Применение алгоритма муравьиной колонии для решения задачи маршрутизации транспорта
Motor fuel distribution problem is considered. Accepting some assumptions it can be reduced to a well-known vechicle routing problem with capacity constraints. Ant colony optimization approach is suggested for solving CVRP. Modified ant algorithms are performed. Computational results for some benchmarks are given in compare with classical ant algorithms.
In this paper we consider application of ant colony optimization techniques for capacitated vehicle routing problem. Modified ant colony optimization algorithm is proposed, computational results are reported.
The problem of automatic vehicle routing for oil products transportation from storage depots to filling stations is considered. An overview of existing software solutions and their limitations are shown. Metaheuristic algorithm for solving this problem is described, software architecture of the system is proposed.
The companies that are IT-industry leaders perform from several tens to several hundreds of projects simultaneously. The main problem is to decide whether the project is acceptable to the current strategic goals and resource limits of a company or not. This leads firms to an issue of a project portfolio formation; therefore, the challenge is to choose the subset of all projects which satisfy the strategic objectives of a company in the best way. In this present article we propose the multi-objective mathematical model of the project portfolio formation problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After analysis, we choose ant colony optimization method and SPEA II method, which is a modification of a genetic algorithm. We describe the implementation of these methods applied to the project portfolio formation problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modification of our SPEA II implementation were considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. The part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population more strongly than an initial population, which is generated completely by random. Comparing of ant colony optimization algorithm and three modifications of a genetic algorithm was performed. We use the following parameters: speed of execution and the C-metric between each pair of algorithms. Genetic algorithm with non-random initial population show better results than other methods. Thus, we propose using this algorithm for solving project portfolio formation problem.