Abstract
The goal of this project was to compute minimal cost solutions satisfying the demand of pre-given product portfolios and to investigate the dependence of the fix costs and investment costs on the product portfolio.
The most important parameters characterizing the production facilities are the number and the size of the reactors. The production is subject to shelf-life constraints, i.e., products cannot be stored longer than one week.
Even if we analyze this problem under the simple assumption of constant batch sizes and limit ourself to only one time period covering one week, the computation of minimum cost scenarios requires that we determine global minima of a nonconvex MINLP problem. An objective function built up by the sum of concave functions and trilinear products terms involving the variables describing the number of batches, the utilization rates and the volume of the reactor are the nonlinear features in the model.
We have successfully applied four different solution techniques to solve this problem. (1) An exact transformation allows us to represent the nonlinear constraints by MILP constraints. Using piecewise linear approximations for the objective function the problem is solved with XPress-MP, a commercial MILP solver. (2) The local MINLP Branch-and-Bound solver SBB which is part of the modeling system GAMS. (3) The Branch&Reduce Optimization Navigator (BARON) also called from GAMS. (4) A taylorized Branch&Bound approach based on the construction of a lower bounding problem by underestimating the concave objective function with piecewise linear approximations described in a forecoming paper.
Our overall conclusion from a detailed analysis of specific portfolio cases is that the problem, for some cases, can be solved with nowadays standard solvers’ capacities but it requires a lot of CPU time. Therefore, in order not to cover only special cases and also to cope with the scaling properties of this problem suffering from weak lower bounds, we recommend to use taylorized approaches in addition.
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References
C. S. Adjiman, I. P. Androulakis, and C. A. Floudas. Global Optimization of Mixed-Integer Nonlinear Problems. AIChE, 46:1769–1797, 2000.
C. S. Adjiman, S. Dallwig, C. A. Floudas, and A. Neumaier. A Global Optimization Method, αBB, for General Twice-differentiable Constrained NLPs - I. Theoretical Advances. Computers and Chemical Engineering, 22:1137–1158, 1998.
C. S. Adjiman, S. Dallwig, C. A. Floudas, and A. Neumaier. A Global Optimization Method, cxBB, for General Twice-differentiable Constrained NLPs - II. Implementation and Computational Results. Computers and Chemical Engineering, 22:1159–1179, 1998.
R. W. Ashford and R. C. Daniel. LP-MODEL XPRESS-LP’s model builder. Institute of Mathematics and its Application Journal of Mathematics in Management, 1:163–176, 1987.
A. Brooke, D. Kendrick, and A. Meeraus. GAMS - A User’s Guide (Release 2.25). Boyd & Fraser Publishing Company, Danvers, Massachusetts, 1992.
C. A. Floudas. Nonlinear and Mixed-Integer Optimization. Oxford University Press, Oxford, UK, 1995.
C. A. Floudas. Deterministic Global Optimization: Theory, Methods and Applications. Kluwer Academic Publishers, Dordrecht, The Netherlands, 2000.
V. Ghildyal and N. V. Sahinidis. Solving Global Optimization Problems with BARON. In A. Migdalas, P. Pardalos, and P. Varbrand, editors, From Local to Global Optimization. A Workshop on the Occasion of the 70th Birthday of Professor Hoang Tuy, pages 205–230, Boston, MA, 2001. Kluwer Academic Publishers.
J. Kallrath and J. M. Wilson. Business Optimisation Using Mathematical Programming. Macmillan, Houndmills, Basingstoke, UK, 1997.
X. Lin, C. A. Floudas, and J. Kallrath. Global Solution Approaches for Nonconvex MINLP Problems in Product Portfolio Optimization. Journal of Global Optimization, submitted, 2003.
M. Tawarmalani and N. V. Sahinidis. Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming: Theory, Algorithms, Software, and Applications. Nonconvex Optimization And Its Applications Series. Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002.
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Kallrath, J. (2004). Exact Computation of Global Minima of a Nonconvex Portfolio Optimization Problem. In: Floudas, C.A., Pardalos, P. (eds) Frontiers in Global Optimization. Nonconvex Optimization and Its Applications, vol 74. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0251-3_13
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DOI: https://doi.org/10.1007/978-1-4613-0251-3_13
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