Development Of Heuristic Methods Based On Genetic Algorithm (Ga) For Solving Vehicle Routing Problem (Pembangunan Kaedah Heuristik Berasaskan Algoritma Genetik Untuk Menyelesaikan Masalah Penjalanan Kenderaan) (original) (raw)
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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
Solving the Vehicle Routing Problem using Genetic Algorithm
The main goal of this research is to find a solution of Vehicle Routing Problem using genetic algorithms. The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem that belongs to the NP-complete class. Due to the nature of the problem it is not possible to use exact methods for large instances of the VRP. Genetic algorithms provide a search technique used in computing to find true or approximate solution to optimization and search problems. However we used some heuristic in addition during crossover or mutation for tuning the system to obtain better result.
Solving vehicle routing problem by using improved genetic algorithm for optimal solution
Journal of Computational Science, 2017
Context: The Vehicle Routing Problem (VRP) has numerous applications in real life. It clarifies in a wide area of transportation and distribution such as transportation of individuals and items, conveyance service and garbage collection. Thus, an appropriate selecting of vehicle routing has an extensive influence role to improve the economic interests and appropriateness of logistics planning. Problem: In this study the problem is as follows: Universiti Tenaga Nasional (UNITEN) has eight buses which are used for transporting students within the campus. Each bus starts from a main location at different times every day. The bus picks up students from eight locations inside the campus in two different routes and returns back to the main location at specific times every day, starting from early morning until the end of official working hours, on the following conditions: Every location will be visited once in each route and the capacity of each bus is enough for all students included in each route. Objectives: Our paper attempt to find an optimal route result for VRP of UNITEN by using genetic algorithm. To achieve an optimal solution for VRP of UNITEN with the accompanying targets: To reduce the time consuming and distance for all paths. which leads to the speedy transportation of students to their locations, to reduce the transportation costs such as fuel utilization and additionally the vehicle upkeep costs, to implement the Capacitated Vehicle Routing Problem (CVRP) model for optimizing UNITEN's shuttle bus services. To implement the algorithm which can be used and applied for any problems in the like of UNITEN VRP. Approach: The Approach has been presented based on two phases: firstly, find the shortest route for VRP to help UNITEN University reduce student's transportation costs by genetic algorithm is used to solve this problem as it is capable of solving many complex problems; secondly, identify The CVRP model is implemented for optimizing UNITEN shuttle bus services. Finding: The findings outcome from this study have shown that: (1) A comprehensive listed of active GACVRP; (2) Identified and established an evaluation criterion for GACVRP of UNITEN; (3) Highlight the methods, based on hybrid crossover operation, for selecting the best way (4) genetic algorithm finds a shorter distance for route A and route B. The proportion of reduction the distance for each route is relatively short, but the savings in the distance becomes greater when calculating the total distances traveled by all buses daily or monthly. This applies also to the time factor that has been reduced slightly based on the rate of reduction in the distances of the routes.
Solution to Vehicle Routing Problem Using Genetic Algorithms
2014
Vehicle routing problem is one of the most challenging areas of research in the field of combinatorial research. This problem is designing optimal set of routes for fleet of vehicles in order to serve a given set of costumers. Our project is mainly concerned about finding the optimized path between source and destination having several intermediate stages. We consider the problem in 2 regards: (a) Transportation View and (b) Personalized view. When coming to transportation, the goal is to maximize the income of the journey that is facilitating maximum people. The key objectives considered there is route constraint that limits the length of all feasible routes. When coming to the personalized view, a travel of a single person is concerned with the objectives like minimum cost, maximum mileage and so on To implement this we are using GA which come under multi objective Optimization techniques (MOO) .We choose to implement this because general conventional methods like heuristic search...
