literature

The Vehicle Routing Problem (VRP) involves a set of customers with known locations, each having a certain demand for goods or services. There is also a fleet of vehicles available, each with limited capacity and often a fixed starting point. With the aid of mathematical programming tools, this paper offers an overview of the most recent VRP research. This study also examines the algorithms for solving VRP models and categorizes them in terms of their application areas. For these reasons, related publications that appeared in the international journal have been compiled and studied. According to the literature review, multi-criteria decision-making (MCDM) techniques have yet to constitute the most mathematical programming methods used to solve the VRP problem.

An integrated vehicle routing model to optimize agricultural products distribution in retail chains

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.

Optimization Model for Electric Vehicle Routing Problem with Two Charging Options

The use of electric vehicles recently is an alternative way to lower pollutants and the emissions of carbon dioxide resulting from the usage of motor vehicle fuel oil. The limited battery capacity of this electric vehicle is a problem for its users. Vehicle routing problem (VRP) is a problem of integer programming and combinatorial optimization that is frequently used in planning and decision-making processes. One application of this issue is to find the best path for delivering items from a corporation to customers. VRP problems are frequently utilized in order to reduce internal expenditure. Model optimization in this paper uses two rehcarging options, recharging and swapping battery. The purpose of this paper is to present a VRP optimization model for electric vehicle routing problems to find the best route option that minimizes the total of the fees for fixed vehicles, transit, charging, battery changing, and waiting.

Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming

Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which require fast algorithms to generate solutions of acceptable quality. The basis for many VRP approaches is a heuristic which builds a candidate solution that may subsequently be improved by a local search procedure. Since there are many variants of the basic VRP model, specialised algorithms must be devised that take into account specific constraints and user-defined objective measures. Another factor is that the scheduling process may be carried out in dynamic conditions, where future information may be uncertain or unavailable or may be subject to change. When all of this is considered, there is a need for customised heuristics, devised for a specific problem variant, that could be used in highly dynamic environments. In this paper, we use genetic programming (GP) to evolve a suitable dispatching rule to build solutions for different objectives and classes of VRP problems, applicable in both dynamic and stochastic conditions. The results show great potential, since this method may be used for different problem classes and user-defined performance objectives.

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

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.

Mobile Application Development to Solve Vehicle Routing Problems in Marketing or Tour Trip Planning

The vehicle routing problem (VRP) is a combinatorial optimization problem faced by transportation services related to pick up or delivery, such as industrial raw materials distribution, tour and travel, or travel routing problems in general. VRP is an NP-hard problem where the higher the dimensions of the problem will have a higher computational complexity. Without realizing it, VRP problem are often encountered every day. Therefore, it will be very useful if VRP solver is implemented in mobile application media. So, the aim of this work is developing a mobile application to get the shortest path and minimal cost in VRP problem. It is integrated by both Mapbox API and Google Maps API to get a real distance for modeling problem. The result show that the developed application can run well in all possibility condition.

Two‐Layer Location‐Routing Problem Based on Heuristic Hybrid Algorithm

Location‐routing problem (LRP) thoroughly considers location allocation problem (LAP) and vehicle routing problem (VRP) which has been an integral part applied in modern logistics. A number of researchers at home and aboard have put forward their views by establishing fine models. On the basis of studying the previous research results by classification, summary, and comparative analysis, this study hence proposes a new solution‐fuzzy clustering model and algorithm to resolve two‐layer location‐routing problem based on a heuristic hybrid algorithm: Designing a hybrid genetic and simulated annealing algorithm (GASA) to optimize the initial value of the fuzzy C‐means clustering algorithm (FCM); considering the roving visit characteristics of vehicles to design the path by employing a special VRP problem—the multiple traveling salesman problem (MTSP). Theoretical analysis and experimental results show that the algorithm used in this study has the advantages of fast convergence speed and less iterations, which significantly improve the quality of the initial solution of FCM in LAP, shorten the vehicle patrol cycle in VRP to a great extent, improve the vehicle utilization, and save the vehicle patrol costs. A specific example is programmed by MATLAB to verify the feasibility of this method.

Modeling and solving a multi-trip multi-distribution center vehicle routing problem with lower-bound capacity constraints

Vehicle routing problem (VRP) for delivering goods from distribution centers to customers is one of the main operations in logistics. Optimizing route plans for vehicles allows companies to save a huge amount of operational costs. The VRP problem is also one of the most studied problems in the domain of operations research. There are several variants of the VRP problem that have been considered in the literature. This paper proposes a new variant of the vehicle routing problem taking into account most of the well-studied features, especially with a new constraint on the lower bound of the capacity of vehicles which has not been investigated in the literature. The problem requirements come from one of the biggest dairy distribution companies in Vietnam. With over 1000 customer points included in a plan on average, the company takes at least one working day to make a route plan. We formulate the considered problem as a mixed-integer linear programming problem, analyze the challenges of the lower-bound capacity constraints and propose an adaptive large neighborhood search framework for solving it. Experimental results on large realistic and randomly generated instances show the efficiency and the applicability of the proposed method. The application of the proposed method led to the rapidity in generating the solution; this task that used to take one day is decreased to just two hours.

