Optimization Of Vehicle Routing Problem Research Articles

A Vehicle Routing Optimization Problem for Cold Chain Logistics Considering Customer Satisfaction and Carbon Emissions.

Under fierce market competition and the demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emissions for better development. In order to simultaneously consider cost, customer satisfaction, and carbon emissions in the cold chain logistics path optimization problem, based on the idea of cost–benefit, this paper proposes a comprehensive cold chain vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer. For customer satisfaction, this paper uses the punctuality of delivery as the evaluation standard. For carbon emissions, this paper introduces the carbon trading mechanism to calculate carbon emissions costs. An actual case data is used with a cycle evolutionary genetic algorithm to carry out computational experiments in the model. First, the effectiveness of the algorithm and model were verified by a numerical comparison experiment. The optimization results of the model show that increasing the total cost by a small amount can greatly improve average customer satisfaction, thereby obtaining a highly cost-effective solution. Second, the impact of carbon price on total costs, carbon emissions, and average customer satisfaction have also been numerically analyzed. The experimental results show that as carbon price increases, there are two opposite trends in total costs, depending on whether carbon quota is sufficient. Increasing carbon price within a certain range can effectively reduce carbon emissions, but at the same time it will reduce average customer satisfaction to a certain extent; there is a trade-off between carbon emissions and customer satisfaction. This model enriches the optimization research of cold chain logistics distribution, and the study results complement the impact research of carbon price on carbon emissions and customer satisfaction. Finally, some practical managerial implications for enterprises and government are offered.

Proactive Two-Level Dynamic Distribution Routing Optimization Based on Historical Data

In view of the dynamic dispersion of e-commerce logistics demand, this paper uses the historical distribution data of logistics companies to study data-driven proactive vehicle routing optimization. First, based on the classic 2E-VRP problem, a single-node/multistage 2E-VRP mathematical model is constructed. Then, a framework for solving the proactive vehicle routing optimization problem is proposed in combination with the characteristics of the proposed model, including four modules: data-driven demand forecasting methods, customer clustering methods, proactive demand quotas and replenishment strategies, and vehicle routing optimization procedure. The significant feature of the proposed solution framework is that the response to dynamic customers is proactive rather than passive. The solution is applied to the distribution practice of a large logistics company in Chongqing. The results show that the proposed method has better dynamic scene adaptability and customer response capabilities in traffic limit.

A Joint Vehicle Routing and Speed Optimization Problem

Classic vehicle routing models usually treat fuel cost as input data, but fuel consumption heavily depends on the travel speed, which leads to the study of optimizing speeds over a route to improve fuel efficiency. In this paper, we propose a joint vehicle routing and speed optimization problem to minimize the total operating cost including fuel cost. The only assumption made on the dependence between fuel cost and travel speed is that it is a strictly convex differentiable function. This problem is very challenging, with medium-sized instances already difficult for a general mixed-integer convex optimization solver. We propose a novel set-partitioning formulation and a branch-cut-and-price algorithm to solve this problem. We introduce new dominance rules for the labeling algorithm so that the pricing problem can be solved efficiently. Our algorithm clearly outperforms the off-the-shelf optimization solver, and is able to solve some benchmark instances to optimality for the first time. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0810 .

Vehicle Routing Problem with Time Windows Study

Customer demands of logistics timeliness is getting higher and higher. The research on vehicle routing problem with time windows, can improve the level of logistics services. This paper studies the optimization of vehicle routing problem with time window based on the above background. Thesis assumes that customers tend to be in a specific time period to accept delivery services, beyond the delivery service time will give punishment cost of time, in order to reflect a decrease in the level of service. The paper takes the minimum of total cost as the target, establishes the nonlinear distribution path model, and in the model, corrects the errors of the time constraint in the literature. Finally, the practical case is introduced, the model is established, the genetic algorithm is used to solve the problem, and the optimal distribution path is obtained.

Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance.

Optimization of Vehicle Routing Problem with Time Window (VRPTW) for Food Product Distribution Using Genetics Algorithm

Food distribution process is very important task because the product can expire during distribution and the further the distance the greater the cost. Determining the route will be more difficult if all customers have their own time to be visited. This problem is known as the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW problems can be solved using genetic algorithms because genetic algorithms generate multiple solutions at once. Genetic algorithms generate chromosomes from serial numbers that represent the customer number to visit. These chromosomes are used in the calculation process together with other genetic operators such as population size, number of generations, crossover and mutation rate. The results show that the best population size is 300, 3,000 generations, the combination of crossover and mutation rate is 0.4:0.6 and the best selection method is elitist selection. Using a data test, the best parameters give a good solution that minimize the distribution route.

