A new algorithm for cyclic scheduling and design of multipurpose batch plants

Many researchers have studied the scheduling, planning, and design of multipurpose batch processes. However, not so many studies have treated design and scheduling or design and planning simultaneously. The complexity of these systems stems from the fact that plant configuration must be determined for the purpose of process scheduling, yet scheduling must be done to devise the plant configuration. In the present study, a new algorithm for determining the best multipurpose scheduling and plant configuration is suggested. Since the objective function of the problem is nonlinear, it is linearized using a separable programming method. The proposed method consists of a number of procedures. First, a feasible configuration is obtained. Next, both the optimum equipment size and cyclic scheduling are determined for the plant configuration obtained in the first procedure. Last, the evolutionary design method proposed by Fuchino et al.(J. Chem. Eng. Jpn. 1994,27, 57-64) is used to find the solution that minimizes the total cost. The efficacy of the proposed approach is demonstrated in three examples. Usually, equipment sizes are considered by a continuous variable in mixed integer linear programming and task processing time is assumed to be constant. However, most types of equipment are manufactured only in discrete volume classes. In addition, processing times are dependent on batch sizes. Hence, to apply the optimization methods developed here to real industries, the method was modified such that the volumes of equipment are considered as discrete variables and the processing time is a function of batch size.