A Google Maps based novel approach to the optimization of limited distribution systems (original) (raw)
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INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING, 2016
This article presents a system aimed at generating optimized vehicles routes-the vehicle routing problem with time windows (VRPTW) based on using a Google Maps ™ network data and imperialist competitive algorithm meta-heuristic. The vehicle routing problem with time windows is an important problem in the supply chain management. It copes with route scheduling and the distribution of goods from the distribution center to geographically dispersed customers in urban areas by a fleet of vehicles with constrained capacities. The system was tested for urban goods distribution in Sofia, Bulgaria-35 retailers, warehouse, two types of vehicles (capacity) and a working day.
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...
Intelligent Computational Optimization in Engineering; pp. 241-269, 2011
The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem that can be described as follows: given a fleet of vehicles with uniform capacity, a common depot, and several requests by the customers, find a route plan for the vehicles with overall minimum route cost (eg. distance traveled by vehicles), which service all the demands. It is well known that multiple Traveling Salesman Problem (mTSP) based algorithms can also be utilized in several VRPs by incorporating some additional constraints, it can be considered as a relaxation of the VRP, with the capacity restrictions removed. The mTSP is a generalization of the well known traveling salesman problem (TSP), where more than one salesman is allowed to be used in the solution. Because of the fact that TSP is already a complex, namely an NP-hard problem, heuristic optimization algorithms, like genetic algorithms (GAs) need to be taken into account. The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding and genetic operators to ensure efficiency. The aim of this chapter is to review how genetic algorithms can be applied to solve these problems, and propose a novel, easily interpretable and problem-oriented representation and operators, that can easily handle constraints on the tour lengths, and the number of salesmen can vary during the evolution. The elaborated heuristic algorithm is demonstrated by a complete realistic example.
A Novel Approach to Solve Multiple Traveling Salesmen Problem by Genetic Algorithm
Computational Intelligence in Engineering, pp 141-151, 2010
The multiple Traveling Salesman Problem (mTSP) is a complex combinatorial optimization problem, which is a generalization of the well-known Traveling Salesman Problem (TSP), where one or more salesmen can be used in the solution. The optimization task can be described as follows: given a fleet of vehicles, a common depot and several requests by the customers, find the set of routes with overall minimum route cost which service all the demands. Because of the fact that TSP is already a complex, namely an NP-complete problem, heuristic optimization algorithms, like genetic algorithms (GAs) need to take into account. The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding to ensure efficiency. The aim of this paper is to review how genetic algorithms can be applied to solve these problems and propose a novel, easily interpretable representation based GA. KeywordsmTSP-VRP-genetic algorithm-multi-chromosome-optimization
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.
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 ...
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.
eprints.utm.my
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