Abstract
This paper concerns the problem of cyclic job shop scheduling problem with linear constraints. The main characteristic of this problem is that the tasks of each job are generic, that is, they have an infinite number of occurrences; moreover, these tasks are constrained by linear precedence constraints. The general approach to solving this scheduling problem, is based on the coupling of a genetic algorithm and a scheduler. The scheduler uses a Petri net to construct a feasible solution step by step, respecting the linear constraints between the tasks. The genetic algorithm aims at proposing heuristics for solving the resource conflicts that occur during the simulation process achieved by the scheduler. Finally, a benchmark and some preliminary results of this approach are presented.
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