Michael Morin

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Université Laval | ULAVAL · Department of Operations and Decision Systems

We develop optimization approaches to the graph-clear problem, a pursuit-evasion problem where mobile robots must clear a facility of intruders. The objective is to minimize the number of robots required. We contribute new formal results on progressive and contiguous assumptions and their impact on algorithm completeness. We present mixed-integer l...

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,...

We present a metamodeling approach, based on supervised learning, to estimate the probability of success of maritime search and rescue operations. The objective is to improve search planning in a context where lives are at risk and time is of the essence. The proposed approach has been evaluated both in terms of its predictive performance (Can the...

Continuous high-frequency wood drying, when integrated with a traditional wood finishing line, allows correcting moisture content one piece of lumber at a time in order to improve its value. However, the integration of this precision drying process complicates sawmills logistics. The high stochasticity of lumber properties and less than ideal lumbe...

We present a novel artificial intelligence approach that encompasses both predictive and prescriptive aspects for the challenging task of model-based control of industrial wood planers. These sophisticated lumber finishing machines are known for the complexity of their operation, and the available data pertaining to the planing process exhibits com...

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be...

In today's dynamic markets, decision-making relies heavily on simulation models to evaluate different production control methods. Although price-driven production control methods have proven their effectiveness in exploiting price volatility, certain industries are still reluctant to adopt these methods in their operational decision-making. This re...

Robotized welding processes in the manufacturing industry play a crucial role in enhancing competitiveness through automation, adaptability, and increased productivity. To optimize welding parameters, modeling approaches have gained significance, enabling users to simulate welding experiments and determine appropriate settings. With the growing nee...

We propose and compare different data-driven personalized approaches to predict future blood glucose levels in type-1 diabetes patients, living and exercising in real-world conditions. Multiple machine learning (XGBoost, Random Forest) and deep learning (LSTM, CNN-LSTM, Dual-encoder with attention layer) regression models are considered. Each deep-...

A sawmilling process scans a wood log and must establish a series of cutting and rotating operations to perform in order to obtain the set of lumbers having the most value. The search space can be expressed as an and/or tree. Providing an optimal solution, however, may take too much time. The complete search for all possibilities can take several m...

Planning an efficient maritime search and rescue operation (MSAR) requires estimating the quality of search plans. Nowadays, decision support systems (DSS) for MSAR planning use Monte Carlo drift simulation and search simulators. Carrying multiple simulations is computationally intensive. We present supervised learning-based metamodels to reduce th...

In this paper, we present a mathematical formulation for the dynamic scheduling of an industrial welding robot. The studied robot executes various processes concurrently, using several loading and welding platforms as well as a mechanical arm to move the pieces. The robot is subject to capacity and setup constraints (time needed to move the arm). W...

In this paper, we present a mathematical formulation for the dynamic scheduling of an industrial welding robot. The studied robot executes various processes concurrently, using several loading and welding platforms as well as a mechanical arm to move the pieces. The robot is subject to capacity and setup constraints (time needed to move the arm). W...

Predicting the lumber products that can be obtained from a log allows for better allocation of resources and improves operations planning. Although sawing simulators make it possible to anticipate the production associated with a log, they do not allow processing many logs quickly. It was shown that machine learning can be used in place of a simula...

In gas metal arc welding, a weld quality and performance depends on many parameters. Selecting the right ones can be complex, even for an expert. One generally proceeds through trial and error to find a good set of parameters. Therefore, the current experts’ method is not optimized and can require a lot of time and materials. We propose using super...

Supplementary Material for Paper Entitled "Ant Colony Optimization for Path Planning in Search and Rescue Operations"

Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to several factors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced i...

Synthetic data generation of industrial processes exhibiting non-stationarity and complex, non-linear dependencies between their inputs and outputs is a challenging task. We argue that vine copula models are particularly well suited for this problem and present a method combining limited available data and expert knowledge in order to generate synt...

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...

Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to several factors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced i...

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...

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...

We tackle the problem of predicting the lumber products resulting from the break down of the logs at a given sawmill. Although previous studies have shown that supervised learning is well suited for that prediction problem, to our knowledge, there exists only one approach using the 3D log scans as inputs and it is based on the iterative closest-poi...

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.

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...

The forest-products supply chain gives rise to a variety of interconnected problems. Addressing these problems is challenging, but could be simplified by rigorous data analysis through a machine learning approach. A large amount of data links these problems at various hierarchical levels (e.g., strategic, tactical, operational, online) which compli...

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...

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...

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...

Decision support systems (DSS), in particular optimization-based DSS, are now a common tool in engineering, business, and management. Although optimization-based DSS provide theoretically and/or empirically well-founded solutions to a given practical problem, a user that does not fully comprehend the system’s recommendation might simply choose to i...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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’...

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...