Optimization of Vehicle Routing Problem with Time Windows for Cold Chain Logistics Based on Carbon Tax

In order to reduce the cost pressure on cold-chain logistics brought by the carbon tax policy, this paper investigates optimization of Vehicle Routing Problem (VRP) with time windows for cold-chain logistics based on carbon tax in China. Then, a green and low-carbon cold chain logistics distribution route optimization model with minimum cost is constructed. Taking the lowest cost as the objective function, the total cost of distribution includes the following costs: the fixed costs which generate in distribution process of vehicle, transportation costs, damage costs, refrigeration costs, penalty costs, shortage costs and carbon emission costs. This paper further proposes a Cycle Evolutionary Genetic Algorithm (CEGA) to solve the model. Meanwhile, actual data are used with CEGA to carry out numerical experiments in order to discuss changes of distribution routes with different carbon emissions under different carbon taxes and their influence on the total distribution cost. The critical carbon tax value of carbon emissions and distribution cost is obtained through experimental analysis. The research results of this paper provide effective advice, which is not only for the government on carbon tax decision, but also for the logistics companies on controlling carbon emissions during distribution.

Effects of Introducing Similarity Measures into Artificial Bee Colony Approach for Optimization of Vehicle Routing Problem

Effects of Introducing Similarity Measures into Artificial Bee Colony Approach for Optimization of Vehicle Routing Problem

A Decision Support Approach for Postal Delivery and Waste Collection Services

This paper presents an urban-decision support system (U-DSS) devoted to manage, in a unified framework, the logistic services of the smart cities, such as postal delivery (PD) and waste collection (WC) services. The U-DSS architecture is proposed by describing its main components. In particular, this paper focuses on the core of the U-DSS, i.e., the model component that provides the solutions of a general vehicle assignment and routing optimization problem with the aim of minimizing the length of the routes and satisfying time and capacity constraints. In order to solve the vehicle routing problems in reasonable time, a two-phase heuristic algorithm is proposed based on a clustering strategy and a farthest insertion heuristic for the solution of a traveling salesman problem. The applicability of the proposed U-DSS is enlightened by comparing the proposed heuristic algorithm solutions with the mixed integer linear programming problem solutions of the PD and WC services. Moreover, the discussion of the real case studies of the city of Bari (Italy) assesses the proposed approach.

An Exponential Entropy-based Hybrid Ant Colony Algorithm for Vehicle Routing Optimization

Vehicle routing problem(VRP) is important combinatorial optimization problems which have received considerable attention in the last decades. The optimization of vehicle routing problem is a we ll-known research problem in the logistics distribution. In order to overcome the prematurity of Ant Colony Algorithm (ACA) for logistics distribution routing optimization, a hybrid algorithm combining improved ACA with Iterated Local Search (ILS) is proposed. The proposed algorithm adjusts the pheromone trail to balance the convergence rate and diversification of solutions self-adaptively. The exponential entropy is used to control the path selection and pheromone updating strategy. Combining with ILS is to avoid local best solutions and accelerate the search. Computational results denote the efficiency of the proposed algorithm on some standard bench mark problems.

The Application of a Improved Hybrid Ant Colony Algorithm in Vehicle Routing Optimization Problem

With the development of the market economy, the logistics industry has been developed rapidly.It is easy to understand that good vehicle travel path planning has very important significance in the logistics company,especially in the general production enterprises. This paper mainly studies the microcosmic traffic system in the type of vehicle routing problems: capacity-constrained vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem, treated as nodes in a spatial network. For the networks where the nodes are concentrated, the use of hybrid heuristic optimization can greatly improve the efficiency of the solution. The algorithm produces high-quality solutions for the capacity-constrained vehicle routing problem.

The Application of a Improved Hybrid Ant Colony Algorithm in Vehicle Routing Optimization Problem

With the development of the market economy, the logistics industry has been developed rapidly.It is easy to understand that good vehicle travel path planning has very important significance in the logistics company,especially in the general production enterprises. This paper mainly studies the microcosmic traffic system in the type of vehicle routing problems: capacity-constrained vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem, treated as nodes in a spatial network. For the networks where the nodes are concentrated, the use of hybrid heuristic optimization can greatly improve the efficiency of the solution. The algorithm produces high-quality solutions for the capacity-constrained vehicle routing problem.

