Long-range process planning under uncertainty via parametric programming Academic Article

abstract

• Scenario analysis within a multi-stage stochastic programming formulation offers an attractive framework for modelling uncertainties in long-range planning models. However, whether the expected outcome is implicitly/explicitly evaluated, such formulations lead to computationally intensive optimization problems. Focussing here on the scenario planning/multi-period approach for mixed integer linear programming (MILP) problems under uncertainty, this paper presents a novel decomposition strategy for its solution. The proposed algorithm circumvents the direct solution of the large-scale deterministic equivalent MILP problem by exploiting its block-angular structure. Through the use of parametric programming, separable subproblems are formulated and solved in parallel at a relatively low computational cost. Computational studies show that the algorithm is ideally suited for problems where a large number of scenarios inhibits the direct solution of the deterministic equivalent problem. 2005 Elsevier B.V. All rights reserved.

authors

• Pistikopoulos, Stratos

published proceedings

• EUROPEAN SYMPOSIUM ON COMPUTER-AIDED PROCESS ENGINEERING-15, 20A AND 20B

author list (cited authors)

• Hugo, A., & Pistikopoulos, S.

citation count

• 2

complete list of authors

• Hugo, A||Pistikopoulos, S

publication date

• December 2005

publisher

• Elsevier  Publisher

keywords

• Long-range Capacity Optimization
• Parametric Programming
• Scenario Analysis
• Strategic Planning
• Two-stage Stochastic Programming

Digital Object Identifier (DOI)

• 10.1016/s1570-7946(05)80143-9

start page

• 127

end page

• 132

volume

• 20a-20b

issue

• C

URL

• http://dx.doi.org/10.1016/s1570-7946(05)80143-9