Optimal Installation Program for Reprocessing Plants

Optimization of the program of installation of reprocessing plants is mathematically formulated as problem of mixed integer programming, which is numerically solved by the branch-and-bound method. A new concept of quasi-penalty is used to obviate the difficulties associated with dual degeneracy. The...

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Veröffentlicht in:Journal of nuclear science and technology 1976-11, Vol.13 (11), p.633-647
Hauptverfasser: KUBOKAWA, Toshihiko, KIYOSE, Ryohei
Format: Artikel
Sprache:eng
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Zusammenfassung:Optimization of the program of installation of reprocessing plants is mathematically formulated as problem of mixed integer programming, which is numerically solved by the branch-and-bound method. A new concept of quasi-penalty is used to obviate the difficulties associated with dual degeneracy. The finiteness of the useful life of the plant is also taken into consideration. It is shown that an analogous formulation is possible for the cases in which the demand forecasts and expected plant lives cannot be predicted with certainty The scale of the problem is found to have kN binary variables, (k + 2)N continuous variables, and (k + 3)N constraint conditions, where k is the number of intervals used in the piece-wise linear approximation of a nonlinear objective function, and N the overall duration of the period covered by the installation program. Calculations are made for N=24 yr and k = 3, with the assumption that the plant life is 15 yr, the plant scale factor 0.5, and the maximum plant capacity 900 (t/yr). The results are calculated and discussed for four different demand forecasts. The difference of net profit between optimal and non-optimal installation programs is found to be in the range of 50-100 M$. The pay-off matrix is calculated, and the optimal choice of action when the demand cannot be forecast with certainty is determined by applying Bayes' theory. The optimal installation program under such conditions of uncertainty is obtained also with a stochastic mixed integer programming model.
ISSN:0022-3131
1881-1248
DOI:10.1080/18811248.1976.9734084