Development and Comparison of Four GA based Algorithms for Heterogeneous Vehicle Routing Problem with Time Windows (original) (raw)
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Journal of Service Science and Management, 2015
The vehicle routing problem (VRP) is classified as an NP-hard problem. Hence exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. To get solutions in determining routes which are realistic and very close to the optimal solution, one has to use heuristics and meta-heuristics. In this paper, an attempt has been made to develop a GA based meta-heuristic to solve the time dependent vehicle route problem with time windows (TDVRPTW). This algorithm is compared with five other existing algorithms in terms of minimizing the number of vehicles used as well as the total distance travelled. The algorithms are implemented using Matlab and HeuristicLab optimization software. A plugin was developed using Visual C# and NET Framework 4.5. Results were tested using Solomon's 56 benchmark instances (of which 24 instances are used with 4 in each of the 6 problem classes) classified into groups such as C1, C2, R1, R2, RC1, and RC2. For each of the performance measures, through a complete factorial experiment with two factors, it is proved that the proposed algorithm is the best among all the six algorithms compared in this paper.
Evaluation of Vehicle Routing Problem with Time Windows by Using Metaheuristics Algorithm
2011
Vehicle routing problem with Time Window (VRPTW) has received much attention by researchers in solving many scheduling applications for transportation and logistics. The objective of VRPTW is to use a fleet of vehicles with specific capacity to serve a number of customers with various demands and time window constraints. As a non-polynomial (NP) hard problem, the VRPTW is complex and time consuming, especially when it involves a large number of customers and constraints. This paper presents a metaheuristics approach for solving VRPTW. The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been selected as the two metaheuristics algorithm. A computational experiment has been carried out by running the PSO and GA with the VRPTW benchmark data set. The empirical results show that PSO perform better than GA when tested on clustered based customer distribution. On the other hand, GA is superior to PSO on the random customer distributions. In term of computing time, the performance of PSO algorithm is better than GA.
A Hybrid Metaheuristic Approach to Solve the Vehicle Routing Problem with Time Windows
Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics, 2012
This paper addresses the Capacitated Vehicle Routing Problem with Time Windows, with constraints related to the vehicle capacity and time windows for customer service. To solve this problem two different metaheuristics are used: Tabu Search and Genetic Algorithms. Based on these techniques a hybrid algorithm is developed. The main goal is the development of a Hybrid Algorithm focused on the Vehicle Routing Problem which uses the intensification power of the Tabu Search and the diversification power of the Genetic Algorithms, in order to obtain good quality solutions without compromising the computational time. In the experiments are combined policies of diversification and intensification in Tabu Search and Genetic Algorithm to verify the efficiency and robustness of the proposed hybrid algorithm. Finally, the results are compared with the best heuristic and exact methods results found in the literature. The Hybrid Algorithm here proposed shows efficiency and robustness, with several optimal solutions achieved.
2019
Vehicle Routing Problem with Time Windows (VRPTW) involves traversing a coordinated set of vehicular paths such that a set of customers is visited once within a given timestamped boundary. VRPTW poses a great challenge to logistics distribution and supply chain management systems, due to its characterized stochastic and NP-hard combinatorial properties, which requires that its corresponding optimal path planning and vehicle scheduling solutions be both highly efficient and cost effective even as customers’ demands change dynamically. In this paper, a new hybrid metaheuristic scheme, tagged TERMHIGEN, based on the characteristics of the Termite-Hill algorithm and a modified Genetic Algorithm, with its associated adaptive self-learning and tuning schemes, based on is developed and applied to solving a prototype VRPTW specifically with the objective of minimizing overall logistic distribution cost. TERMHIGEN was tested using Solomon’s 56 VRPTW instances containing 100 customers. The pe...
