Michael Morin

Michael Morin
Laval University | ULAVAL · Department of Operations and Decision Systems

Ph.D.

About

33
Publications
2,754
Reads
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129
Citations
Introduction
I’m an assistant professor at the Department of Operations and Decision Systems of Université Laval. My research interests include: optimization and operational research, metasimulation and machine learning, and humanitarian, industrial and business applications.
Additional affiliations
January 2018 - present
Laval University
Position
  • Professor (Assistant)
January 2017 - December 2017
University of Toronto
Position
  • PostDoc Position
Description
  • Postdoctoral fellow at the Toronto Intelligent Decision Engineering Laboratory (TIDEL)
November 2015 - December 2016
Université Laval
Position
  • Postdoctoral fellow in optimization, machine learning and operational research
Description
  • Collaborative postdoctoral fellowship between the FORAC research consortium and the Centre de recherche en modélisation, information et décision (CERMID) of Universté Laval
Education
September 2010 - December 2015
Laval University
Field of study
  • Computer Science
January 2008 - August 2010
Laval University
Field of study
  • Computer Science
September 2005 - December 2007
Laval University
Field of study
  • Computer Science

Publications

Publications (33)
Conference Paper
Wood planers are high speed sophisticated lumber finishing machines that are difficult to operate and for which the available data shows complex, non-linear patterns. We present a machine learning approach to build a control loop for an industrial wood planer. In order to predict the thickness of the outgoing boards with better accuracy than the in...
Article
In search and rescue operations, an efficient search path, colloquially understood as a path maximizing the probability of finding survivors, is more than a path planning problem. Maximizing the objective adequately, i.e., quickly enough and with sufficient realism, can have substantial positive impact in terms of human lives saved. In this paper,...
Conference Paper
Full-text available
The Canadian wood industry use sawing simulators to digitally break a log into a basket of lumbers. However, those simulators tend to be computationally intensive. In some cases, this renders them impractical as decision support tools. Such a use case is the problem of dispatching large volume of wood to several sawmills in order to maximise total...
Presentation
Full-text available
Cette recherche explore l'application de deux méthodes d'analyse (Indice des risques de corruption et la détection d'anomalies), afin de détecter des contrats soupçonnés de corruption à partir des données massives extraites de la plateforme gouvernementale SEAO.
Article
The Canadian wood industry use sawing simulators to digitally break a log into a basket of lumbers. However, those simulators tend to be computationally intensive. In some cases, this renders them impractical as decision support tools. Such a use case is the problem of dispatching large volume of wood to several sawmills in order to maximise total...
Conference Paper
Full-text available
When comparing supervised learning models, one generally considers the average prediction performance obtained over individual test samples. However, when using machine learning to predict which lumber products will be obtained when sawing logs, it is usually the performance over the entire lot that matters. In this paper, we show the impact of thi...
Chapter
Full-text available
In the Canadian’s lumber industry, simulators are used to predict the lumbers resulting from the sawing of a log at a given sawmill. Giving a log or several logs’ 3D scans as input, simulators perform a real-time job to predict the lumbers. These simulators, however, tend to be slow at processing large volume of wood. We thus explore an alternative...
Article
Full-text available
In the Canadian's lumber industry, simulators are used to predict the lumbers resulting from the sawing of a log at a given sawmill. Giving a log or several logs' 3D scans as input, simulators perform a real-time job to predict the lumbers. These simulators, however, tend to be slow at processing large volume of wood. We thus explore an alternative...
Article
Full-text available
In this paper, we present work conducted in order to explain the results of a commercial software used for real-time decision support for the flow management of a combined wastewater network. This tool is deployed in many major cities and is used on a daily basis. We apply decision trees to build rules for classifying and interpreting the solutions...
Conference Paper
In this paper, we present work conducted in order to explain the results of a commercial software used for real-time decision support for the flow management of a combined wastewater network. This tool is deployed in many major cities and is used on a daily basis. We apply decision trees to build rules for classifying and interpreting the solutions...
Conference Paper
We use machine learning to generate metamodels for sawing simulation. Simulation is widely used in the wood industry for decision making. These simulators are particular since their response for a given input is a structured object, i.e., a basket of lumbers. We demonstrate how we use simple machine learning algorithms (e.g., a tree) to obtain a go...
Thesis
Full-text available
