Optimization Of Vehicle Routing Problem Research Articles

Retracted: Multipoint Distribution Vehicle Routing Optimization Problem considering Random Demand and Changing Load

Retracted: Multipoint Distribution Vehicle Routing Optimization Problem considering Random Demand and Changing Load

Optimization of vehicle routing problem using guided local search and simulated annealing

Transportation concerns in the supply chain are important because they have the potential to greatly raise logistical requirements. The proposed solution to the issue is the vehicle routing problem. The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) model will be implemented in this study using the metaheuristic methodology together with the Guided Local Search (GLS) and Simulated Annealing (SA) approaches for case studies. Python 3.10 software was used to analyze the SA and GLS, and the Google OR-Tools module was used. Implementing the SA and GLS algorithm based on data from the case study in a logistics company involved in the distribution of chicken feed and gas is the goal of this project. By doing so, it will be possible to determine whether the current situation is ideal or if it could be improved to reduce the number of vehicles used, find the best route, lower shipping costs, and increase efficiency. The findings of this study show that when compared to the GLS algorithm, research employing SA produces better outcomes in terms of cost and route management. According to actual data, SA increases income by an average amount between 3% and 10%.

Multiobjective Optimization for Vehicle Routing Optimization Problem in Low-Carbon Intelligent Transportation

The work aims to reduce the energy consumption and carbon emissions generated during the urban logistics transportation and distribution and make the actual path planning flexible. Based on Vehicle Routing Problem (VRP), the routing problem of distribution vehicles is optimized under satisfying customers’ cargo demand and time requirements. Because Non-dominated Sorting Genetic Algorithm (NSGA-II) reduces the complexity of non-inferior sorting genetic algorithm and is characterized by fast running speed and good convergence, it is deeply improved. NSGA-II algorithm based on Multifactorial Evolutionary Algorithm (MFEA) (M-NSGA-II) is proposed. In terms of the solution of the stability of the optimal values of four target functions, including distribution cost, customer satisfaction, fuel conservation, and carbon emission, the lowest distribution costs of M-NSGA-II algorithm in ten experiments were all lower than those of other three standard algorithms. The solution duration of M-NSGA-II algorithm was 85.2s and the corresponding average frontier value amounted to 20. The multi-objective path optimization model designed is of great value for reducing carbon emissions under satisfying customers’ cargo demand and time requirements.

Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

The object of this research is a combinatorial optimization problem arising in the problem of the route of goods delivery vehicles. In this study, the proposed method for solving combinatorial optimization problems consists of several stages: Data Cleaning, Data Preprocessing, K-NN and Cavacity Vehicle Routing Problem model. The results show that the machine learning approach can optimise combinatorial optimization problems, especially in generating vehicle route points and delivery capacity. The characteristics in determining vehicle routes by considering latitude and longitude points. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification from the influence of the number of 'nodes' on vehicle routes with machine learning. The purpose of the model in general is to gain an understanding of the mechanism in the problem so that it can classify unbalanced vehicle route data based on Jalur Nugraha Ekakurir delivery routes. So that with the availability of the model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. The results of research with machine learning models and vehicle routing problems with testing K values 11, 13, 15. Where it has a percentage of K=11 accuracy 57.3265 % and K=13 accuracy 57.3265 % and K=15 accuracy 81.8645 %. From the test results with odd K values have better accuracy and the K 15 K=15 value is better with a percentage of 81.8645 % compared to K 11 K=11, and 13 K=13. As a result, the developed model in terms of accuracy of the cavacity vehicle routing problem model has an accuracy of 93.80 % and the time series achieves an average precision of 93.31 % and with a recall value of 93.80 %. The results obtained can be useful in developing a more modern model, Cavacity Vehicle Routing Problem with Machine Learning

Integrated scheduling problems of open shop and vehicle routing using an ensemble of group teaching optimization and simulated annealing

