Hierarchical capacity expansion and production planning decisions in stochastic manufacturing systems

The authors present an approach of hierarchical decision making in production planning and capacity expansion problems under uncertainty. They show that under reasonable assumptions, the strategic level management can base the capacity decision on aggregated information from the shopfloor, and the operational level management, given this decision, can derive a production plan for the system, without too large a loss in optimality when compared to simultaneous determination of optimal capacity and production decisions. The results are obtained via an asymptotic analysis of hierarchical investment and production decisions in a manufacturing system with machines subject to breakdown and repair. The demand facing the system is assumed to be a deterministic monotone increasing function. The production capacity can be increased by purchasing a finite number of new machines over time. The control variables are a sequence of purchasing times and a production plan. The rate of change in machine states is assumed to be much larger than the rate of discounting of costs. This gives rise to a limiting problem in which the stochastic machine availability is replaced by the equilibrium mean availability. The value function for the original problem converges to the value function of the limiting problem. Three different methods are developed for constructing decisions for the original problem from the optimal solution of the limiting problem in a way which guarantees the asymptotic optimality of constructed decisions. Finally, it is shown that as the number of machines that could be purchased tends to infinity, the problem approximates the corresponding problem with no limit on number of machine purchases.