Solving Vehicle Routing Problem Research Articles

AIS-based Algorithm for Solving Vehicle Routing Problem with Simultaneous Pick-up and Delivery

Vehicle Routing Problem with Simultaneous Pick-up and Delivery (VRP-SPD) is regarded as an NP-hard problem, which takes unacceptable time to use traditional algorithms to solve. This article presents an artificial immune systems or AIS-based algorithm to solve the problem and the results shows competitive performance. This algorithm is embedded with a sweep approach to generate random initial population. For the mutation operator, a variety of local search techniques are applied to realize the diversity. The algorithm is tested with examples which used by many of other works and compared with the results obtained by an exact algorithm. Experimental results suggest that the algorithm is a valuable alternative to other metaheuristics for solving VRP-SPD. 

Multi Objective Genetic Approach for Solving Vehicle Routing Problem

Multi Objective Genetic Approach for Solving Vehicle Routing Problem

Heuristics for an oil delivery vehicle routing problem

Companies distributing heating oil typically solve vehicle routing problems on a daily basis. Their problems may involve various features such as a heterogeneous vehicle fleet, multiple depots, intra-route replenishments, time windows, driver shifts and optional customers. In this paper, we consider such a rich vehicle routing problem that arises in practice and develop three metaheuristics to address it, namely, a tabu search (TS) algorithm, a large neighborhood search (LNS) heuristic based on this TS heuristic and another LNS heuristic based on a column generation (CG) heuristic. Computational results obtained on instances derived from a real-world dataset indicate that the LNS methods outperform the TS heuristic. Furthermore, the LNS method based on CG tends to produce better quality results than the TS-based LNS heuristic, especially when sufficient computational time is available.

A Shuffled Frog Leaping Algorithm for Solving Vehicle Routing Problem

For vehicle routing problem, its model is easy to state and difficult to solve. The shuffled frog leaping algorithm is a novel meta-heuristic optimization approach and has strong quickly optimal searching power. The paper applies herein this algorithm to solve the vehicle routing problem; presents a high-efficiency encoding method based on the nearest neighborhood list; improves evolution strategies of the algorithm in order to keep excellent characteristics of the best frog. This proposed algorithm provides a new idea for solving VRP.

Optimization on Vehicle Routing Problem Based on Immune Clonal Selection Algorithm

Vehicle routing problem is a typical NP-hard problem and is difficult to get an optimum solution. Aiming at the shortages of the existing methods, this paper proposed an algorithm based on immune clonal selection to solve vehicle routing problem. In the algorithm, expressed antibody with matrix, generated the initial population of antibodies randomly, and employed the operations such as clonal selection, genetic mutation iteratively to search optimum solution in solution space. The experimental results show that the algorithm presented here can converge to the global optimum solution rapidly, overcoming such disadvantages of the genetic algorithm as slower convergent velocity and the convergence to a local optimum solution.

Rich vehicle routing in theory and practice

The contribution of this paper is a comparison of the state of the art of scientific research on and commercial software for modelling and solving vehicle routing problems. To this end, the paper presents a compact review of vehicle routing literature and an overview of the results of a recent study of commercial vehicle routing software systems with respect to the problem features these systems are able to handle and the solution methods the systems use for automatic generation of vehicle routes. In this way, existing application and research gaps are identified.

A heuristic method for solving reverse logistics vehicle routing problems with time windows

Vehicle routing problems with simultaneous pickup and delivery with time windows are studied in this paper. In a closed-loop logistics network, vehicles visiting customers for pickup and delivery may be only allowed for certain time intervals. A mixed integer programming model of the problem is presented. It has been known that solving vehicle routing problems with time windows is NP-hard. A heuristic solution procedure is developed to solve the problem. The proposed heuristic is tested on a number of numerical examples based on modified benchmark problems in the literature.

An Approach for Solving Vehicle Routing Problem with Link Cost Variability in the Time

In this paper a method for goods distribution optimization in urban areas is reported considering the time-dependent networks. Goods distribution optimization refers to the best tour optimization for each fright vehicle. A first step in the route design is the definition of the best path from the actual client to all others client. The traffic conditions change during the day; hence the link cost is a variable that depends on the time. The path topology and the path cost are also time-dependent and the path search must be generalized considering the time variability. In the paper a model, a solution procedure and a test in a small system are reported.

Hybrid Ant Algorithm and Applications for Vehicle Routing Problem

Abstract Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.

