Knapsack problems—An overview of recent advances. Part II: Multiple, multidimensional, and quadratic knapsack problems
After the seminal books by Martello and Toth (1990) and Kellerer, Pferschy, and Pisinger
(2004), knapsack problems became a classical and rich research area in combinatorial …
(2004), knapsack problems became a classical and rich research area in combinatorial …
The Benders decomposition algorithm: A literature review
The Benders decomposition algorithm has been successfully applied to a wide range of
difficult optimization problems. This paper presents a state-of-the-art survey of this algorithm …
difficult optimization problems. This paper presents a state-of-the-art survey of this algorithm …
A framework for sensitivity analysis of decision trees
In the paper, we consider sequential decision problems with uncertainty, represented as
decision trees. Sensitivity analysis is always a crucial element of decision making and in …
decision trees. Sensitivity analysis is always a crucial element of decision making and in …
Improving online algorithms via ML predictions
In this work we study the problem of using machine-learned predictions to improve
performance of online algorithms. We consider two classical problems, ski rental and non …
performance of online algorithms. We consider two classical problems, ski rental and non …
[ΒΙΒΛΙΟ][B] Set-valued optimization
AA Khan, C Tammer, C Zalinescu - 2016 - Springer
Set-valued optimization is a vibrant and expanding branch of applied mathematics that
deals with optimization problems where the objective map and/or the constraint maps are …
deals with optimization problems where the objective map and/or the constraint maps are …
Robust solutions of optimization problems affected by uncertain probabilities
In this paper we focus on robust linear optimization problems with uncertainty regions
defined by φ-divergences (for example, chi-squared, Hellinger, Kullback–Leibler). We show …
defined by φ-divergences (for example, chi-squared, Hellinger, Kullback–Leibler). We show …
Closed-loop supply chain network design under disruption risks: A robust approach with real world application
In today's globalized and highly uncertain business environments, supply chains have
become more vulnerable to disruptions. This paper presents a stochastic robust optimization …
become more vulnerable to disruptions. This paper presents a stochastic robust optimization …
[ΑΝΑΦΟΡΑ][C] Robust Optimization
A Ben-Tal - Princeton University Press google schola, 2009 - books.google.com
Robust optimization is still a relatively new approach to optimization problems affected by
uncertainty, but it has already proved so useful in real applications that it is difficult to tackle …
uncertainty, but it has already proved so useful in real applications that it is difficult to tackle …
Robust convex optimization
We study convex optimization problems for which the data is not specified exactly and it is
only known to belong to a given uncertainty set U, yet the constraints must hold for all …
only known to belong to a given uncertainty set U, yet the constraints must hold for all …
Stochastic network models for logistics planning in disaster relief
Emergency logistics in disasters is fraught with planning and operational challenges, such
as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable …
as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable …