Researcher:
Kirlik, Gökhan

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PhD Student

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Gökhan

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Kirlik

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Kirlik, Gökhan

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2012 - 20157

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Researcher Of Publications: search.filters.isAuthorOfPublication.b45e9746-11fe-486f-a928-16c0b8fc435f

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Now showing 1 - 7 of 7
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    A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems
    (Elsevier Science Bv, 2014) N/A; N/A; Department of Business Administration; Kirlik, Gökhan; Sayın, Serpil; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 6755
    Most real-life decision-making activities require more than one objective to be considered. Therefore, several studies have been presented in the literature that use multiple objectives in decision models. In a mathematical programming context, the majority of these studies deal with two objective functions known as bicriteria optimization, while few of them consider more than two objective functions. In this study, a new algorithm is proposed to generate all nondominated solutions for multiobjective discrete optimization problems with any number of objective functions. In this algorithm, the search is managed over (p - 1)-dimensional rectangles where p represents the number of objectives in the problem and for each rectangle two-stage optimization problems are solved. The algorithm is motivated by the well-known epsilon-constraint scalarization and its contribution lies in the way rectangles are defined and tracked. The algorithm is compared with former studies on multiobjective knapsack and multiobjective assignment problem instances. The method is highly competitive in terms of solution time and the number of optimization models solved.
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    A dynamic path planning approach for multirobot sensor-based coverage considering energy constraints
    (IEEE-Inst Electrical Electronics Engineers Inc, 2014) Yazici, Ahmet; Parlaktuna, Osman; Sipahioglu, Aydin; N/A; Kirlik, Gökhan; PhD Student; Graduate School of Sciences and Engineering; N/A
    Multirobot sensor-based coverage path planning determines a tour for each robot in a team such that every point in a given workspace is covered by at least one robot using its sensors. In sensor-based coverage of narrow spaces, i.e., obstacles lie within the sensor range, a generalized Voronoi diagram (GVD)-based graph can be used to model the environment. A complete sensor-based coverage path plan for the robot team can be obtained by using the capacitated arc routing problem solution methods on the GVD-based graph. Unlike capacitated arc routing problem, sensor-based coverage problem requires to consider two types of edge demands. Therefore, modified Ulusoy algorithm is used to obtain mobile robot tours by taking into account two different energy consumption cases during sensor-based coverage. However, due to the partially unknown nature of the environment, the robots may encounter obstacles on their tours. This requires a replanning process that considers the remaining energy capacities and the current positions of the robots. In this paper, the modified Ulusoy algorithm is extended to incorporate this dynamic planning problem. A dynamic path-planning approach is proposed for multirobot sensor-based coverage of narrow environments by considering the energy capacities of the mobile robots. The approach is tested in a laboratory environment using Pioneer 3-DX mobile robots. Simulations are also conducted for a larger test environment.
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    Capacitated arc routing problem with deadheading demands
    (Pergamon-Elsevier Science Ltd, 2012) Sipahioglu, Aydin; N/A; Kirlik, Gökhan; PhD Student; Graduate School of Sciences and Engineering; N/A
    Capacitated arc routing problem (CARP) is the determination of vehicle tours that serve all positive-demand edges (required edge) exactly once without exceeding vehicle capacity while minimizing sum of all tour costs. In CARP, total demand of a tour is calculated by means of all required edges on the tour. In this study, a new CARP variation is introduced, which considers not only required edges but also traversed edges while calculating total demand of the tour. The traversing demand occurs when the traversed edge is either servicing or non-servicing (deadheading). Since the new CARP formulation incurs deadheading edge demands it is called CARP with deadheading demands. An integer linear model is given for the problem which is used to solve small-sized instances, optimally. A constructive heuristic is presented to solve the problem which is a modified version of a well-known CARP heuristic. Furthermore, two post-optimization procedures are presented to improve the solution of the heuristic algorithm. The effectiveness of the proposed methods is shown on test problems, which are obtained by modifying CARP test instances.
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    A variable neighborhood search for minimizing total weighted tardiness with sequence dependent setup times on a single machine
    (Pergamon-Elsevier Science Ltd, 2012) N/A; N/A; Department of Industrial Engineering; Kirlik, Gökhan; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6033
