Vehicle routing problem based on object-oriented discrete event simulation

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 field of grain logistics transport vehicle route has seen the development of a variety of mathematical techniques, and the practical application of these techniques has depended on the use of spatial information, such as regional allocation information of road network. Vehicle Routing Problem (VRP) is regarded as the key of grain logistics distribution. In this paper, firstly, focus on the grain logistics VRP with time windows, then VRP model is successful constructed. In order to reduce the cost of grain logistics distribution, to combine the VRP model to geographic information system (GIS) by analyzing the VRP model. GIS takes into account for grain logistics, and the Particle Swarm Optimization (PSO) is used for the identification of optimal routes. And the optimal route for grain distribution is urgent since it has been calculated that, of the total cost for transportation. It improves the efficiency of grain logistics distribution and gets a better effect.

In actual distribution process, the traffic flow varies with time, and each consumer has multiple fuzzy windows. To minimize the total distribution cost and mean consumer dissatisfaction, this paper sets up a vehicle routing problem (VRP) model with multiple fuzzy time windows, based on time-varying traffic flow. In addition, the Ito algorithm was improved based on time-varying traffic flow. The model and algorithm were verified through example simulation, in comparison with ant colony optimization (ACO). During the simulation, the improved Ito algorithm effectively reduced the distribution cost and consumer dissatisfaction, and outperformed the ACO in solving efficiency and solution quality. The results fully demonstrate the feasibility and effectiveness of the proposed algorithm. The research findings provide a desirable solution to VRPs with multiple fuzzy windows and time-varying traffic flow in the real world.

Automotive industry is very competitive and efficiency is needed in key processes such as logistics in all companies at the supply chain. Using optimization models to increment the efficiency of inbound logistics, such as the in-plant transportation systems, can bring competitive advantages to these companies. This work presents a Vehicle Routing Problem (VRP) model to support and optimize the in-plant transportation system routing, Milk-Run, at the worldwide manufacturing plant of manual assembly in an automotive company, where the routing was planed manually, and too much time and resources were used on this task. At the present, 4 or 5 routes were necessary to supply and collect both raw material and finished items in the plant with 21 workstations. A VRP model was developed into a decision support tool using Integer Programming. The IBM ILOG CPLEX 12.0.8 was used to run the developed model using the production plan of two months of 2018. The results point out an optimal number of 2 milk-runs, a reduction of circa 50%, allowing the freed resources to be used in other production areas of the plant.

Resource allocation methodology based on object-oriented discrete event simulation: A production logistics system case study

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in the context of VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap by providing an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures of equity, and point out important connections between their properties and the properties of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e., composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions. We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis remain valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research on equity in logistics. The data are available at https://doi.org/10.1287/trsc.2017.0744 .

The model and algorithm for Vehicle Routing and Scheduling Problem (VRSP) in picking up and delivering customers to airport service were presented in this paper. VRSP is an extension of the class Vehicle Routing Problem (VRP) with more resource constraints. Mathematical models are described for the VRSP with minimizing the cost based on low carbon emissions. The Bi-directional Optimization Heuristic Algorithm (BOHA) is developed for this model. The results verified the effectiveness of the model and Algorithm. The proposed model and algorithm can provide quantitative support and help the air ticket company decision-maker develop economical, punctual shuttle service with high satisfaction.

Transportation is part of the logistics activities that are very important and cost the most. The problem faced in relation to distribution is determining vehicle routing problems. Transportation contributes to the increase in pollution and greenhouse gas emissions that result in global warming. Therefore, a vehicle routing problem model is needed in the electric fleet. VRP plays an important role in goods distribution activities that aim to optimize distribution activities by minimizing costs and time. The vehicle routing problem in electric fleets also takes into account battery capacity, charging time and charging station location.

The problem in transportation is the Vehicle Routing Problem (VRP), the delivery process of bread production at PT. XYZ is known to have not implemented an optimal distribution system. The purpose of writing this thesis is to create a VRP model for the distribution route in PT XYZ, completing it using the saving matrix, nearest insert and nearest neighbour methods. The saving matrix method uses the saving value, the nearest insert method adds a new point to the existing route, while the nearest neighbour method considers the nearest distance. The calculation carried out using the VRP model with the saving matrix method resulted in a distance saving of 82.284 km, then the nearest insert resulted in a distance saving of 93.829 km, and the nearest neighbor resulted in the largest distance saving, namely 94.406 km, with vehicle route 1 Factory-Wonokerto-Pringlangu-Landungsari-Poncol-Factory, vehicle route 2 is Factory-Grogol-Talang-Adiwerna-Slawi-Factory, vehicle route 3 is Factory-Poso-Randugunting-Kraton-Factory, and vehicle route 4 is Factory-Mejasem-Kemantran-Brebes-Factory. The proposed distribution route received the largest decrease, which was 94.406 km, initially the company's mileage was 285.414 km. The calculation of the route saving percentage is 33% and saves costs of Rp. 235,284 thus the optimal route.

The Vehicle Routing Problem's focal point is no longer a single objective to get a shortest routing or satisfy customer's time demand, but a multiple objective which include nearly every aspect in the distribution network such as the cost of delivery, shipping time, and distance of transportation. Most of these aspects are very significant in the delivery network. Classic VRP models cannot take real-time reaction to the traffic jam. In this paper, a two-layer model including multiple objective and real-time road information is established. The genetic algorithm, NSGA II and ESGA, is used to optimize this model. This optimization algorithm could find the optimal result very soon.

Driven by the supply chain, suppliers, manufacturers and warehouses are working more closely together for improving service quality. However, tremendous cost may incur in the supply chain if transportation is not planned properly and efficiently, which frustrates enterprises in the intense market. In this paper, we present a model of vehicle routing problem in complex supply chain (VRPCSC) and propose an intelligent algorithm called hybrid chaotic discrete bat algorithm with variable neighborhood search for minimizing the purchase cost of materials, processing cost, and delivery cost along the path from suppliers, to manufacturers and warehouses in the vehicle routing problem. Based on the principles of bat algorithm, a discrete chaotic initialization strategy (DCIS) and a variable neighborhood search (VNS) are adopted to enhance the convergence capacity. Finally, two sets of experiments are conducted, which show that the proposed algorithm can solve the VRPCSC effectively.

It is necessary to consider the maximum load of truck and replenishment location in the regions-integrated distribution routes planning. After stating the assumptions, a mixed integer nonlinear programming model was built to solve the vehicle routing problem (VRP) based on the strategy of replenishment on the way with regions-integrated. Since the problem is NP-hard and existing algorithms can not be used directly, according to the model's specific configuration, a heuristic algorithm was developed based on the geometrical analysis, by which the replenishment locations on the way and the distribution route can be ascertained by secondary search and batch insert. Finally, a numerical example was given to demonstrate the algorithm operation, it showed that the decision model was practical and the algorithm was valid

In order to address the timely delivery of distribution and the internal resource constraints of distribution enterprises, this paper constructed a optimization model of vehicle routing problem (VRP) with multi-constraints, and added test subroutines (such as mileage restrictions, time windows constraints, the number and capacity-limited of vehicles) and an effective mechanism to avoid premature convergence to the ant algorithm to solve the VRP. Finally, a numerical example is presented. Comparison is made between the improved ant algorithm proposed by the paper and saving algorithm, distribution agorithm and genetic algorithm. The experimental results showed that the model and the approach was an effective solution to VRP.

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.

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.