Solving the dynamic Vehicle Routing Problem using genetic algorithms
2014 International Conference on Logistics Operations Management, 2014
The vehicle routing problem (VRP) has become one of the focus issues in operations research and management sciences over the past two decades. One of its principal branches is the dynamic vehicle routing problem (DVRP), which can receive new order requests during the service process and make a timely response, unlike static vehicle routing problems (SVRP) where all information is known before the optimization starts. In this paper, we solve DVRP while using an enhanced genetic algorithm (GA) that tries to increase both diversity and global searching ability. The maximum saving method and the nearest neighbor method are adopted in the crossover operation to improve the path selection. Considering the near distance priority service principle (NDPSP) in the actual operation, a new assessment scheme with penalty factors is applied to our individual assessment. In addition, a paired-t test as a non-parametric statistical analysis is implemented to demonstrate the efficiency of the enhanced genetic optimization algorithm, based on a publicly available VRP benchmark, which includes 21 data sets. Analysis results show that our approach outperformed the published approached based on optimizing results.
2018
ABSTRACT— this research was focused on a heterogeneous fleet of passenger ships multi-depot by using the genetic algorithm (GA) to solve a combinatorial problem i.e. vehicle routing problem (VRP). The objective of this study is to compare the roulette wheel selection, single cut point crossover, and shift neighborhood mutation with selection based on selection rate, single cut point crossover, and shift neighborhood mutation to minimize the sum of the fuel consumption travelled, the cost for violations of the ship draft and sea depth, and penalty cost for violations of the load factor; to maximize the number port of call; and to maximize load factor. Problem-solving in this study is how to generate feasible route combinations for rich VRP that meets all the requirements with the optimum solution. Route generated by roulette wheel selection, single cut point crossover, and shift neighborhood mutation could decrease fuel consumption about 17.8990% compared to selection rate, single cu...
An efficient implementation of genetic algorithms for constrained vehicle routing problem
SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218)
The aim of this paper is to further study the rich vehicle routing problem (RVRP), which is a well-known combinatorial optimization problem arising in many transportation and logistics settings. This problem is known to be subject to a number of real life constraints, such as the number and capacity limitation of vehicles, time constraints including ready and due dates for each customer, heterogeneous vehicle fleets and different warehouses for vehicles. A Genetic Algorithm (GA)-based approach is proposed to tackle this highly constrained problem. The proposed approach efficiently resolves the problem despite its high complexity. To the best of our knowledge, no GA have been used for solving multi-depot heterogeneous limited fleet VRP with time windows so far. The new algorithm has been tested on benchmark and real-world instances. In fact, promising computational results have shown its good cost-effectiveness.
Using Genetic Algorithm in Implementing Capacitated Vehicle Routing Problem
Vehicle Routing Problem (VRP) has been considered as a significant segment in logistic handling. Thus, a proper selection of vehicle routes plays a very important part to ameliorate the economic benefits of logistic operations. In this paper, we consider the application of a Genetic Algorithm (GA) to a Capacitated Vehicle Routing Problem (CVRP) in which a set of vehicles with limits on capacity and travel time are available to service a set of customers and constrained by earliest and latest time for serving. The results of our test show that GA is able to determine the optimum route for the vehicles while maintaining their constraints of capacity and travel time.
Optimization Approach for Capacitated Vehicle Routing Problem Using Genetic Algorithm
IJSRD, 2013
Vehicle Routing Problem (VRP) is a combinatorial optimization problem which deals with fleet of vehicles to serve n number of customers from a central depot. Each customer has a certain demand that must be satisfied using each vehicle that has the same capacity (homogeneous fleet). Each customer is served by a particular vehicle in such a way that the same customer is not served by another vehicle. In this paper, Genetic Algorithm (GA) is used to get the optimized vehicle route with minimum distance for Capacitated Vehicle Routing Problem (CVRP). The outcomes of GA achieve better optimization and gives good performance. Further, GA is enhanced to minimize the number of vehicles.
Unconventional Heuristics for Vehicle Routing Problems
2016
The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing an optimal set of routes for a fleet of vehicles in order to serve a given set of customers. Vehicle routing problem forms an integral part of the supply chain management, which plays a significant role in productivity improvement in organizations through an efficient and effective delivery of goods/services to customers. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems, it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper, we have performed an experimental study that indicates a suitable use of genetic algorithms for the vehicle routing problem. We tested instances from Capacitated Vehicle Routing Problem Library (CVRPLIB) series A, B, E, M and X. The obtained experimental outputs ...