CLING: heuristic to solve integrated resource allocation and routing problem with time window

One of the important extensions of the classical resource allocation problem is integrated resource allocation and routing problem with time window (IRARPTW). IRARPTW problem focuses on the time window for the service at the demanding node with the consideration of travel time of vehicle for a varying demand-oriented multi echelon supply chain with the consideration of limitation on the number of supply catalyst resource. We have developed a unified heuristic named clustering inherent genetic algorithm (CLING) to solve vehicle routing problem with time windows and IRARPTW. Heuristic CLING was tested for benchmark datasets of VRPTW and derived datasets of IRARPTW and yielded encouraging results.

An Approximation Algorithm for Vehicle Routing Problems with Hierarchized Traffic Network

Efficient delivery planning for freight transport is required for reducing environmental load and logistical costs. By using an actual traffic condition, a more realistic delivery plan, increased delivery work efficiency, and CO2 reduction will be possible. An algorithm for quickly obtaining a best solution is required to solve actual vehicle routing problems of large-scale networks and fluctuating delivery costs. The purpose of our research was to solve vehicle routing problems on a large-scale road network that can be applied to actual vehicle routing problems. Our proposed algorithm uses a hierarchical traffic network model composed of two layers according to the frequency of road use. The algorithm is based on tabu search, and the hierarchical traffic network is applied to a generation strategy of the neighborhood solution. Specifically, priority of movement of neighborhood solutions for generating a neighborhood solution is given to delivery points connected in frequently used roads. By using travelling salesman and simple vehicle-routing benchmark problems as a first step in our computing, we confirmed that the proposed algorithm can quickly provide a better solution than the non-application of the proposed algorithm. Furthermore, the effectiveness of the proposed algorithm is discussed by using a virtual vehicle routing problem based on an actual road network.

A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing Problems

We present a self-adaptive hyper-heuristic capable of solving static and dynamic instances of the capacitated vehicle routing problem. The hyper-heuristic manages a generic sequence of constructive and perturbative low-level heuristics, which are gradually applied to construct or improve partial routes. We present some design considerations to allow the collaboration among heuristics, and to find the most promising sequence. The search process is carried out by applying a set of operators which constructs new sequences of heuristics, i.e., solving strategies. We have used a general and low-computational cost parameter control strategy, based on simple reinforcement learning ideas, to assign non-arbitrary reward/penalty values and guide the selection of operators. Our approach has been tested using some standard state-of-the-art benchmarks, which present different topologies and dynamic properties, and we have compared it with previous hyper-heuristics and several well-known methods proposed in the literature. The experimental results have shown that our approach is able to attain quite stable and good quality solutions after solving various problems, and to adapt to dynamic scenarios more naturally than other methods. Particularly, in the dynamic case we have obtained high-quality solutions when compared with other algorithms in the literature. Thus, we conclude that our self-adaptive hyper-heuristic is an interesting approach for solving vehicle routing problems as it has been able (1) to guide the search for appropriate operators, and (2) to adapt itself to particular states of the problem by choosing a suitable combination of heuristics.

MIRHA: multi-start biased randomization of heuristics with adaptive local search for solving non-smooth routing problems

This paper discusses the use of probabilistic or randomized algorithms for solving vehicle routing problems with non-smooth objective functions. Our approach employs non-uniform probability distributions to add a biased random behavior to the well-known savings heuristic. By doing so, a large set of alternative good solutions can be quickly obtained in a natural way and without complex configuration processes. Since the solution-generation process is based on the criterion of maximizing the savings, it does not need to assume any particular property of the objective function. Therefore, the procedure can be especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods—both of exact and approximate nature—may fail to reach their full potential. The results obtained so far are promising enough to suggest that the idea of using biased probability distributions to randomize classical heuristics is a powerful one that can be successfully applied in a variety of cases.

A study on the effect of the asymmetry on real capacitated vehicle routing problems

Matrices with distances between pairs of locations are essential for solving vehicle routing problems like the Capacitated Vehicle Routing Problem ( CVRP), Traveling Salesman Problem ( TSP) and others. This work deals with the complex reality of transportation networks and asymmetry. Through a series of comprehensive and thorough computational and statistical experiments we study the effect that many factors like asymmetry, geographical location of the depot and clients, demand, territory and maximum vehicle capacity have in the solution of CVRP instances. We examine both classical heuristics as well as current state-of-the-art metaheuristics and show that these methods are seriously affected by the studied factors from a solution time and quality of solutions perspective. We systematically compare the solutions obtained in the symmetric scenario with those obtained in the real asymmetric case at a quantitative as well as a qualitative level, with the objective of carefully measuring and understanding the differences between both cases.

