Modeling and genetic solution of a class of flexible job shop scheduling problems

We consider an extended class of flexible job shop scheduling problems. First, we translate the problem into a mathematical programming formula, i.e., a mixed-integer programming problem. This makes it possible to apply standard packages of mixed integer programming solvers and, while lots of computational time is required in general, to obtain the optimal schedule. Then, in order to seek the schedules close to the optimal for larger-scale problems, we newly design a solution method by adopting genetic algorithms based on the formula. Through some computational experiments, the effectiveness and the possibility of the proposed approach are examined.