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In this work, we address the flexible job shop scheduling problem (FJSSP), which is a classification of the well-known job shop scheduling problem. This problem can be encountered in real-life applications such as automobile assembly, aeronautical, textile, and semiconductor manufacturing industries. To represent inherent uncertainties in the production process, we consider stochastic flexible job shop scheduling problem (SFJSSP) with operation processing times represented by random variables following a known probability distribution. To solve this stochastic combinatorial optimization problem we propose a simulation-optimization approach to minimize the expected makespan. Our approach employs Monte Carlo simulation integrated into a Jaya algorithm framework. Due to the unavailability of standard benchmark instances in SFJSSP, our algorithm is evaluated on an extensive set of well-known FJSSP benchmark instances that are extended to SFJSSP instances. Computational results demonstrate the performance of the algorithm at different variability levels through the use of reliability-based methods.

In this work, a hybrid artificial bee colony algorithm is proposed for solving the flexible job shop scheduling problem (FJSP) which is a classification of the classical job shop scheduling problem (JSP) considered to NP-hard in nature. In FJSP, an operation can be processed on a set of capable machines with different processing times, thereby dealing with a routing and sequencing problem. The objective considered is to minimize the makespan. The basic artificial bee colony (ABC) algorithm stresses on the balance between global exploration and local exploitation. However, the drawback of the basic ABC algorithm is that it converges prematurely and may get trapped in the local optima. Hence to improve its exploration capability in local space, it is hybridized using a Tabu search (TS) algorithm. At first, initial solutions are generated with certain quality and diversity as food sources using multiple strategies in combination. Crossover and mutation operations are carried out for machine assignment and operation sequencing separately generating new neighboring solutions. Lastly, a local search strategy based on TS is proposed to enhance the local search capability. Kacem's and Brandimarte's benchmark instances are used to compare the performance of the proposed approach to five other well-known algorithms in the literature. Experimental results revealed the superiority of the proposed approach in solving FJSP.