We tackle two different and complementary problems: the Coverage Path Planning (CPP) and the Optimal Search Path (OSP). The CPP is a main challenge in mobile robotics. The OSP is a classic from search theory. We first present a review of both problems that highlights their differences and their similarities from the point of view of search (coverag...
Poster
Full-text available
In search theory, the goal of the Optimal Search Path (OSP) problem is to find a finite length path maximizing the probability that a searcher detects a lost wanderer on a graph. We propose to bound the probability of finding the wanderer in the remaining search time by relaxing the problem into a stochastic game of cop and robber from graph theory...
Conference Paper
Full-text available
In search theory, the goal of the Optimal Search Path (OSP) problem is to find a finite length path maximizing the probability that a searcher detects a lost wanderer on a graph. We propose to bound the probability of finding the wanderer in the remaining search time by relaxing the problem into a stochastic game of cop and robber from graph theory...
Presentation
Full-text available
In search theory, the goal of the Optimal Search Path (OSP) problem is to find a finite length path maximizing the probability that a searcher detects a lost wanderer on a graph. We propose to bound the probability of finding the wanderer in the remaining search time by relaxing the problem into a stochastic game of cop and robber from graph theory...
Conference Paper
Full-text available
In the Optimal Search Path problem from search theory, the objective is to find a finite length searcher's path that maximizes the probability of detecting a lost wanderer on a graph. We introduce a novel bound on the probability of finding the wanderer in the remaining search time and discuss how this bound is derived from a relaxation of the prob...
Conference Paper
Full-text available
We introduce a novel global Markov transition constraint (Mtc) to model finite state homogeneous Markov chains. We present two algorithms to filter the variable domains representing the imprecise probability distributions over the state space of the chain. The first filter-ing algorithm is based on the fractional knapsack problem and the second fil...
Conference Paper
Full-text available
We are interested in the coverage path planning problem with imperfect sensors, within the context of robotics for mine countermeasures. In the studied problem, an autonomous underwater vehicle (AUV) equipped with sonar surveys the bottom of the ocean searching for mines. We use a cellular decomposition to represent the ocean floor by a grid of uni...
Conference Paper
Full-text available
We present a methodology to construct optimal visibility graphs from vector and raster terrain data based on the integration of Geographic Information Systems, computational geometry, and integer linear programming. In an emergency situation, the ability to observe an environment, completely or partially, is crucial when searching an area for survi...
Chapter
Full-text available
The optimal search path (OSP) problem is a single-sided detection search problem where the location and the detectability of a moving object are uncertain. A solution to this NP-hard problem is a path on a graph that maximizes the probability of finding an object that moves according to a known motion model. We developed constraint programming mode...
Conference Paper
Full-text available
Search and Rescue operations involve the efficient allocation of available resources in order to locate a lost search object caught in a critical situation (e.g., the survivors of an aeronautical incident). In this paper, we describe our work in progress for developing a multi-criteria inland search operations planning method. The method uses geogr...
Conference Paper
Full-text available
The long-term objective of our project is to develop a knowledge-based tool for Search and Rescue (SAR) operations to support a Canadian search mission coordinator in determining the likely location of a missing aircraft overland. In order to attain this objective, we used a knowledge engineering approach to acquire, structure and model SAR experts...
Conference Paper
Full-text available
In the first part of this paper, we present the Optimal Searcher Path problem with Visibility, a novel path planning approach that models inter-region visibility and that uses concepts from search theory to model uncertainty on the goal’s (i.e., the search object) detectability and location. In the second part, we introduce the Ant Search algorithm...
Thesis
Full-text available
How can search theory and path planning concepts be used to formulate and to solve detection search problems in the context of a ground SAR operation while taking into account practical aspects such as terrain visibility constraints? As an answer to this research question, we have formulated a novel detection search problem to include the searcher’...
Conference Paper
Full-text available
In this paper, the problem of path planning for a ground search unit looking for an object of unknown location is considered. As in the classical optimal searcher path problem, the probability of finding the search object is the main criterion of optimality and the search unit is constrained by the environment topology that influences its choices f...

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Projects

Projects (2)
Project
This project is about development of a software solution for real-time control of sewer networks. This solution uses rainfall forecasts, current state of the network, and solves a mixed integer linear programming model to find the optimal set points to be sent to gates and pumps.