    This paper deals with the single machine scheduling problem to minimize the total weighted tardiness in the presence of sequence dependent setup. Firstly, a mathematical model is given to describe the problem formally. Since the problem is NP-hard, a general variable neighborhood search (GVNS) heuristic is proposed to solve it. Initial solution for the GVNS algorithm is obtained by using a constructive heuristic that is widely used in the literature for the problem. The proposed algorithm is tested on 120 benchmark instances. The results show that 37 out of 120 best known solutions in the literature are improved while 64 instances are solved equally. Next, the GVNS algorithm is applied to single machine scheduling problem with sequence dependent setup times to minimize the total tardiness problem without changing any implementation issues and the parameters of the GVNS algorithm. For this problem, 64 test instances are solved varying from small to large sizes. Among these 64 instances, 35 instances are solved to the optimality, 16 instances' best-known results are improved, and 6 instances are solved equally compared to the best-known results. Hence, it can be concluded that the GVNS algorithm is an effective, efficient and a robust algorithm for minimizing tardiness on a single machine in the presence of setup times.
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    A new crossover operator for single machine total weighted tardiness problem with sequence dependent setup times
    (Gazi Üniversitesi, 2012) Kartal, Zuhal; Hasgül, Servet; N/A; Kirlik, Gökhan; PhD Student; Graduate School of Sciences and Engineering; N/A
    The single machine total weighted tardiness problem with sequence dependent setup times is a challenging and heavily studied problem. This problem is NP-hard, so several heuristics have been proposed in the literature so far. One of them is the genetic algorithm. The genetic algorithm is both powerful solution technique and applicable to wide range of different problem types, although its performance is heavily parameter and operator dependent. It is seen in literature that the well-conducted and adapted genetic algorithm operators and parameters increase the solution quality. In this study, a new crossover operator is proposed for the single machine with sequence dependent setup times problem to minimize the total weighted tardiness. The proposed crossover operator improves the relative positions by using apparent tardiness cost with setups (ATCS) heuristic while preserving the absolute positions. These are the two main aspects of the permutation type crossover operators for scheduling problems. The performance of the proposed crossover operator is tested by comparing it with partially mapped crossover (PMX) in different test cases using benchmark instances from literature. It is shown that the proposed ATCS based crossover operator gives better results than PMX in all test problems.
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    Computing the nadir point for multiobjective discrete optimization problems
    (Springer, 2015) N/A; N/A; Department of Business Administration; Kirlik, Gökhan; Sayın, Serpil; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 6755
    We investigate the problem of finding the nadir point for multiobjective discrete optimization problems (MODO). The nadir point is constructed from the worst objective values over the efficient set of a multiobjective optimization problem. We present a new algorithm to compute nadir values for MODO with objective functions. The proposed algorithm is based on an exhaustive search of the -dimensional space for each component of the nadir point. We compare our algorithm with two earlier studies from the literature. We give numerical results for all algorithms on multiobjective knapsack, assignment and integer linear programming problems. Our algorithm is able to obtain the nadir point for relatively large problem instances with up to five-objectives.
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    PublicationOpen Access
    A new crossover operator for single machine total weighted tardiness problem with sequence dependent setup times
    (Gazi Üniversitesi, 2012) Kartal, Zuhal; Hasgül, Servet; N/A; Kirlik, Gökhan; College of Engineering
    The single machine total weighted tardiness problem with sequence dependent setup times is a challenging and heavily studied problem. This problem is NP-hard, so several heuristics have been proposed in the literature so far. One of them is the genetic algorithm. The genetic algorithm is both powerful solution technique and applicable to wide range of different problem types, although its performance is heavily parameter and operator dependent. It is seen in literature that the well-conducted and adapted genetic algorithm operators and parameters increase the solution quality. In this study, a new crossover operator is proposed for the single machine with sequence dependent setup times problem to minimize the total weighted tardiness. The proposed crossover operator improves the relative positions by using apparent tardiness cost with setups (ATCS) heuristic while preserving the absolute positions. These are the two main aspects of the permutation type crossover operators for scheduling problems. The performance of the proposed crossover operator is tested by comparing it with partially mapped crossover (PMX) in different test cases using benchmark instances from literature. It is shown that the proposed ATCS based crossover operator gives better results than PMX in all test problems.