We present a master planning problem that arises in semiconductor manufacturing. The problem consists indetermining appropriate wafer quantities for several products, facilities, and periods of time. Different demandtypes, I.e. confirmed orders and forecasts, are considered. We use a combined objective function that takesproduction costs for in-house locations, subcontracting costs, inventory costs, and costs due to unmet demand intoaccount. Demand fulfillments and capacity constraints are considered. We present a genetic algorithm to solve themaster planning problem heuristically. The results of some computational experiments are presented. We comparethe results for small size test instances with results obtained from a commercial MIP solver. The genetic algorithm isable to produce solutions with reasonable quality in little time.
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