Workload Equity in Vehicle Routing Problems: A Survey and Analysis

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.

This The bread industry has seen advancements in distribution, making bread products easier to transport. With the implementation of delivery orders and effective picking processes, the bread industry can streamline the shipping process and make it easier for customers to obtain bread products. To address inefficiencies in distribution, companies can manage data related to bread, orders, and sales. In this context, Toko Roti XYZ, which has three branches (Branch A, Branch B, and Branch C), faces challenges in the distribution process. The unpredictability of bread demand at these branches is a major cause of repetitive distribution. Forecasting bread demand from these branches was performed using fuzzy time series, moving average, and exponential smoothing methods to determine the quantity of products to be distributed. Subsequently, distribution optimization was carried out using the Vehicle Routing Problem (VRP) method to achieve an optimal delivery schedule that affects distribution costs with a minimum result. The forecasting results with the smallest Mean Absolute Percentage Error (MAPE) were obtained using the moving average method, with an error of 23% for large bread and 9% for small bread from Branch A, an error of 17% for large bread and 14% for small bread from Branch B, and an error of 22% for large bread and 14% for small bread from Branch C. In the VRP method, number 1 represents the depot/production place, number 2 represents Branch A, number 3 represents Branch B, and number 4 represents Branch C. The scheduling routes obtained were 1-3-2-1 and 1-4-1, with a maximum of two distributions for the two available vehicles. With these optimizations, Toko Roti XYZ was able to save distribution costs by Rp. 35,492 and reduce distribution time by 116 minutes per day compared to the previous condition. Additionally, the optimization allowed Toko Roti XYZ to reduce total carbon emissions by 5,342 kg CO₂ per year.

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.

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.

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.

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.

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.

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.

This paper presents two partition methods that speed up iterative search methods applied to vehicle routing problems including a large number of vehicles. Indeed, using a simple implementation of taboo search as an iterative search method, every best‐known solution to classical problems was found. The first partition method (based on a partition into polar regions) is appropriate for Euclidean problems whose cities are regularly distributed around a central depot. The second partition method is suitable for any problem and is based on the arborescence built from the shortest paths from any city to the depot. Finally, solutions that are believed to be optimum are given for problems generated on a grid. © 1993 by John Wiley & Sons, Inc.

The article presents the author's approach to planning vehicle routes in conditions of uncertainty, which is based on short-term forecasts of the travel time in the city's road network. The prediction model was created on the basis of the artificial neural networks. The uncertainty of the time value was modelled on the basis of the estimated values of the forecast and its errors, which were used to define the set of uncertainty for the parameters of the vehicle route planning model. The characteristics and advantages of the proposed methodology are illustrated on the basis of a simulation example based on a selected road network of the city of Kraków.

The capital’s traffic density affects the way waste is transported as well. The utilization of the vehicle routing problem (VRP) approach for determining the optimal route for the transportation of food waste from PT. ABC restaurants are covered in this study. PT. ABC owns a number of restaurants that are located on routes with traffic regulations that restrict the number of vehicles on the road. As a result, PT. ABC’s waste delivery vehicles must find new routes in order to continue moving waste. The transportation trucks return to the waste collecting place later than expected since they picked another route. It is anticipated that by streamlining the waste transportation route, the total time and distance required will be reduced. The VRP method is utilized to make the existing waste pickup route more effective and efficient. The suggested route was derived from the data processing procedure under two different scenarios: normal traffic conditions and vehicle restrictions. In the suggested route, 111 minutes would be needed to travel 36.3 km in normal conditions. In the meantime, the suggested trip took 126 minutes to complete and covered 46.8 km when vehicle restrictions were in place.