Wyniki wyszukiwania - BazTech

1

An approach to the waste collection routing problem in the municipal services companies

Izdebski M., Jacyna M.

Prace Naukowe Politechniki Warszawskiej. Transport

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2015

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z. 108

5-14

EN

This article solves the waste collection routing problem from individual inhabitants. This type of waste collection routing problems is determined as kerbside collection. This problem was modified to the problem of the assignment of vehicles to tasks (in the litereture it is presented as the vehicle scheduling problem). In order to designate the assignment problem a method was developed. The presented method designates the set of tasks which are assigned to each vehicle, so it designates the routes of vehicles. The method consists of two stages. In the first stage the tasks were designated, whereas in the second stage the assignment of vehicles to tasks was made. Each stage consists of three phases: the preparatory phase, the optimization phase, the generation task or the generation assignment phase. Each phase was characterized. In this paper the block scheme of constructing the hybrid algorithm solving optimization problem of the method was presented. The application TransMar solving the waste collection routing problem was characterized.

PL

W artykule rozwiązano problem zbiórki odpadów od indywidualnych mieszkańców. Problem został zmodyfikowany do zagadnienia przydziału pojazdów do zadań (w literaturze problem ten jest prezentowany jako problem harmonogramowania tras pojazdów). W celu wyznaczenia przydziału opracowano metodę. Przedstawiona metoda wyznacza zbiór zadań, które są przydzielane do pojazdów, więc metoda wyznacza trasy pojazdów. Metoda składa się z dwóch etapów. W pierwszym etapie są wyznaczane zadania, w drugi przydział pojazdów do zadań. Każdy etap składa się z trzech faz: fazy przygotowawczej, optymalizacyjnej oraz generowania zadań i przydziału. W pracy przedstawiono schemat blokowy konstruowania algorytmu hybrydowego rozwiązującego problem optymalizacyjny przedstawionej metody. Przedstawiono aplikację TransMar rozwiązującą problem trasowania pojazdów w przedsiębiorstwach komunalnych.

2

Model przydziału pojazdów do zadań w systemach obsługi odpadów komunalnych

Izdebski J., Jacyna M.

Logistyka

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2015

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nr 4

3716--3725, CD 2

PL

W artykule przedstawiono wybrane aspekty modelowania przydziału pojazdów do zadań w przedsiębiorstwie komunalnym. Opisano model przydziału oraz metodę wyznaczającą przydział pojazdów do zadań. Metoda składa się z dwóch etapów. W pierwszym etapie zostały wyznaczone zadania, natomiast w drugim dokonano przydziału pojazdów do tych zadań. Implementacja komputerowa metody w postaci aplikacji TransMar umożliwia opracowanie przydziału pojazdów do zadań w przedsiębiorstwach usług komunalnych dla ustalonego rejonu sieci transportowej. Do rozwiązywania zagadnień optymalizacyjnych zaproponowano algorytm hybrydowy, czyli połączenie algorytmu genetycznego i mrówkowego.

EN

The paper presents some aspects of modelling assignment of the vehicles to tasks in the municipal services companies. The model of assignment and the method of determining assignment of vehicles to tasks was described. This method consists of two stages. The first stage is to designate the tasks in the municipal services companies, the second stage is to assign vehicles to these tasks. Computational implementation of the method in the form of application TransMar enables elaboration of assignment vehicles to tasks in the municipal services companies for the fixed the region of the transport network. In order to solve the optimization issues the hybrid algorithm was presented i.e. the combination of the genetic and ant algorithm.

3

Use of computer assistance in order to designate the tasks in the municipal services companies

Izdebski M., Jacyna M.

Journal of KONES

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2014

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Vol. 21, No. 2

105--112

EN

In this article, the method of designating the tasks in the municipal services companies was described. Presented method consists of three phase: the preparatory phase, the optimization phase and the generated tasks phase. Each phase was characterized. In this paper, the mathematical model of this problem was presented. The function of criterion and the condition on designating the tasks were defined. The minimum route described in the optimization phase was designated by the genetic algorithm. In this paper, the stages of constructing of the genetic algorithm were presented. A structure of the data processed by the algorithm, a function of adaptation, a selection of chromosomes, a crossover, a mutation and an inversion were characterized. A structure of the data was presented as string of natural numbers. In selection process, the roulette method was used and in the crossover, process the operator PMX was presented. The method was verified in programming language C #. The process of verification was divided into two stages. In the first stage, the best parameters of the genetics algorithm were designated. In the second stage, the algorithm was started with these parameters and the result was compared with the random search algorithm. The random search algorithm generates 2000 routes and the best result is compared with the genetic algorithm. The influence of the inversion, the mutation and the crossover on quality of the results was examined.

4

The Use of Heuristic Algorithms to Optimize the Transport Issues on the Example of Municipal Services Companies

Izdebski M.

Archives of Transport

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2014

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Vol. 29, iss. 1

27--36

EN

In this article the main optimization problems in the municipal services companies were presented. These problems concern the issue of vehicle routing. The mathematical models of these problems were described. The function of criterion and the conditions on designating the vehicle routing were defined. In this paper the hybrid algorithm solving the presented problems was proposed. The hybrid algorithm consists of two heuristic algorithms: the ant and the genetic algorithm. In this paper the stages of constructing of the hybrid algorithm were presented. A structure of the data processed by the algorithm, a function of adaptation, a selection of chromosomes, a crossover, a mutation and an inversion were characterized. A structure of the data was presented as string of natural numbers. In selection process the roulette method was used and in the crossover process the operator PMX was presented. This algorithm was verified in programming language C #. The process of verification was divided into two stages. In the first stage the best parameters of the hybrid algorithm were designated. In the second stage the algorithm was started with these parameters and the result was compared with the random search algorithm. The random search algorithm generates 2000 routes and the best result is compared with the hybrid algorithm.