Routing optimization method of waste transportation vehicle using biological evolutionary algorithm under the perspective of low carbon and environmental protection

Reasonably and effectively formulating the best route for urban waste transportation vehicle is particularly important for realizing low carbon and environmental protection of Green China construction concept. However, the current path planning method has shortcomings such as local optimization. In order to solve this problem, this paper aims at low carbon and environmental protection construction needs and proposes a routing optimization method of waste transportation vehicles based on improved ant colony algorithm. Firstly, the mathematical model of Vehicle Routing Problem (VRP) is constructed by considering transportation distance and carbon emissions cost. Then, network parameters in traditional ant colony algorithm are combined and optimized to realize self-adaptive update in the whole cycle. Furthermore, the neighborhood algorithm is used to iteratively optimize improved algorithm to avoid the defects of local optimization when solving VRP problem. Finally, the simulation results based on an actual dataset in North China show that the proposed method can achieve efficient and accurate optimal routing optimization for complex samples, and its solution stability index is 0.87 and the average deviation is 0.011, the lowest distribution cost after optimization is about 3,080 yuan, which are better than the comparison methods.

Parameter optimization of shared electric vehicle dispatching model using discrete Harris hawks optimization.

The vehicle routing problem (VRP) problem is a classic NP-hard problem. Usually, the traditional optimization method cannot effectively solve the VRP problem. Metaheuristic optimization algorithms have been successfully applied to solve many complex engineering optimization problems. This paper proposes a discrete Harris Hawks optimization (DHHO) algorithm to solve the shared electric vehicle scheduling (SEVS) problem considering the charging schedule. The SEVS model is a variant of the VPR problem, and the influence of the transfer function on the model is analyzed. The experimental test data are based on three randomly generated examples of different scales. The experimental results verify the effectiveness of the proposed DHHO algorithm. Furthermore, the statistical analysis results show that other transfer functions have apparent differences in the robustness and solution accuracy of the algorithm.

Analytics and machine learning in vehicle routing research

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered in several traditional research fields with very different, sometimes confusing, terminologies. This paper presents a first, comprehensive review of hybrid methods that combine analytical techniques with ML tools in addressing VRP problems. Specifically, we review the emerging research streams on ML-assisted VRP modelling and ML-assisted VRP optimisation. We conclude that ML can be beneficial in enhancing VRP modelling, and improving the performance of algorithms for both online and offline VRP optimisations. Finally, challenges and future opportunities of VRP research are discussed.

Vehicle Routing Optimization System with Smart Geopositioning Updates

Solving the vehicle routing problem (VRP) is one of the best-known optimization issues in the TLS (transport, logistic, spedition) branch market. Various variants of the VRP problem have been presented and discussed in the literature for many years. In most cases, batch versions of the problem are considered, wherein the complete data, including customers’ geographical distribution, is well known. In real-life situations, the data change dynamically, which influences the decisions made by optimization systems. The article focuses on the aspect of geopositioning updates and their impact on the effectiveness of optimization algorithms. Such updates affect the distance matrix, one of the critical datasets used to optimize the VRP problem. A demonstration version of the optimization system was developed, wherein updates are carried out in integration with both open source routing machine and GPS tracking services. In the case of a dynamically changing list of destinations, continuous and effective updates are required. Firstly, temporary values of the distance matrix based on the correction of the quasi-Euclidean distance were generated. Next, the impact of update progress on the proposed optimization algorithms was investigated. The simulation results were compared with the results obtained “manually” by experienced planners. It was found that the upload level of the distance matrix influences the optimization effectiveness in a non-deterministic way. It was concluded that updating data should start from the smallest values in the distance matrix.

Firefly Algorithm for Multi-type Vehicle Routing Problem

The problem of distribution is a problem that cannot be separated from the industrial world. These problems are because these problems have a considerable influence on the cost and level of service to consumers. Therefore we need a way so that the distribution process can run smoothly and on time. One way that can be done in the distribution process is to optimize vehicle routes so that the time spent serving two consumers is more efficient and the goods can get to consumers on time. The problem of optimizing vehicle routes is known as vehicle routing problem (VRP). VRP which aims to form an optimal route to serve consumers with capacity constraints and Type of Vehicle is called Multi-type vehicle routing problem (MTVRP). In solving the MTVRP problem, two methods will be combined namely the exact method and the heuristic method. The firefly algorithm has been widely used in solving VRP problems. The results are obtained total distance of 2488 km.

