Genetic algorithm for flexible job shop scheduling problem - a case study

This work proposes the impact assessment of the workers in the optimal time of operations in a Flexible Job Shop Scheduling Problem. In this work, a real enterprise was studied. The problem consists in finding the workers operations schedule, taking into account the precedence constraints. The scheduling of operations is a complex problem consisting in determining the optimal allocation of tasks to resources under a set of constraints, which in enterprise business assumes a critical issue. Solving this issue requires the use of optimization techniques that guarantees the achievement of acceptable solutions as optimized as possible. In industrial environments, characterized by the frequent occurrence of unplanned disturbances and changes, the optimal plan becomes inapplicable and obsolete very fast. This introduces a new requirement to the use of optimization techniques, where besides the quality of the calculated solution, it is also crucial to consider the time to compute the solution. This paper studies the application of a genetic algorithm approach to determine the scheduling in an industrial factory plant organized as flexible job shop problem. Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the traditional Job Shop Scheduling Problem, differing from this in the sense that some workers may be capable of performing more than one type of tasks. Additionally, for each task there is at least, one worker that is capable of performing the operation. The main objective is to minimize the finish time of the last task completed in the schedule.