One of the main sources of uncertainty in remanufacturing applications is yield, since the state of the quality of returns is not always sufficient for successful recovery. In this talk, two inventory models under remanufacturing yield loss are focused on. In the first model, we investigate the desired level of recovery under various inventory control policies when the success of recovery is probabilistic. Returns depend on demand; each item used returns to the system. All the returns go into a recovery process that is modeled as a single stage operation. The recovery effort is represented by the expected time spent for it. The effects of increasing recovery effort on the success probability together with unit cost of the operation are included by assuming general forms of dependencies. Alternative to recovered items, demand is satisfied by brand-new ones supplied from an outside vendor. Four inventory control policies that differ in timing of and information used in purchasing decision are proposed. The objective is to find the recovery level together with inventory control parameter that minimize long-run average total cost. In the second model, we concentrate on the case where demand and return processes are independent, and manufacturing is an alternative to satisfy customer demand. Manufacturing and remanufacturing are performed in the same facility, but there are dedicated resources for these two modes of production. In this case, the probability that each return can successfully be remanufactured is a parameter. We propose four different alternative inventory-disposal rules that differ in information used to maximize long-run average profit.
In this talk the models, the results of the computational studies together with the managerial insights gained are presented.