The complexity of decision support systems can take many forms. Some optimization problems involve millions of variables and constraints. Others are made up of highly nonlinear functions, obtained by simulations requiring a long computation time. The complexity can also be due to the large number of agents who act without coordination on the system. This line of research aims to design a range of algorithms adapted to the characteristics of these systems, to apply them to real problems, and to analyze their convergence.
Cahiers du GERAD
Machine-learning-based arc selection for constrained shortest path problems in column generation
Column generation is an iterative method used to solve a variety of optimization problems. It decomposes the problem into two parts: a master problem, and on...BibTeX reference
Faster delivery, lower shipping costs, and a higher chance of product availability, are some of the benefits offered by an omnichannel business model. Assumi...BibTeX reference
Ruslan Goyenko – Associate Professor, Finance, Desautels Faculty of Management, McGill University
Sırma Zeynep Alparslan Gök – Suleyman Demirel University
G. Selin Savaşkan – Çanakkale Onsekiz Mart University