Yazar "Güngör, Zülal" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Öğe
    Analyzing the Behaviors of Virtual Cells (VCs) and Traditional Manufacturing Systems: Ant Colony Optimization (ACO)-Based Metamodels
    (2009) Kesen, Sadettin Erhan; Toksarı, M. Duran; Güngör, Zülal; Güner, Ertan
    The aim of this paper is two fold. First we investigate the three different types of systems, namely cellular layout (CL), process layout (PL) and virtual cells (VCs). VCs are addressed by using family-based scheduling rule, developed by a part allocation algorithm in a PL. Simulation is used to compare three types of systems under the performance metrics such as mean flow time and mean tardiness. Results indicate that VCs have better responsiveness in terms of the performance metrics. Second we develop a new ant colony optimization-based metamodels fed by existing simulation runs to represent the prospective simulation runs, which require a lot of time and effort. Regression metamodels, which allow us to obtain much faster results, are seen to be promising to estimate the systems behaviors.
  • Küçük Resim
    Öğe
    A Genetic Algorithm Based Heuristic for Scheduling of Virtual Manufacturing Cells (VMCs)
    (Pergamon-Elsevier Science Ltd, 2010) Kesen, Saadettin Erhan; Das, Sanchoy K.; Güngör, Zülal
    We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.
  • Küçük Resim
    Öğe
    A Mixed Integer Programming Formulation for Scheduling of Virtual Manufacturing Cells (VMCs)
    (Springer London Ltd, 2010) Kesen, Saadettin Erhan; Das, Sanchoy K.; Güngör, Zülal
    We present a multi-objective mixed integer programming formulation for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part family as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. The two scheduling objectives are makespan minimization and minimizing total traveling distance. Since batch splitting is permitted in the system, scheduling decisions must tell us the (a) assignment of jobs to the machines, (b) the job starting time at each machine, and (c) the part quantity processed on different machines due to batch splitting. Under these decision variables, the objective function is to minimize the sum of the makespan and total traveling distance/cost. Illustrative examples are given to demonstrate the implementation of the model.