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Remanufacturing planning based on constrained ordinal optimization |
Chen SONG1, Xiaohong GUAN2,3, Qianchuan ZHAO2, Qing-Shan JIA2( ) |
| 1. Ubiquitous Energy Research Center, ENN Research and Development Corporation, Langfang 065001, China; 2. Center for Intelligent and Networked Systems, Department of Automation, TNLIST, Tsinghua University, Beijing 100084, China; 3. State Key Laboratory for Manufacturing Systems Engineering (SKLMS Lab) and Ministry of Education Key Laboratory for Intelligent Network and Network Security (MOE KLINNS Lab), Xi’an Jiaotong University, Xi’an 710049, China |
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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.
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| Keywords
remanufacturing systems
constrained ordinal optimization (COO)
simulation-based optimization
machine learning
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Corresponding Author(s):
JIA Qing-Shan,Email:jiaqs@tsinghua.edu.cn
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Issue Date: 05 September 2011
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