Extremely Fast “Solution” to the Large-Scale and Very Large-Scale Vehicle Routing Problem

A solution to the vehicle routing problem (VRP) is presented that takes only quadratic space, <i>O(n²),</i> and quadratic time, <i>O(n²)</i>, if <i>n</i> is the number of stops on a route. The input is assumed to be a list of stops of length <i>n</i> in longitude, latitude format. The output is an origin-destination (OD) matrix of size <i>O(n²)</i>, which takes <i>O(n²)</i> time to build. The element <i>(i, j)</i> in the matrix is the approximate driving distance between stop <i>i</i> and stop <i>j</i> on the route. Each approximate driving distance takes constant or <I>O(1)</I> time to compute. (The approximate driving distance appears in previous work by the author, published in URISA GIS-Pro ‘19 and CalGIS 2020.) This OD matrix is well-suited for solving large-scale and very large-scale VRP problems, since computing approximate driving distances is lightning fast. For instance, using real-world data, it took less than one (1) second to produce a route with 5,156 stops. The OD matrix can be used with any exact or approximation algorithm to find a route, including the nearest-neighbor approximation algorithm: Starting at an origin, the next closest stop is visited repeatedly, ending at the destination once all stops have been visited. Determining the next stop to visit takes linear or <i>O(n)</i> time to compute, and this is done <i>O(n)</i> times. This solution to the VRP is a polynomial-time, <i>O(n²)</i>, approximation; it is not exact, but is extremely fast.

ROUTING OF VEHICLES USING THE METAHEURISTIC ALGORITHM

The main goal of the proposed research is to solve the problem of vehicle routing using genetic algorithms. Vehicle Routing Problem (VRP) is an NP-complete complex combinatorial problem. With a large amount of input data in a VRP problem, it is very expensive to find the most optimal solution. Genetic algorithms offer the most optimal solution in a short period of time. This article discusses genetic algorithms based on the mechanism of evolution for finding the optimal route by metaheuristic methods. The aim of the work is to minimize the time needed to find the most acceptable optimal solution to the problem, as well as to develop metaheuristic methods.

Vehicle Routing Optimization for Surplus Food in Nonprofit Organizations

Non-profit organizations mitigate the problem of food insecurity by collecting surplus food from donors and delivering it to underprivileged people. In this paper, we focus on a single non-profit organization located in Makkah city (Saudi Arabia), referred to as Ekram. The current surplus food pickup/delivery and operational routing model of Ekram orga-nization have several apparent deficiencies. First, we model the surplus pickup/delivery and operational routing model as a vehi-cle routing problem (VRP). Then, we optimize the pickup/delivery of different types of food groups through the different available routes. Finally, we utilize the formulated VRP problem by minimizing the total route distances. Our proposed model ensures reduction of the total time and effort necessary to send the collecting vehicles to the donors of surplus food.

A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants

Recently, a taxonomic review of the Vehicle Routing Problem (VRP) literature published between 2009 and June 2015 stated that most of the surveyed articles use metaheuristics for solving the problem and its variants. As extension to that work, this paper serves two-fold objectives: first, classifying the VRP and its variants solved using metaheuristic algorithms; second, investigating the contribution of each metaheuristic algorithm in solving the VRP problems. Based on a metaheuristic classification, we classify 299 VRP articles published between 2009 and 2017. The results are analyzed to reveal the usage trends of the algorithms and the solved VRP variants for showing the ones that are most popular, and those that are promising topics for future research.

Route Optimization of Malatya Metropolitan Municipality Pesticide Vehicles

In today's competitive environment, businesses attach importance to transfer products, services and related information between supply and consumption points in order to meet increasing customer demands, in accordance with minimum cost, optimum route and customer satisfaction. Due to its importance in the economy, the Vehicle Routing Problem (VRP), which has been working hard on academics for the last 50 years and is a sub-topic of logistics management, constitutes a significant part of the total distribution cost in the businesses and financial expenditures are serious. In this study, VRP and Chinese Postman Problem (CPP) is introduced, optimal routes of Malatya Metropolitan Municipality pesticide vehicles is determined by using Hierholzer & Floyd Warshall Algorithms and Excel - Solver and solution is compared.

50th Anniversary Invited Article—Future Research Directions in Stochastic Vehicle Routing

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.

Vehicle routing problem with fuel consumption and carbon emission

In this paper, we study a vehicle routing problem (VRP) with the consideration of fuel consumption and carbon emission. To be specific, we incorporate fuel cost, carbon emission cost, and vehicle usage cost into the traditional VRP problem and establish a low-carbon routing problem model. Based on the route splitting method, we develop an improved tabu search algorithm named RS-TS for solving the model. In the RS-TS algorithm, we introduce a novel route encoding and decoding algorithm named WSS, in which three neighborhood search methods are applied. By numerical studies we show that the RS-TS algorithm is effective with a satisfactory solution and can better reveal the relationships among distance, fuel consumption, travel time, and other parameters. Moreover, we also show that the route and vehicle arrangements based on fuel consumption and carbon emission are both economic and environmentally friendly.