Production and distribution are two parts of importance in supply chains. Yet, seldom attention is given to an integrated production and distribution in the case of open shop scheduling and vehicle routing problems. This article addresses an integrated scheduling problem of open shop and vehicle routing optimization, in which the jobs are processed in an open shop, and subsequently are delivered to customers at diverse places. First, a mixed integer programming model is developed to define the problem with minimizing maximum completion time. Second, an ensemble of group teaching optimization and simulated annealing methods is devised to optimally handle the considered problem. A heuristic rule is designed to generate a high-quality initial solution, while a simulated annealing method considering key machines is proposed to reinforce exploitation abilities. A nearest insertion method is used to make delivery decisions of vehicles. At last, the sensitivity analysis of user parameters in the developed approach is executed. Via solving a set of benchmark instances, we carry out extensive comparison experiments. The formulated model is verified by a mathematical programming optimizer CPLEX and the devised approach is compared with four meta-heuristics. The results and discussions confirm the strong competitiveness of the developed model and method for solving the concerned problems.

드론을 포함한 3종 운송수단의 배송경로 최적화

This study addresses the vehicle routing optimization problem with three nested transportation modes, consisting of ships, trucks, and drones. A typical nested-three-mode vehicle routing problem appears in delivery systems that include areas of sparsely scattered, varying-size islands. In the problem, drones play an important role in achieving high efficiency in the entire delivery system, of their high-speed accessibility. In this paper, we present a mathematical model for the problem and explore its potential usability as a heuristic. For the usability check-up, a geographic data set from the island area of Tongyeong in Korea is used in an illustrative scenario for disaster relief goods distribution. Our limited computational experiment shows promise in the proposed optimization model as a heuristic with a preset computation time limit when compared with greedy heuristics.

Optimization of Cold Chain Distribution Route with Mixed Time Window considering Customer Priority

In order to study the mixed time window vehicle routing optimization problem based on customer priority, a customer differentiation management strategy based on customer priority is proposed. Combined with the main factors affecting customer priority evaluation and the characteristics of vehicle routing problem with mixed time windows, a comprehensive evaluation index affecting customer priority was first established and DBSCAN clustering algorithm was used for clustering analysis of customer priority to solve the optimization problem of cold chain distribution route considering customer priority. Fuzzy time window of refrigerated vehicles was then constructed with trapezoidal fuzzy number, and a mathematical programming model was built with an objective function for minimizing the sum of fixed, green, penalty, refrigeration, and cargo damage costs. Two scenarios of out-of-stock and not-out-of-stock were designed. Finally, an improved genetic algorithm was used to solve the model, and the rationality of the model was verified through a case of imported fruit distribution in Xiamen City. Results showed that the proposed method can effectively solve the routing problem of refrigerated trucks considering customer priority. Moreover, the findings of this study can provide a new approach for solving the routing optimization problem of refrigerated trucks considering customer priority.

Multipoint Distribution Vehicle Routing Optimization Problem considering Random Demand and Changing Load

In the distribution scenario, the using cost of vehicles is closely related to energy consumption, and the energy consumption rate of a vehicle is closely related to the size of its load. The traditional vehicle routing optimization model takes the shortest distance as the optimization goal when the customer demand is determined, while the influence of the random demand and the changing load on the energy consumption and cost of vehicles in the process of distribution is ignored. Therefore, in this paper, load varying vehicle routing problem with stochastic demands (LVGVRPSD) model is proposed with the goal of minimizing transportation energy consumption and considering the load variability and the randomness of customer demand. K-means clustering algorithm is combined with ant colony optimization (ACO) to solve the problem, and the constraint of risk probability is introduced to describe the vehicle overload problem. Examples in the standard vehicle routing problem test data set are provided and analyzed. LVGVRPSD is also compared with the traditional capacitated vehicle routing problem (CVRP) model. The case study results show that the vehicle energy consumption can be reduced by 2% in the model that considers changing load compared to the model that does not consider changing load. The results illustrate that the method of path optimization is more advantageous and reasonable in the pursuit of reducing energy consumption, when the changing load and the random demand of customer are considered.

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.

