Flexible resource allocation in complex processing networks with *reliability considerations.

We consider the allocation of reconfigurable resources in a serial manufacturing line with machine failures. The objective is to develop resource allocation policies that decrease inventory holding costs and increase average throughput. Each station is equipped with non-idling dedicated servers while the whole system is equipped with a finite number of reconfigurable servers that are available to be assigned to any station. In a two station clearing system (without external arrivals) both with and without machine failures, we show the existence of an optimal monotone policy. Moreover, when all of the machines are reliable, the switching curve defined by this policy has slope greater than or equal to -1. This continues to hold true when the holding cost rate is higher at the first stage and machine failures are considered. In systems with external arrivals, we provide conditions for a policy to achieve throughput optimality. We also show in the two-station case that transition monotone optimal policies exist. We develop two-pairing heuristics for n-station production systems based on the transition monotone properties in two-station models. To date, most dynamic resource allocation research considers only two stations and less than two flexible servers. None of this research includes server reliability considerations. Our research shows the optimality of monotone control policies in systems with multiple servers and reliability considerations. Based on the optimality of monotone control policies, the two-pairing heuristics allow problems of practical dimension to be decomposed into sub-problems that can be solved as Markov Decision Processes and reduce average holding costs significantly. These heuristics are compared to several heuristics from the literature via a detailed numerical study. Compared to other dynamic resources allocation policies, the two-pairing heuristics create lookup tables for decision makers and no additional computation is required in daily operation.Ph.D.Applied SciencesIndustrial engineeringManagementOperations researchSocial SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125780/2/3208574.pd