A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions. Frequently, these nominal distributions are themselves estimated from data, which makes them susceptible to estimation errors. We thus propose to replace each nominal distribution with an ambiguity set containing all distributions … Read more
For many classical combinatorial optimization problems such as, e.g., the shortest path problem or the spanning tree problem, the robust counterpart under general discrete, polytopal, or ellipsoidal uncertainty is known to be intractable. This implies that any algorithm solving the robust counterpart that can access the underlying certain problem only by an optimization oracle has … Read more
Competitive facility location problem is a typical facility locating optimization problem but in a competitive environment. The main characteristic of this problem is the competitive nature of the market. In essence, the problem involves two competitors, i.e., a leader and a follower, who seek to attract customers by establishing new facilities to maximize their own … Read more
In this errata, we corrected the imprecise statements in “Polynomial Time Algorithms and Extended Formulations for Unit Commitment Problems” [IISE Transactions 50 (8): 735-751, 2018]. Article Download View Errata to "Polynomial Time Algorithms and Extended Formulations for Unit Commitment Problems"
In this paper, we study the complexity of the selection of a graph discretization order with a stepwise linear cost function.Finding such vertex ordering has been proved to be an essential step to solve discretizable distance geometry problems (DDGPs). DDGPs constitute a class of graph realization problems where the vertices can be ordered in such … Read more
We study the single allocation hub location problem with heterogeneous economies of scale (SAHLP-h). The SAHLP-h is a generalization of the classical single allocation hub location problem (SAHLP), in which the hub-hub connection costs are piecewise linear functions of the amounts of flow. We model the problem as an integer non-linear program, which we then … Read more
Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and … Read more
Nonlinear symmetric cone programming (NSCP) generalizes important optimization problems such as nonlinear programming, nonlinear semidefinite programming and nonlinear second-order cone programming (NSOCP). In this work, we present two new optimality conditions for NSCP without constraint qualifications, which implies the Karush-Kuhn-Tucker conditions under a condition weaker than Robinson’s constraint qualification. In addition, we show the relationship … Read more
We study a class of single-objective nonlinear optimization problems, the so-called Integer Linear Generalized Maximum Multiplicative Programs (IL-GMMP). This class of optimization problems has a significant number of applications in different fields of study including but not limited to game theory, systems reliability, and conservative planning. An IL-GMMP can be reformulated as a mixed integer … Read more
Adaptive robust optimization is a modelling paradigm for multistage optimization under uncertainty where one seeks decisions that minimize the worst-case cost with respect to all possible scenarios in a prescribed uncertainty set. However, optimal policies for adaptive robust optimization problems are difficult to compute. Therefore, one often restricts to the class of affine policies which … Read more