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  • 1.
    Gholami, Omid
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Inst Technol, Karlskrona, Sweden..
    Sotskov, Y. N.
    Natl Acad Sci Belarus, BLR.
    Werner, F.
    Otto von Guericke Univ, DEU.
    Zatsiupo, A. S.
    Servolux, Mogilev, BLR.
    Heuristic Algorithms to Maximize Revenue and the Number of Jobs Processed on Parallel Machines2019In: Automation and remote control, ISSN 0005-1179, E-ISSN 1608-3032, Vol. 80, no 2, p. 297-316Article in journal (Refereed)
    Abstract [en]

    A set of jobs has to be processed on parallel machines. For each job, there are given a release time and a due date and the job must be processed no later than its due date. If the job will be completed no later than the given due date, a benefit will be earned. Otherwise, this job will be rejected and the benefit will be discarded. The criterion under consideration is to maximize the weighted sum of the benefits and the number of jobs processed in time. Some properties of the objective function are found which allow to construct a optimal schedule. We develop a simulated annealing algorithm, a tabu search algorithm, and a genetic algorithm for solving this problem. The developed algorithms were tested on moderate and large instances with up to 500 jobs and 50 machines. Some recommendations are given showing how to use the obtained results and developed algorithms in production planning.

  • 2.
    Gholami, Omid
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sotskov, Yuri
    United Institute Of Informatics Problems, BLR.
    Werner, Frank
    Otto-von-Guericke-Universität, DEU.
    A Genetic algorithms for hybrid job-shop problems with minimizing the makespan and mean flow time2018In: Journal of Advanced Manufacturing Systems, ISSN 0219-6867, Vol. 17, no 4, p. 461-486Article in journal (Refereed)
    Abstract [en]

    We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean ow time. In the hybrid job-shop, machines of type k are available for processing the specific subset O^k of the given operations. Each set O^k maybe partitioned into subsets for their processing on the machines of type k. Solving the hybrid job-shopproblem implies the solution of two subproblems: an assignment of all operations fromthe set O^k to the machines of type k and nding optimal sequences of the operationsfor their processing on each machine. In this paper, a genetic algorithm is developedto solve these two subproblems simultaneously. For solving the subproblems, a specialchromosome is used in the genetic algorithm based on a mixed graph model. We com-pare our genetic algorithms with a branch and bound algorithms and three other recentheuristic algorithms from the literature. Computational results for benchmark instanceswith 10 jobs and up to 50 machines show that the proposed genetic algorithm is ratherecient for both criteria. Compared with the other heuristics, the new algorithm givesmost often an optimal solution and the average percentage deviation from the optimalfunction value is about 4 %.

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