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    Maximum completion time under a learning effect in the permutation flowshop scheduling problem
    (Univ Cincinnati Industrial Engineering, 2018) Mustu, Settar; Eren, Tamer
    The permutation flowshop scheduling problem under a position-based learning effect is addressed in this study. Minimization of the maximum completion time (make span) is considered for the identified problem. The mathematical programming model is established to find optimal solutions for small-sized problems. Furthermore, meta-heuristics are developed to achieve effective solutions for large-sized problems encountered in real applications. These meta-heuristics are the genetic algorithm which is a population-based solution approach, the kangaroo and the variable neighborhood search algorithms which both are single-solution-based solution approaches. In addition, different solution methods, which are in the literature for similar problem structures, are also used. Improved heuristics are evaluated according to optimal results for small-sized problems and according to performance differences between each other for large-sized problems.
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    The single machine scheduling problem with sequence-dependent setup times and a learning effect on processing times
    (Elsevier Science Bv, 2018) Mustu, Settar; Eren, Tamer
    For the past few years, learning effect has been receiving wide attention in the scheduling problems. There are many studies that take into account the learning effect with several structural constraints and some specific conditions of machine scheduling problems. However, very few studies have explored the scheduling problem including learning effect and sequence-dependent setup times simultaneously, although both are found in the scheduling literature. Therefore, this study addresses an NP-Hard problem of minimizing the total tardiness on a single machine with sequence-dependent setup times and a position-based learning effect on processing times. Several dominance properties are verified and a lower bound is provided to develop a branch and bound algorithm for solving the problem. In addition, four heuristic algorithms (two genetic algorithms and two variable neighborhood search algorithms) are developed in order to obtain near optimal solutions in reasonable time limits. Furthermore, some computational experiments are performed on randomly generated test problems to evaluate the performance of the algorithms. (C) 2018 Elsevier B.V. All rights reserved.