A fuzzy milp-model for the optimization of vehicle routing problem

In this article, a novel model for the solution of a fuzzy vehicle routing problem is presented. The model originates from a crisp MILP Mixed Integer Linear Programming model previously presented on a conference. This work is motivated by a business context of timber transportation. Within this context, uncertainties arise from the fact that the distances and times between pickup points are inherently fuzzy. The decisions to be made are routing decisions, truck assignment and the determination of the pickup order for a set of loads and available trucks. The paper also presents briefly how the model is implemented in the Microsoft Excel environment, utilizing the LP-solve freeware as the optimization engine. The model is also illustrated with a numerical example. To the authors knowledge, there are no previously reported vehicle routing MILP models, where the times and distances are allowed to be fuzzy numbers.

Study on Optimization of Vehicle Routing Problem with Multiple Vehicle Type and Vehicle Access Restricted Based on Genetic Algorithm

In many cities, large trucks cant be used to delivery in the urban district, but in the suburbs all kinds of vehicles can be used. To solve the problem, this paper establishes the optimization model and designs a solving method using a genetic algorithm (GA), taking Zigong tobacco distribution center for example. Comparing with the data calculated by empirical distribution route, the results show that the total distance declines 155.47 km, the number of different type vehicles reduces 1-2 and the average load rate increased by 68.3% to 85.1%.

Research on Vehicle Routing Optimization Based on Improved Ant Colony Algorithm

This paper analyzes the domestic and international logistics distribution route optimization problem and the research status of ant colony algorithm, illustrates the problems existing in the logistics distribution now. It reflects the necessity to research on the vehicle routing optimization problem. In order to increasing the ant colony algorithm’s convergence speed and avoiding to fall into local optimum, we improve the pheromone evaporation coefficient and visibility to optimize the searching ability, which can avoid premature convergence and stagnation.

Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm

When there are multiple objectives co-existent in Vehicle routing problem(VRP), it is difficult to achieve optical status simultaneously. To solve this issue, it introduces a method of improved multi-objective Genetic Algorithm (MOGA). It adopts an approach close to heuristic algorithm to cultivate partial viable chromosomes, route decoding to ensure that all individuals meet constraints and uses relatively efficient method of arena contest to construct non-dominated set. Finally programme to fulfill the multi-objective algorithm and then apply it in the standard example of VRP to verity its effectiveness by comparison with the existing optimal results.

Study on Vehicle Routing and Scheduling Problems in Underground Mine Based on Adaptively ACA

For vehicle routing optimization problem in the underground mine, a famous NP- Hard problem is put forward. This paper uses improved ant colony algorithm (ACA) to solve the problem. Basic ant colony algorithm (ACA) has many shortages, such as long searching time， slow convergence rate and easily limited to local optimal solution etc. The improved ant colony algorithm is proposed to overcome these shortcomings and improve its performance adaptively. In every iteration of the ant colony algorithm, adaptive evaporating coefficient is selected to control the convergence rate at first. And the power of this approach was demonstrated on a test case. The results derived from basic ACA and the improved ACA are compared and analyzed in the experiment. It proved that the improved ant colony algorithm is effective

Logistics Distribution Vehicle Routing Problem with Time Windows

Vehicle routing optimization problem is one of the major research topics in logistics distribution field. Suitable vehicle routing selection is vital to reduce the logistics cost. The paper presents a hybrid optimization method to solve the vehicle routing problem with time windows. In the hybrid optimization method, discrete particle swarm optimization algorithm is used to assign the customers on routes and simulated annealing (SA) algorithm to avoid becoming trapped in local optimum. The simulation results have shown that the proposed method is feasible and effective for the vehicle routing problem with time windows.

Research on Optimization of Vehicle Routing Problem for Ride-sharing Taxi

In order to improve transportation efficiency of taxi and mitigate urban traffic congestion, it is necessary to carry out ride-sharing strategy which contributes to reducing operating cost and saving road resources. This study focuses on the routing optimization of ride-sharing taxis, in which interests of both taxi drivers and passengers are taken into account. Minimization of operating cost and maximization of customer satisfaction are considered as the objective, and travel mileage, waiting time and extra riding time due to ride-sharing are used to quantify them respectively. Routing optimization model for ride-sharing taxis is established and then appropriate simulated annealing algorithm satisfying constraints of the model proposed is designed. At last, a computational experiment is conducted to verify the model and the result shows that this method is able to save 19% mileage as well as 66% taxis available.

Application of Genetic Algorithms to Solve the Multidepot Vehicle Routing Problem

This paper deals with the optimization of vehicle routing problem in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time window constraint, the objective regarded in this paper comprises not only the cost due to the total traveling distance, but also the cost due to the total traveling time. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve the problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FLGA, a number of benchmark problems are used to examine its search performance. Also, several search methods, branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted to compare with FLGA in randomly generated data sets. Simulation results show that FLGA outperforms other search methods in all of three various scenarios.