Optimal remanufacture-up-to strategy with uncertainties in acquisition quality, quantity, and market demand

Abstract

Remanufacturing promotes the limiting of waste and increasing the reuse of recoverable components from obsolete products. However, in most relevant studies, the condition of returned end of life (EOL) items is taken as a constant, which causes deviation in the cost of remanufacturing. Some researchers assume it as a discrete series, which also isolates the uncertainty analysis of quality from other uncertainty factors in the remanufacturing systems. This study considers the uncertainties in acquisition quantity, quality, and market demand, then establishes the relationships among production indices and remanufacture-up-to ratio to derive the optimum production strategies. Using the Lagrange multiplier method, we proved the existence of these optimum solutions and deduced the close-form expressions for minimum cost and maximum profit. Validated by numerical examples, our results indicated that the assumption of a condition distribution type had little influence on the optimal strategies or the optimum remanufacture-up-to ratio increases, as the expected supply and demand gap becomes wider. The method employed by this model provides an innovative approach in uncertainty analysis and helps remanufacturers adopt preemptive policies for acquisition and production.