Our work focuses on an extension of the well known job shop scheduling problem. We call this extension the General Flexible Job Shop Scheduling Problem (GFJSSP). It occurs in various kinds of workshops which are particularly constrained by one or several transportation resources (called robots). GFJSSP is characterized by the flexibility of both machines and robots. In the studied problem, the term General involves unitary capacity transportation resources, bounded processing times, and possible input/output buffers for machines. After defining the workshop and the corresponding problem, we proposed two kinds of model for the GFJSSP: a mathematical model, and a graphical one. This last one is a generalization of the disjunctive graph commonly used for job shop problems. We then addressed the resolution in two steps: firstly, by focusing on the sequencing of processing and transportation tasks. For this purpose we have developed two heuristics (Tabu search and an improved shifting bottleneck procedure). Secondly, we have considered the assignment problem involved by the flexibility of some resources. For this last step, we combined the above methods with a genetic algorithm. This hybrid algorithm allowed us to solve various specific cases of instances in the literature, with performance rather close to the best dedicated methods. In the future, it could be integrated within a general decision support system which could emancipate from the initial identification phase of the considered type of job shop, and which would be suitable for solving many cases (with or without assignment problem, fixed or bounded processing times, with or without storage, and so on).
