Modern manufacturing systems are part of a complex supply chain where customer preferences are constantly evolving. The rapidly evolving market demands manufacturing organizations to be increasingly agile and flexible. Medium term capacity planning for manufacturing systems employ queueing network models based on stationary demand assumptions. However, these stationary demand assumptions are not very practical for rapidly evolving supply chains. Nonstationary demand processes provide a reasonable framework to capture the time-varying nature of modern markets. The analysis of queues and queueing networks with time-varying parameters is mathematically intractable. In this dissertation, heuristics which draw upon existing steady state queueing results are proposed to provide computationally efficient approximations for dynamic multi-product manufacturing systems modeled as time-varying queueing networks with multiple customer classes (product types). This dissertation addresses the problem of performance evaluation of such manufacturing systems.

This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.