A Genetic Algorithm for Optimizing Parameters for Ant Colony Optimization Solving Capacitated Vehicle Routing Problems

The multiple trip vehicle routing problem involves several sequences of routes. Working shift of single vehicle can be scheduled in multiple trips. It is suitable for urban areas where the vehicle has very limited size and capacity over short travel distances. The size and capacity limit also requires the vehicle should be vacated frequently. As a result, the vehicle could be used in different trips as long as the total time or distance is not exceeded. Various approaches are developed to solve the vehicle routing problem (VRP). Except for the simplest cases, VRP is always a computationally complex issue in order to optimize the objective function in terms or both time and expense. Ant colony optimization (ACO) has been introduced to solve the vehicle routing problem. The multiple ant colony system is proposed to search for alternative trails between the source and destination so as to minimize (fuel consumption, distance, time) among numerous geographically scattered routes. The objective is to design adaptive routing so as to balance loads among congesting city networks and to be adaptable to connection failures. As the route number increases, each route becomes less densely packed. It can be viewed as the vehicle scheduling problem with capacity constraints. The proposed scheme is applied to typical cases of vehicle routing problems with a single depot and flexible trip numbers. Results show feasibility and effectiveness of the approach.

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Ant colony optimization for real-world vehicle routing problems

Swarm Intelligence ◽

10.1007/s11721-007-0005-x ◽

2007 ◽

Vol 1 (2) ◽

pp. 135-151 ◽

Cited By ~ 100

Author(s):

A. E. Rizzoli ◽

R. Montemanni ◽

E. Lucibello ◽

L. M. Gambardella

Keyword(s):

Ant Colony Optimization ◽

Vehicle Routing ◽

Real World ◽

Ant Colony ◽

Vehicle Routing Problems ◽

Routing Problems

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A Genetic Algorithm's Approach to the Optimization of Capacitated Vehicle Routing Problems

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9644-0.ch008 ◽

2016 ◽

pp. 209-235 ◽

Cited By ~ 3

Author(s):

Mariano Frutos ◽

Fernando Tohmé ◽

Fabio Miguel

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Real World ◽

Selective Pressure ◽

Fresh Fruit ◽

Vehicle Routing Problems ◽

Routing Problems ◽

The Family ◽

Capacitated Vehicle ◽

Real World Problems

This chapter addresses the family of problems known in the literature as Capacitated Vehicle Routing Problems (CVRP). A procedure is introduced for the optimization of a version of the generic CVRP. It generates feasible clusters and, in a first step, yields a coding of their ordering. The next stage provides this information to a genetic algorithm for its optimization. A selective pressure process is added in order to improve the selection and subsistence of the best candidates. This arrangement allows improving the performance of the algorithm. We test it using Van Breedam and Taillard's problems, yielding similar results as other algorithms in the literature. Besides, we test the algorithm on real-world problems, corresponding to an Argentinean company distributing fresh fruit. Four instances, with 50, 100, 150 and 200 clients were examined, giving better results than the current plans of the company.

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