This paper deals with a fuzzy group shop scheduling problem. The group shop scheduling problem is a general formulation that includes the flow shop, the job shop, and the open shop scheduling problems. Job release dates and processing times are considered to be triangular fuzzy numbers. The objective is to find a job schedule that minimizes the maximum completion time, or makespan. First, the problem is formulated in a form of fuzzy programming and then, prepared in a form of deterministic mixed binary integer linear programming by applying the chance-constrained programming. To solve the problem, an efficient genetic algorithm hybridized with an improvement procedure is developed. Both Lamarckian and Baldwinian versions are then implemented and evaluated through computational experiments.
