Machine learning for combinatorial optimization: a methodological tour d'horizon
Y Bengio, A Lodi, A Prouvost - European Journal of Operational Research, 2021 - Elsevier
This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …
research communities, at leveraging machine learning to solve combinatorial optimization …
A survey of network interdiction models and algorithms
This paper discusses the development of interdiction optimization models and algorithms,
with an emphasis on mathematical programming techniques and future research challenges …
with an emphasis on mathematical programming techniques and future research challenges …
Industry 4.0: smart scheduling
Smart Manufacturing and Industry 4.0 production environments integrate the physical and
decisional aspects of manufacturing processes into autonomous and decentralised systems …
decisional aspects of manufacturing processes into autonomous and decentralised systems …
[ספר][B] Partially observed Markov decision processes
V Krishnamurthy - 2016 - books.google.com
Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …
applications in controlled sensing (including social learning, adaptive radars and sequential …
Data analytics in operations management: A review
Research in operations management has traditionally focused on models for understanding,
mostly at a strategic level, how firms should operate. Spurred by the growing availability of …
mostly at a strategic level, how firms should operate. Spurred by the growing availability of …
Inverse optimization: Theory and applications
Inverse optimization describes a process that is the “reverse” of traditional mathematical
optimization. Unlike traditional optimization, which seeks to compute optimal decisions given …
optimization. Unlike traditional optimization, which seeks to compute optimal decisions given …
[ספר][B] Assignment problems: revised reprint
R Burkard, M Dell'Amico, S Martello - 2012 - SIAM
When SIAM asked us to prepare a new edition of this book after less than three years from
publication, we expected a light duty. Just the correction of some typos and imprecisions …
publication, we expected a light duty. Just the correction of some typos and imprecisions …
Max-margin Markov networks
In typical classification tasks, we seek a function which assigns a label to a single object.
Kernel-based approaches, such as support vector machines (SVMs), which maximize the …
Kernel-based approaches, such as support vector machines (SVMs), which maximize the …
Bilevel optimization: theory, algorithms, applications and a bibliography
Bilevel optimization problems are hierarchical optimization problems where the feasible
region of the so-called upper level problem is restricted by the graph of the solution set …
region of the so-called upper level problem is restricted by the graph of the solution set …
Inverse optimization
In this paper, we study inverse optimization problems defined as follows. Let S denote the
set of feasible solutions of an optimization problem P, let c be a specified cost vector, and x 0 …
set of feasible solutions of an optimization problem P, let c be a specified cost vector, and x 0 …