Intelligent scheduling model and algorithm for manufacturing

This paper puts forward an intelligent scheduling model based on Hopfield n eural network and a unified algorithm for manufacturing. The energy computa tion function and its dynamic state equation are derived and discussed in d etail about their coefficients (parameters) and steps (Delta t) in iteratio n rewards convergence. The unified model is focused on the structure of the above function and equation, in which the goal and penalty items must be i nvolved and meet different schedule models. The applications to different schedule mode including jobshop static schedu ling, scheduling with due-date constraint or priority constraint. dynamic s cheduling, and JIT (just in time) scheduling are discussed, and a series of examples with Gantt charts are illustrated.