A neutrosophic set-based TLBO algorithm for the flexible job-shop scheduling problem with routing flexibility and uncertain processing times

Abstract Different with the plain flexible job-shop scheduling problem (FJSP), FJSP routing flexibility is more complex and it can be deemed as integrated process planning (job shop) (IPPS) problem, where job shop two important functions are considered a whole optimized simultaneously to utilize in manufacturing system. Although, many novel meta-heuristics have been introduced address this corresponding fruitful results observed; dilemma real-life applications of resultant schemes stems from uncertainty or nondeterminacy processing times, since times will disturb predefined scheme by influencing unfinished operations. As result, performance system also deteriorated. Nevertheless, research on such issue has seldom before. This focuses modeling optimization method IPPS uncertain times. The neutrosophic set first model Due complexity math model, we developed an improved teaching-learning-based optimization(TLBO) algorithm capture robust schemes. In proposed method, score values completion each machine compared obtain most promising solution. Distinct levels fluctuations uncertainties defined testing well-known Kim’s benchmark instances. computational analyzed competitive solutions smaller obtained. Computational show that Gantt charts obtained; general, TLBO suggested better than those other algorithms function values. gives ideas clues for problems