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 op...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.展开更多
基金The research presented in this paper was supported in part by the National Natural Science Foundation of China(Grant Nos.60274011,60574087,60704008,and 90924001)the National High Technology Research and Development Program of China(Grant No.2007AA04Z154)+2 种基金the Program for New Century Excellent Talents in University(NCET-04-0094)the Specialized Research Fund for the Doctoral Program of Higher Education(20070003110)the 111 International Collaboration Project(B06002).
文摘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.
