TY  - JOUR
SN  - 0098-1354
UR  - http://dx.doi.org/10.1016/j.compchemeng.2018.04.015
ID  - discovery10049261
N2  - Major application areas of the process systems engineering, such as hybrid control, scheduling and synthesis can be formulated as mixed integer linear programming (MILP) problems and are naturally susceptible to uncertainty. Multi-parametric programming theory forms an active field of research and has proven to provide invaluable tools for decision making under uncertainty. While uncertainty in the right-hand side (RHS) and in the objective function's coefficients (OFC) have been thoroughly studied in the literature, the case of left-hand side (LHS) uncertainty has attracted significantly less attention mainly because of the computational implications that arise in such a problem. In the present work, we propose a novel algorithm for the analytical solution of multi-parametric MILP (mp-MILP) problems under global uncertainty, i.e. RHS, OFC and LHS. The exact explicit solutions and the corresponding regions of the parametric space are computed while a number of case studies illustrates the merits of the proposed algorithm.
KW  - Optimisation under uncertainty
KW  -  Multi-parametric programming
KW  -  Mixed integer linear programming
KW  -  Cylindrical algebraic decomposition
KW  -  Grobner bases
KW  -  Process scheduling
A1  - Charitopoulos, VM
A1  - Papageorgiou, LG
A1  - Dua, V
JF  - Computers and Chemical Engineering
EP  - 295
AV  - public
VL  - 116
Y1  - 2018/08/04/
SP  - 279
TI  - Multi-parametric mixed integer linear programming under global uncertainty
N1  - Copyright © 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/).
ER  -