Mohammad Komaki

TL;DR: This study considers an X-bar control chart design problem with multiple and often conflicting objectives, including the expected time the process remains in statistical control status, the type-I error, and the detection power, and applies multi-objective optimization methods founded on the reference-points-based non-dominated sorting genetic algorithm-II and MOPSO to efficiently solve the optimization problem.

TL;DR: This paper addresses minimizing makespan of the distributed no-wait flow shop scheduling problem where there are several identical factories that work in parallel and the goal is finding allocation and sequence of jobs on each factory such that the completion time of the last job processed in the system is minimized.

TL;DR: The applicability and efficiency of the proposed hybrid approach in providing the optimal values of all parameters of the evolutionary optimization algorithms to optimize their performance in solving an optimization problem is demonstrated.

TL;DR: The DCOA developed in this study contains several new adjustments that allow it to solve large-sized discrete problems, including a grouping strategy based on the Jaccard similarity coefficient, as well as modified egg laying and immigration processes.

TL;DR: A hybrid meta-heuristic algorithm based on Harmony Search Algorithm with improved search ability and quality of generated harmony is developed to address the permutation flow shop scheduling problem with sequence dependent setup times between jobs.