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ŞAHİNGÖZ, ÖZGÜR KORAY

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ŞAHİNGÖZ

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ÖZGÜR KORAY

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20203
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Subject: Genetic Algorithm

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Now showing 1 - 3 of 3
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    Genetic Algorithm Based Optimized Waste Collection in Smart Cities
    (Institute of Electrical and Electronics Engineers Inc., 2020) Özmen, Mehmet; Şahin, Hasan; ŞAHİNGÖZ, ÖZGÜR KORAY
    In recent years, the concept of smarts cities emerged to cope with the growth that cities around the world are facing. There are lots of problem areas in smart cities such as smart education, health, buildings, shopping, traffic management, etc. Waste management is one complex and effective problems of urbanization that is needed to be solved in smart cities. Route planning for waste collection and garbage trucks is a known issue in waste management. In this project, a genetic algorithm is proposed to address the problem of waste collection route using a truck fleet. The algorithm was tested in a simplified real state in single area and proved to be applicable to real-world scenarios based solely on the actual data of waste collection of cities.
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    An Evolutionary Approach to Multiple Traveling Salesman Problem for Efficient Distribution of Pharmaceutical Products
    (Institute of Electrical and Electronics Engineers Inc., 2020) Koçyiğit, Emre; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, Banu
    Considerable growth of computer science has created novel solutions for variable problem fields and has increased the efficiency of available solutions. Evolutionary algorithms are quite successful in dealing with real-world problems that require optimization. In this article, we implemented a Genetic Algorithm that is well known evolutionary algorithm in order to provide an efficient solution for the Distribution of Pharmaceutical Products, which is a vital optimization problem, especially in situations such as a pandemic. The Multiple Traveling Salesman Problem approach was used to distribute pharmaceutical products as soon as possible. Moreover, we strengthened our proposal algorithm with 2-Opt Algorithm to get optimal results in earlier iterations. Different datasets from a library were applied to measure the quality of solutions and computation time. At the end of the work, we observed that our proposed algorithm generates successful solutions in an acceptable running time. This study will be extended with a new mutation concept as future work.
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    Job Shop Scheduling Problem and Solution Algorithms: A Review
    (Institute of Electrical and Electronics Engineers Inc., 2020) Çebi, Ceren; Ataç, Enes; ŞAHİNGÖZ, ÖZGÜR KORAY
    Job Shop Scheduling Problem (JSSP), which aims to schedule several jobs over some machines in which each job has a unique machine route, is one of the NP-hard optimization problems researched over decades for finding optimal sequences over machines. Optimization mainly focused on minimizing the maximum completion time (which is also named as makespan) of whole tasks. According to the size of the problem, JSSP can be defined as Gantt-Chart, Disjunctive Graph, and binary representation forms. This type of scheduling problem is solved with various optimization algorithms such as the Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization, Tabu Search, or with linear programming models. In this paper, we explain the main characteristics of JSSP and the solution methodologies of this type of problem.