Multi-depot Heterogeneous Vehicle Routing Research Articles

Efficient Network Selection Using Multi-Depot Routing Problem for Smart Cities

Smart cities make use of a variety of smart technology to improve societies in better ways. Such intelligent technologies, on the other hand, pose significant concerns in terms of power usage and emission of carbons. The suggested study is focused on technological networks for big data-driven systems. With the support of software-defined technologies, a transportation-aided multicast routing system is suggested. By using public transportation as another communication platform in a smart city, network communication is enhanced. The primary objective is to use as little energy as possible while delivering as much data as possible. The Attribute Decision Making with Capacitated Vehicle (CV) Routing Problem (RP) and Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used in the proposed research. For the optimum network selection, a Multi-Attribute Decision Making (MADM) method is utilized. For the sake of reducing energy usage, the Capacitated Vehicle Routing Problem (CVRP) is employed. To reduce the transportation cost and risk, Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used. Moreover, a mixed-integer programming approach is used to deal with the problem. To produce Pareto optimal solutions, an intelligent algorithm based on the epsilon constraint approach and genetic algorithm is created. A scenario of Auckland Transport is being used to validate the concept of offloading the information onto the buses for energy-efficient and delay-tolerant data transfer. Therefore the experiments have demonstrated that the buses may be used effectively to carry out the data by customer requests while using 30% of less energy than the other systems.

Multi-Depot Heterogeneous Vehicle Routing Optimization for Hazardous Materials Transportation

Multi-Depot Heterogeneous Vehicle Routing Optimization for Hazardous Materials Transportation

Time-Dependent Multi-Depot Heterogeneous Vehicle Routing Problem Considering Temporal–Spatial Distance

Aiming at the multi-depot heterogeneous vehicle routing problem under the time-dependent road network and soft time window, considering vehicle fixed cost, time window penalty cost and vehicle transportation cost, an optimization model of time-dependent multi-depot heterogeneous vehicle routing problem is established with the objective of minimizing distribution cost. According to the characteristics of the problem, a hybrid genetic algorithm with variable neighborhood search considering the temporal–spatial distance is designed. Customers are clustered according to the temporal–spatial distance to generate initial solutions, which improves the quality of the algorithm. The depth search capability of the variable neighborhood search algorithm is applied to the local search strategy of the genetic algorithm to enhance the local search capability of the algorithm. An adaptive neighborhood search number strategy and a new acceptance mechanism of simulated annealing are proposed to balance the breadth and depth required for population evolution. The validity of the model and algorithm is verified by several sets of examples of different scales. The research results not only deepen and expand the relevant research on vehicle routing problem, but also provide theoretical basis for logistics enterprises to optimize distribution scheme.

Half Open Multi-Depot Heterogeneous Vehicle Routing Problem for Hazardous Materials Transportation

How to reduce the accidents of hazardous materials has become an important and urgent research topic in the safety management of hazardous materials. In this study, we focus on the half open multi-depot heterogeneous vehicle routing problem for hazardous materials transportation. The goal is to determine the vehicle allocation and the optimal route with minimum risk and cost for hazardous materials transportation. A novel transportation risk model is presented considering the variation of vehicle loading, vehicle types, and hazardous materials category. In order to balance the transportation risk and the transportation cost, we propose a bi-objective mixed integer programming model. A hybrid intelligent algorithm is developed based on the ε-constraint method and genetic algorithm to obtain the Pareto optimal solutions. Numerical experiments are provided to demonstrate the effectiveness of the proposed model. Compared with the close multi-depot heterogeneous vehicle routing problem, the average risk and cost obtained by the proposed bi-objective mixed integer programming model can be reduced by 3.99% and 2.01%, respectively. In addition, compared with the half open multi-depot homogeneous vehicle routing problem, the cost is significantly reduced with the acceptable risk.

Multidepot Heterogeneous Vehicle Routing Problem for a Variety of Hazardous Materials with Risk Analysis

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

A Survey for Vehicle Routing Problems and Its Derivatives

Based on the research of recent papers on routing optimization, this paper classifies routing problem into traditional vehicle routing problem and its derivatives, such as CVRP (Capacity Vehicle Routing Problem), HVRP (Heterogeneous Vehicle Routing Problem), MDVRP (Multi-warehouse Vehicle Routing Problem, VRPTW (Vehicle Routing Problem with Time Window). Optimization problem, MDVRPTW (multi-depot vehicle routing problem with time windows), MDHVRPTW (multi-depot heterogeneous vehicle routing problem with time windows). The characteristics of the above problems are summarized and analyzed, and the innovative solution methods are proposed. Finally, the further research trends and hot spots of the path optimization problem are prospected.

Methods and algorithms to solve the vehicle routing problem with time windows and further conditions

In this paper methods and algorithms are examined for solving one important problem of transport logistics, namely formation of roadmap. The proposed mathematical model is based on the well-known multi-depot heterogeneous vehicle routing problem with time windows algorithm. Modifications of the model provide additional conditions and restrictions. Algorithmic support of information transport system in an enterprise is connected with features of the vehicle routing problem. The suggested solution is based on modified Clarke and Wright Algorithm and Variable Neighborhood Search. The risk on the roads and the cost of toll roads are calculated, too.

