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We propose a class of functions, called multiple objective satisficing (MOS) criteria, for evaluating the level of compliance of a set of objectives in meeting their targets collectively under uncertainty. The MOS criteria include the targets' achievement probability (success probability criterion) as a special case and also extend to situations when the(More)
Satisficing, as an approach to decision-making under uncertainty, aims at achieving solutions that satisfy the problem's constraints as well as possible. Mathematical optimization problems that are related to this form of decision-making include the P-model of Charnes and Cooper (1963), where satisficing is the objective, as well as chance-constrained and(More)
a r t i c l e i n f o In this work we develop a new approach to study the energy import resilience of an economy using linear programming and economic input–output analysis. In particular, we propose an energy import resilience index by examining the maximum level of energy import reduction that the economy can endure without sacrificing domestic demands. A(More)
Many important dynamic systems operate in uncertain environments, and in many cases even small levels of noise can severely compromise their performance and stability. In this work, we propose a robust design approach to improve the performance of such systems. Our approach first specifies a set of linear constraints describing dynamic performance(More)
Modularity density maximization is a clustering method that improves some issues of the commonly-used modularity maximization approach. Recently, some Mixed-Integer Linear Programming (MILP) reformulations have been proposed in the literature for the modularity density maximization problem, but they require as input the solution of a set of auxiliary binary(More)