| This research focuses on large-scale manufacturing systems having a number of stations with multiple tools and product types with different and deterministic processing steps. The objective is to determine the production quantities of multiple products and the tool requirements of each station that maximizes net profit while satisfying strategic constraints such as cycle times, required throughputs, and investment. The formulation of the problem, named OptiProfit, is a mixed-integer nonlinear programming (MINLP) with the stochastic issues addressed by mean-value analysis (MVA) and queuing network models. Observing that OptiProfit is an NP-complete, nonconvex, and nonmonotonic problem, the research develops a heuristic method, Differential Coefficient Based Search (DCBS). It also performs an upper-bound analysis and a performance comparison with six variations of Greedy Ascent Procedure (GAP) heuristics and Modified Simulated Annealing (MSA) in a number of randomized cases. An example problem based on a semiconductor manufacturing minifab is modeled as an OptiProfit problem and numerically analyzed. The proposed methodology provides a very good quality solution for the high-level design and operation of manufacturing facilities. |
