Premium
Stochastic inventory management for tactical process planning under uncertainties: MINLP models and algorithms
Author(s) -
You Fengqi,
Grossmann Ignacio E.
Publication year - 2011
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.12338
Subject(s) - mathematical optimization , safety stock , computer science , stochastic programming , exploit , production (economics) , nonlinear system , linear programming , piecewise linear function , operations research , supply chain , mathematics , economics , physics , computer security , quantum mechanics , political science , law , geometry , macroeconomics
We address in this article the mid‐term planning of chemical complexes with integration of stochastic inventory management under supply and demand uncertainty. By using the guaranteed service approach to model time delays in the flows inside the network, we capture the stochastic nature of the supply and demand variations, and develop an equivalent deterministic optimization model to minimize the production, feedstock purchase, cycle inventory, and safety stock costs. The model determines the optimal purchases of the feedstocks, production levels of the processes, sales of final products, and safety stock levels of all the chemicals. We formulate the model as a mixed‐integer nonlinear program with a nonconvex objective function and nonconvex constraints. To solve the global optimization problem with modest computational times, we exploit some model properties and develop a tailored branch‐and‐refine algorithm based on successive piecewise linear approximation. Five industrial‐scale examples with up to 38 processes and 28 chemicals are presented to illustrate the application of the model and the performance of the proposed algorithm. © 2010 American Institute of Chemical Engineers AIChE J, 2011