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Гибридный алгоритм решения транспортных задач с ограничением по времени

Гибридный алгоритм решения транспортных задач с ограничением по времени

Abstract

Рассматриваются новые подходы к решению задач маршрутизации автотранспорта. Обоснована актуальность и важность решения подобного рода задач для повышения эффективности и развития транспортной инфраструктуры регионов. Отмечено, что особый интерес представляют некоторые классы задач маршрутизации автотранспорта, в частности транспортная задача с ограничением по времени. Приведена математическая постановка задачи маршрутизации автотранспорта в терминах теории графов. Определена функция оценки качества получаемых решений. Сформулированы и записаны в виде математических выражений ограничения рассматриваемой оптимизационной задачи. Разработана методика кодирования решений для использования их в генетическом алгоритме. Предложены новые модификации операторов кроссинговера и мутации, направленные на повышение разнообразия текущей популяции и преодоление локальных оптимумов. Приведена структура разработанного алгоритма. На основе проведенного анализа установлено, что для эффективности подобных задач необходима разработка новых методов, позволяющих осуществлять динамическое изменение параметров алгоритма и при необходимости видоизменять структуру алгоритма. Предложены новые подходы к построению гибридных методов решения на основе сочетания методов генетического поиска и нечетких математических моделей и лингвистических переменных. Описан принцип действия и показан механизм работы нечеткого логического контроллера. Приведены примеры управляющего воздействия на параметры генетического алгоритма со стороны нечеткого логического контроллера. Проведены серии вычислительных экспериментов для анализа и сравнения качества получаемых решений с результатами известных тестовых примеров (бенчмарок). На основании анализа сделаны выводы о достоинствах и недостатках предложенного алгоритма.

The article deals with new approaches to solving vehicle routing. The urgency and importance of addressing such problems to increase the efficiency and the development of transport infrastructure in the regions. It is noted that some special interest classes vehicle routing problems, in particular transport problem with a time limit. The mathematical formulation of the problem of routing vehicles in terms of graph theory. We define the function evaluation of the quality of the solutions obtained. Formulated and written in the form of mathematical expressions limit considered optimization problem. The technique of coding solutions for use in the genetic algorithm. Proposed new versions of crossover and mutation operators to increase the diversity of the current population and overcome local optima. The structure of the algorithm. Based on the analysis found that the effectiveness of these tasks necessary to develop new methods that enable the dynamic change of the parameters of the algorithm and, if necessary, to modify the structure of the algorithm. New approaches to the construction of hybrid methods of solution based on a combination of genetic research methods and mathematical models and fuzzy linguistic variables. The principle of action and shows the mechanism of the fuzzy logic controller. Examples of the control action on the parameters of the genetic algorithm from the fuzzy logic controller. We carried out a series of numerical experiments to analyze and compare the quality of the decisions with the results known test cases (benchmark). Based on the analysis conclusions about the advantages and disadvantages of the proposed algorithm.

Keywords

ЗАДАЧИ МАРШРУТИЗАЦИИ АВТОТРАНСПОРТА,ДИНАМИЧЕСКАЯ ТРАНСПОРТНАЯ ЗАДАЧА С ОГРАНИЧЕНИЕМ ПО ВРЕМЕНИ,ЭВОЛЮЦИОННЫЕ ВЫЧИСЛЕНИЯ,ГИБРИДНЫЕ ИНТЕЛЛЕКТУАЛЬНЫ МЕТОДЫ,VEHICLE ROUTING PROBLEMS,DYNAMIC VEHICLE ROUTING PROBLEM WITH TIME WINDOWS,EVOLUTIONARY CALCULATIONS,HYBRID INTELLECTUAL METHODS

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average