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The Vehicle Routing Problem (VRP) is an important area and has been studied as combinatorial optimization problems. VRP calls for the determination of the optimal set of routes to be performed by a fleet of vehicle to serve a given set of customers. VRP in which demand at each location is unknown at the time when the route is designed but is follow a known probability distribution, is known as VRP with Stochastic Demands (VRPSD). VRPSD finds its application on wide-range of distribution and logisticstransportation sector with the objective is to serve a set of customers at minimum total expected cost. One of the applications of VRPSD is in the case of picking up garbage done by solid waste collection company. The computational complexity of most vehicle routing problem and moreover the intricate of stochastic VRP algorithm has made them an important candidate for solution using metaheuristics. This research proposes the enhanced metaheuristic algorithms that exploit the power of Tabu Search, Genetic Algorithm, and Simulated Annealing for solving VRPSD. Genetic Algorithm as population-based methods are better identifying promising areas in the search space, while Tabu Search and Simulated Annealing as trajectory methods are better in exploring promising areas in search space. Simulated Annealing is a global optimization technique which traverses the search space by generating neighboring solutions of the current solution. A superior neighbor is always accepted and an inferior neighbor is accepted with some probability. Tabu Search is similar to Simulated Annealing, in that both traverse the solution space by testing mutations of an individual solution. However, simulated annealing generates only one mutated solution but Tabu Search generates many mutated solutions and moves
A genetic algorithm for solving the generalized vehicle routing problem
Hybrid Artificial Intelligence Systems, 2010
Abstract. The generalized vehicle routing problem is a variant of the well-known vehicle routing problem in which the nodes of a graph are partitioned into a given number of node sets (clusters) and the objective is to find the minimum-cost delivery or collection of routes, subject to capacity restrictions, from a given depot to the number of predefined clusters passing through one node from each clusters. We present an effective metaheuristic algorithm for the problem based on genetic algorithms. The proposed metaheuristic is ...
Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms
PROMET-Traffic&Transportation, 2012
Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process. Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search. Both algorithms generate initial feasible solution using constructive heuristics and use operators and various strategies for an iterative improvement. The algorithms were tested on Solomon's benchmark problems and real world vehicle routing problems with time windows. In total, 44 real world problems were optimized in the case study using described algorithms. Obtained results showed that the same distribution task can be accomplished with savings up to 40% in the total travelled distance and that manually constructed routes are very ineffective.
An Improved Genetic Algorithm for Solving Multi Depot Vehicle Routing Problems
International Journal of Information Systems and Supply Chain Management
The classical Vehicle Routing Problem (VRP) tries to minimise the cost of dispatching goods from depots to customers using vehicles with limited carrying capacity. As a generalisation of the TSP, the problem is known to be NP-hard and several authors have proposed heuristics and meta-heuristics for obtaining good solutions. The authors present genetic algorithm-based approaches for solving the problem and compare the results with available results from other papers, in particular, the hybrid clustering based genetic algorithm. The authors find that the proposed methods give encouraging results on all these instances. The approach can be extended to solve multi depot VRPs with heterogeneous fleet of vehicles.
Transportation Science
This work presents a steady-state genetic algorithm enhanced by a complete trie-based solution archive for solving the generalized vehicle routing problem with stochastic demands using a preventive restocking strategy. As the necessary dynamic programming algorithm for the solution evaluation is very time consuming, considered candidate solutions are stored in the solution archive. It acts as complete memory of the search history, avoids re-evaluations of duplicate solution candidates and is able to efficiently transform them into guaranteed new ones. This increases the diversity of the population and reduces the risk of premature convergence. Similar to a branch-and-bound algorithm, the tree structure of the solution archive is further exploited to compute lower bounds on the nodes to cut off parts of the solution space which evidently do not contain good solutions. Since in each iteration a not yet considered solution candidate is generated and completeness can be efficiently checked, the overall method is in principle an exact enumeration algorithm, which leads to guaranteed optimal solutions for smaller instances. Computational results of this algorithm show the superiority over the so far state-of-the-art metaheuristic.