Multi-objective optimization using genetic algorithm based clustering for multi- depot heterogeneous fleet vehicle routing problem with time windows

Efficient routing and scheduling of vehicles has significant economic implications for both the public and private sectors. For this purpose, we propose in this study a decision support system which aims to optimise the classical capacitated vehicle routing problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that we call the multi-depot heterogeneous vehicle routing problem with time window (MDHVRPTW) by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles...., and we solve this problem by proposing a new scheme based on the application of the bio-inspired genetic algorithm heuristics and by embedding a clustering algorithm within a VRPTW optimisation frame work, that we will specify later. Computational experiments with the benchmark test instances confirm that our approach produces acceptable quality solutions compared with the best previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that our proposed genetic algorithm is effective in solving the MDHVRPTW problem and hence has a great potential.

An Improved Genetic Algorithm for Multi-Depot Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery Time Windows

Multi-depot heterogeneous vehicle routing problem with simultaneous pickup and delivery and time windows (MDHVRPSPDTW) is an extension of vehicle routing problem (VRP), MDHVRPSPDTW mathematical model was established. The improved genetic algorithm (IGA) is proposed for solving the model. Firstly, MDHVRPSPDTW is transferred into different groups by the seed customer selecting method and scanning algorithm (SA).Secondly, IGA based on elite selection and inversion operator is used to solve the model, and then cutting merge strategy based on greedy thought and three kinds of neighborhood search methods is applied to optimize the feasible solutions further. Finally, 3-opt local search is applied to adjust the solution. The proposed IGA has been test on a random new numerical example.The computational results show that IGA is superior to branch and bound algorithm (BBD) by Lingo 9.0 in terms of optimum speed and solution quality, and the model and the proposed approach is effective and feasible.

A generalized multi-depot vehicle routing problem with replenishment based on LocalSolver

In this paper, we consider the multi depot heterogeneous vehicle routing problem with time windows in which vehicles may be replenished along their trips. Using the modeling technique in a new-generation solver, we construct a novel formulation considering a rich series of constraint conditions and objective functions. Computation results are tested on an example comes from the real-world application and some cases obtained from the benchmark problems. The results show the good performance of local search method in the efficiency of replenishment system and generalization ability. The variants can be used to almost all kinds of vehicle routing problems, without much modification, demonstrating its possibility of practical use.

Optimization of Multi-Depot Heterogeneous Incident Vehicle Routing Problem with Soft Time Windows Based on an Improved Ant Colony Optimization

Aiming at vehicle routing problem (VRP) with many extended features is widely used in actual life, multi-depot heterogeneous vehicle routing problem with soft time windows (MDHIVRPSTW) mathematical model is established. An improved ant colony optimization (IACO) is proposed for solving this model. Firstly, MDHIVRPSTW was transferred into different groups according to nearest depot method, then constructing the initial route by scanning algorithm (SA). Secondly, genetic operators were introduced, and then adjusting crossover probability and mutation probability adaptively in order to improve the global search ability of the algorithm. Moreover, smooth mechanism was used to improve the performance of ant colony optimization (ACO). Finally, 3-opt strategy was used to improve the local search ability. The proposed IACO has been tested on a 32-customer instance which was generated randomly. The experimental results show that IACO is superior to other three algorithms in terms of convergence speed and solution quality, thus the proposed method is effective and feasible, and the proposed model is better than conventional model.

An Improved Variable Neighborhood Search Algorithm for Multi Depot Heterogeneous Vehicle Routing Problem based on Hybrid Operators

This paper presents an Imporved Variable Neighborhood Search (IVNS) algorithm to solve Multi Depot Heterogeneous Vehicle Routing Problem with Time Windows (MDHVRPTW). The initial solution is obtained by customer allocation and path planning based on a cost network. The hybrid operators of insert and exchange are used to achieve the shaking process and the later optimization process is presented based on hybird operators to improve the solution space, the best-improvement strategy is adopted, which make the algorithm can achieve a better balance in the solution quality and running time. The idea of simulated annealing is introduced to take control of the acceptance of new solutions. Computational results are presented in benchmark instances, which show that our approach is competitive and even outperforms existing solution procedures proposed in the literature. And finally the proposed model and algorithm is applied to the large water project in China to solve the allocation of vehicles and routes.

Study on Multi Depot Heterogeneous Vehicle Routing Problem with an Improved Variable Neighborhood Search Algorithm

Study on Multi Depot Heterogeneous Vehicle Routing Problem with an Improved Variable Neighborhood Search Algorithm

A New Variable Neighborhood Search Algorithm for the Multi Depot Heterogeneous Vehicle Routing Problem with Time Windows

Abstract The Multi Depot Heterogeneous Vehicle Routing Problem with Time Windows (MDHVRPTW) is an important variant of the Vehicle Routing Problem (VRP), which is complex and still not resolved well. To solve the problem, mathematical model of the MDHVRPTW is constructed and a modified variable neighborhood search algorithm is proposed. In the algorithm, a hybrid operator of insert and exchange are used to achieve the shaking process, the Best-improvement strategy is adopted, and it can make the algorithm to achieve a better balance in the solution quality and running time. The performance of the proposed algorithm is tested on the benchmark instances proposed by Golden and compared with other algorithms in the literature. The results indicate that the proposed algorithm is competitive and feasible in solving the MDHVRPTW.

A branch-and-cut-and-price algorithm for the multi-depot heterogeneous vehicle routing problem with time windows

We present a branch-and-cut-and-price algorithm for the exact solution of a variation of the vehicle routing problem with time windows in which the transportation fleet is made by vehicles with different capacities and fixed costs, based at different depots. We illustrate different pricing and cutting techniques and we present an experimental evaluation of their combinations. Computational results are reported on the use of the algorithm both for exact optimization and as a heuristic method.