Meta Heuristic Algorithm for Vehicle Routing Problem with Uncertainty in Customer Demand

The Vehicle Routing Problem is an optimization problem with number of applications in the transportation field. In this paper, Vehicle Routing Problem (VRP) with uncertainty in customer demand is considered. Often in real world situation, the information on demand on each customer is not accurate enough. Here, in this article we are assuming that the customer demands in each time period are uncertain and are considered as fuzzy variables. This kind of problem is known as an NP-hard problem. The main purpose of this article is to give conceptual and practical idea on how fuzzy logic with meta heuristic algorithm can be implemented to improve the optimization of vehicle routing problem with uncertainty in customer demand. To solve this problem, a hybrid Meta heuristic algorithm (HMHA) including clustering is proposed, with the objective of determining an optimal route to minimize the total cost.

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.

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 Problem: A Study on Capacitated Vehicle Routing Problem

Vehicle Routing Problem is based on the generalization of Travelling Salesman Problem and it is a combinatorial optimization problem. Vehicle Routing Problem can have multiple routes to traverse whereas Travelling Salesman Problem has only one route. Route optimization is a complex task when the problem has more number of destination points. The objective of solving the vehicle routing problem is to deliver the products or goods in a right destination with some time management. In order to achieve these constraints, an effective route planning is required. There are different types of routing problems which are solved using so many algorithms. Among those types, one of the best known problems is Capacitated Vehicle Routing Problem. The Capacitated Vehicle Routing Problem deals with the total distance travelled, total cost and some other special parameters on a demand basis. Many authors have proposed the solutions to solve the Capacitated Vehicle Routing Problem in the recent years. This study mainly aims to expose the Capacitated Vehicle Routing Problem based papers where it reveals the problem statement, type of algorithm used and the parameters achieved from the findings are stated for all the mentioned articles.. The articles are taken from the recent periods.

Two-Stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS Algorithm

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the area scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam graph search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

Hybrid modified ant system with sweep algorithm and path relinking for the capacitated vehicle routing problem

Vehicle routing problem is a widely researched combinatorial optimization problem. We developed a hybrid of three strategies—a modified ant system, a sweep algorithm, and a path relinking—for solving a capacitated vehicle routing optimization problem, a vehicle routing problem with a capacity constraint. A sweep algorithm was used to generate initial solutions and assign customers to vehicles, followed by a modified ant system to generate new generations of good solutions. Path relinking was used for building a better solution (candidate) from a pair of guiding and initial solutions. Finally, a local search method—swap, insert, reverse and greedy search operations—was used to prevent solutions from getting trapped in a local minimum. Performance of the proposed algorithm was evaluated on three datasets from CVRPLIB. Our proposed method was at least competitive to state-of-the-art algorithms in terms of the total route lengths. It even surpassed the best-known solution in the P-n55-k8 instance. Our findings can lower the transportation cost by reducing the travelling distance and efficiently utilizing the vehicle capacity.

Logistics Route Optimization Based on Improved Particle Swarm Optimization

An improved particle swarm optimization (PSO) algorithm is presented by dynamically adjusting the inertia weight in the iterative process of PSO, and it is used to solve the problem of logistics route optimization. This algorithm can not only improve the convergence speed, but also avoid falling into local optimum. In the process of improving the standard algorithm, two methods are proposed to adjust the inertia weight value according to the number of iterations. One is piecewise linear decreasing, another is linear decreasing. The results show that linear decline is better than piecewise linear decline to achieve the purpose of optimization, which is more conducive to accelerate the convergence rate and enhance the ability of optimization. Through the simulation experiment of the specific vehicle routing optimization problem, the results show that after the improvement, the optimization performance is enhanced, the optimization speed is accelerated, and the complexity is not increased, which greatly improves the performance of the original algorithm.

Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions

In recent years, emergency events have affected urban distribution with increasing frequency. For example, the 2019 novel coronavirus has caused a considerable impact on the supply guarantee of important urban production and living materials, such as petrol and daily necessities. On this basis, this study establishes a dual-objective mixed-integer linear programming model to formulate and solve the cooperative multidepot petrol emergency distribution vehicle routing optimization problem with multicompartment vehicle sharing and time window coordination. As a method to solve the model, genetic variation of multiobjective particle swarm optimization algorithm is considered. The effectiveness of the proposed method is analyzed and verified by first using a small-scale example and then investigating a regional multidepot petrol distribution network in Chongqing, China. Cooperation between petrol depots in the distribution network, customer clustering, multicompartment vehicle sharing, time window coordination, and vehicle routing optimization under partial road blocking conditions can significantly reduce the total operation cost and shorten the total delivery time. Meanwhile, usage of distribution trucks is optimized in the distribution network, that is, usage of single- and double-compartment trucks is reduced while that of three-compartment trucks is increased. This approach provides theoretical support for relevant government departments to improve the guarantee capability of important materials in emergencies and for relevant enterprises to improve the efficiency of emergency distribution.

