Remanufacturing planning based on constrained ordinal optimization

Abstract

Resource planning for a remanufacturing system is in general extremely difficult in terms of problem size, uncertainties, complicated constraints, etc. In this paper, we present a new method based on constrained ordinal optimization (COO) for remanufacturing planning. The key idea of our method is to estimate the feasibility of plans by machine learning and to select a subset with the estimated feasibility based on the procedure of horse racing with feasibility model (HRFM). Numerical testing shows that our method is efficient and effective for selecting good plans with high probability. It is thus a scalable optimization method for large scale remanufacturing planning problems with complicated stochastic constraints.

Keywords

remanufacturing systems / constrained ordinal optimization (COO) / simulation-based optimization / machine learning

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