The Improvement of Hybrid Particle Swarm Algorithm and its Application

Hybrid multi-objective particle swarm algorithm is applied to vehicle routing problem and achieved good results, this paper based on the previous work, dynamic inertia weight is added to the particle swarm algorithm with intelligence factors, it improved the global search ability and the capacity of local convergence of the particle swarm algorithm; and the idea of immunity is introduced in the algorithm ,which makes the hybrid multi-objective particle swarm algorithm can effectively discard the repeated solutions in solving vehicle routing problems, this operation can improve the efficiency of the algorithm, and obtain better results under the same conditions.

Application Research of Improved Simulated Annealing Algorithm in Vehicle Routing Problems

This paper analyzed the principle and insufficient of traditional simulated annealing algorithm, and on the basis of the traditional simulated annealing algorithm, this paper used improved simulated annealing algorithm to solve vehicle routing problems. The new algorithm increases memory function, and keeps the current best state to avoid losing current optimal solution while reducing the computation times and accelerating the algorithm speed. The experimental results show that, the algorithm can significantly improve the optimization efficiency, and has faster convergence speed than traditional simulated annealing algorithm.

Solving Vehicle Routing Problems Using Constraint Programming and Lagrangean Relaxation in a Metaheuristics Framework

This paper presents a methodology based on the Variable Neighbourhood Search metaheuristic, applied to the Capacitated Vehicle Routing Problem. The presented approach uses Constraint Programming and Lagrangean Relaxation methods in order to improve algorithm’s efficiency. The complete problem is decomposed into two separated subproblems, to which the mentioned techniques are applied to obtain a complete solution. With this decomposition, the methodology provides a quick initial feasible solution which is rapidly improved by metaheuristics’ iterative process. Constraint Programming and Lagrangean Relaxation are also embedded within this structure to ensure constraints satisfaction and to reduce the calculation burden. By means of the proposed methodology, promising results have been obtained. Remarkable results presented in this paper include a new best-known solution for a rarely solved 200-customers test instance, as well as a better alternative solution for another benchmark problem.

Vehicle Routing Problem with Time Windows Based on Spatiotemporal Distance

Vehicle Routing Problem with Time Windows (VRPTW) is a well known NP-hard problem. A typical way to solve this problem is to partition the customers into groups first and then construct optimal routes for each group or groups. Conventionally, customers are partitioned according to the spatial distance only, while ignoring their time window requirements. In this paper, we consider jointly spatial and temporal characteristics of customers and propose a spatiotemporal distance based criteria for customers partitioning. To improve the initial solution, a tabu search algorithm is developed. To improve the convergence efficiency, this algorithm limits the objective value ranges that around the recently visited solutions, Instead of limiting recently visited solutions, the performance of the proposed algorithm is examined with 56 Solomon benchmark problems. The results indicate that spatiotemporal distance is more effective than spatial distance when solving vehicle routing problems with narrow time windows. Some results are even comparable to the best results obtained so far.

An efficient ant colony system for Vehicle Routing Problems with Time Windows

This paper presents an efficient algorithm based on a meta-heuristic Multiple Ant Colony System (MACS) for solving Vehicle Routing Problems with Time Windows (VRPTW). This study aims to determine the minimum cost routes for a fleet of vehicles of the same capacity to meet the demand of a set of customers within a specified time interval called a time window. The methodology, known as MACS-VRPTW, uses two ant colonies to minimise the number of vehicles and total time required to serve all customers. The algorithm was implemented in the Java environment, and six standard problems used as benchmarks in the literature were tested.

A particle swarm approach to solve vehicle routing problem with uncertain demand: A drug distribution case study

Article history: Received 1 January 2010 Received in revised form 10 March 2010 Accepted 1 April 2010 Available online 7 April 2010 During the past few years, there have tremendous efforts on improving the cost of logistics using varieties of Vehicle Routing Problem (VRP) models. In fact, the recent rise on fuel prices has motivated many to reduce the cost of transportation associated with their business through an improved implementation of VRP systems. We study a specific form of VRP where demand is supposed to be uncertain with unknown distribution. A Particle Swarm Optimization (PSO) is proposed to solve the VRP and the results are compared with other existing methods. The proposed approach is also used for real world case study of drug distribution and the preliminary results indicate that the method could reduce the unmet demand significantly. © 2010 Growing Science Ltd. All rights reserved.