Reverse Logistics Vehicle Routing Optimization Problem Based on Multivehicle Recycling

In the reverse logistics recycling process, considering the diversity of recycling types, a variety of vehicle models are used to meet the reverse logistics recycling requirements. Therefore, this paper considers the fixed costs, transportation costs, carbon emission costs during driving, and time penalty costs in the reverse logistics process under multiple constraints such as multiple vehicle types and time windows in the recycling center. The recovery point is the recovery model, and an improved genetic algorithm is used to solve it. Drawing lessons from the idea of greed, the superiority of the initial population is improved; the entry matrix and the exit matrix of the iterative population are constructed, and the crossover operator is improved based on this, and the forward insertion method is introduced to design the hybrid crossover operation to speed up the population. At the same time, the improvement of the mutation operator is proposed to increase the diversity of the population. The experimental results show that the multivehicle mixed delivery mode can reduce costs more effectively than single-vehicle models, and the improved genetic algorithm has better convergence and stability.

Optimization of vehicle routing problem for emergency cold chain logistics based on minimum loss

Based on the shortest scheduling time of emergency cold chain logistics resources, the mathematical model of emergency cold chain logistics scheduling, including the loss of the vehicle, refrigeration consumption and damage of goods over time, is constructed. Considering the traffic situation of the actual distribution route, based on the network optimization idea and Baidu map API, the heuristic algorithm is used to solve the emergency cold chain logistics scheduling model. The proposed optimization model adjusts the pheromone updating strategy adaptively which can balance the convergence rate and diversification of solutions. Combined with Pareto Local Search (PLS) is to avoid local-best solutions and accelerate the search so as to obtain the uniform Pareto optimal frontier and keep the diversity of Pareto solution set. The experiments show that our method has strong applicability and potential advantages in the rescue of sudden crisis events, and has important practical significance and application value.

Optimization of Transportation Routing Problem for Fresh Food by Improved Ant Colony Algorithm Based on Tabu Search

With the continuous improvement of people’s living standards and their increasing demand for fresh food, the cold chain logistics industry has developed rapidly. One of the biggest challenges to today’s cold chain logistics is to offer fresh food while minimizing CO2 emissions. The fresh degree and CO2 emissions are involved in the vehicle routing optimization problem in the cold chain logistics. In order to meet the quality requirement for fresh agricultural products and low carbon logistics, a novel routing optimization model considering the costs of quality deterioration and carbon emissions (Low Carbon and Freshness Degrees Vehicle Routing Problem (LCFD-VRP)) for cold chain distribution was established in this study. This model takes into account the fixed cost, fuel cost and time window penalty cost. An improved ant colony algorithm (IACA) is used to optimize the whole vehicle distribution routing with its strong global search ability. Tabu Search (TS) algorithm is used to search the single vehicle distribution routing with its good local search ability. An IACA combined with TS (IACATS) was proposed to solve the above LCFD-VRP model. The practicability of the model and the effectiveness of the above improved algorithm are verified using a real case study. The results of Zhoushan Dayang Refrigerated Logistics Co., Ltd. showed that, compared with the traditional algorithm, IACATS could reduce the dispatching of two refrigerated vehicles, thus lowering the total cost by 4.94%, shortening the actual transportation distance by 5.50% and cutting the total CO2 emissions by 8.9%. Therefore, the LCFD-VRP model can effectively help to achieve the low carbon emissions, multi-variety and low-cost distribution of fresh agricultural products. The proposed model and IACATS algorithm would be used to optimize VRP in cold chain enterprises. The results of this study also provide management suggestions for cold chain enterprises to effectively balance economic cost and environmental cost.