Решение задач маршрутизации транспорта методом муравьиных колоний
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.
Although the multi-depot vehicle routing problem with simultaneous deliveries and pickups (MDVRPSDP) is often encountered in real-life scenarios of transportation logistics, it has received little attention so far. Particularly, no papers have ever used metaheuristics to solve it. In this paper a metaheuristic based on iterated local search is developed for MDVRPSDP. In order to strengthen the search, an adaptive neighborhood selection mechanism is embedded into the improvement steps and the perturbation steps of iterated local search, respectively. To diversify the search, new perturbation operators are proposed. Computational results indicate that the proposed approach outperforms the previous methods for MDVRPSDP. Moreover, when applied to VRPSDP benchmarks, the results are better than those obtained by large neighborhood search, particle swarm optimization, and ant colony optimization approach, respectively.
Financial Decision Making Using Computational Intelligence covers all the recent developments in complex financial decision making through computational intelligence approaches. Computational intelligence has evolved rapidly in recent years and it is now one of the most active fields in operations research and computer science. The increasing complexity of financial problems and the enormous volume of financial data often make it difficult to apply traditional modeling and algorithmic procedures. In this context, the field of computational intelligence provides a wide range of useful techniques, including new modeling tools for decision making under risk and uncertainty, data mining techniques for analyzing complex data bases, and powerful algorithms for complex optimization problems.
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.
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.