We present a novel Lie algebra based Iterative Reweighted Least Squares (IRLS) algorithm for robust 3D point cloud alignment. We reformulate the optimal update computation to a compact form which requires only one pass through the data. Although this reformulation does not alter the asymptotic computational complexity, it is well suited for contemporary hardware architectures, yielding significant practical speedups. In extensive experiments on challenging benchmark datasets with added correspondence corruption, the method is consistently at least four times faster than previous literature whilst being mathematically equivalent, demonstrating it is well suited for time-critical applications.

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 complex, non-linear patterns. First, we leverage an ensemble of Gaussian Processes with a specialized weighting scheme named Automatic State Matching, achieving a 39% reduction in prediction error for the thickness of the outgoing board compared to conventional industry methods, as corroborated by real-world data. Subsequently, the predictive strategy is utilized in a novel robust control strategy which exploits the properties of Gaussian Processes to prescribe settings for wood planers. An empirical evaluation on simulated data demonstrated the viability of our prescriptive method, resulting in an 83% reduction in deviation from a predetermined target dimension.

Predicting the time needed to charge an electric vehicle from X% to Y% is a difficult task due to the nonlinearity of the charging process and other external factors such as temperature and battery degradation. Using 28,000 real-life level 3 fast charging sessions from 15 different types of electric vehicles, we train models for this task. We compare learning models such as random forest, linear and seconddegree regressions, support vector regressions, and neural networks. The models take into consideration the external temperature, battery capacity, nominal capacity of the electric vehicle, number of charges made during the same day, maximum charging time allowed by the electric vehicle, target voltage, maximum voltage and maximum current asked by the electric vehicle. The models also take into consideration the vehicle type and the charging station type. We use a data augmentation technique (SMOTE) and hyperparameters optimization to enhance our model performances. The structure of the neural networks is optimized using Bayesian optimization. All models are trained and statistically compared in order to find the overall best model for all vehicle types. The overall best model is a neural network with a sub neural network pre-trained to predict the electric vehicle type.

Solving the problem is an important part of optimization. An equally important part is the analysis of the solution where several questions can arise. For a scheduling problem, is it possible to obtain a better solution by increasing the capacity of a resource? What happens to the objective value if we start a specific task earlier? Answering such questions is important to provide explanations and increase the acceptability of a solution. A lot of research has been done on sensitivity analysis, but few techniques can be applied to constraint programming. We present a new method for sensitivity analysis applied to constraint programming. It collects information, during the search, about the propagation of the CUMULATIVE constraint, the filtering of the variables, and the solution returned by the solver. Using machine learning algorithms, we predict if increasing/decreasing the capacity of the cumulative resource allows a better solution. We also predict the impact on the objective value of forcing a task to finish earlier. We experimentally validate our method with the RCPSP problem.

The problem of an individual who wants to plan a long route in an electric vehicle where charging decisions are needed can be modeled as an instance of the Fixed Route Electric Vehicle Charging Problem (FRVCP). We developed a mixed-integer programming model that optimally solves a new variant of the FRVCP, the FRVCP with nonlinear energy management (FRVCP-NLEM). It considers charging times as a nonlinear function and allows to decide at which speed to drive on each segment of the route while considering the non-linearity of energy consumption functions. The non-linearity of all functions has been solved using multiple linear approximations. The proposed model is tested using an electric vehicle trip planner called PlaniCharge that uses realistic energy consumption and charging functions that take into account external factors such as temperature and road topology. The model is tested under different road types such as urban or highway routes. The proposed model is able to optimally solve most test instances within seconds. Results show that varying the vehicle speed is an important factor to consider under low temperatures and for long-range routes as it can reduce total route duration. Some routes cannot be completed at maximum speed and require varying driving speed on segment to be able to reach the destination.

Tree species identification using bark images is a challenging problem that could prove useful for many forestry related tasks. However, while the recent progress in deep learning showed impressive results on standard vision problems, a lack of datasets prevented its use on tree bark species classification. In this work, we present, and make publicly available, a novel dataset called BarkNet 1.0 containing more than 23,000 high-resolution bark images from 23 different tree species over a wide range of tree diameters. With it, we demonstrate the feasibility of species recognition through bark images, using deep learning. More specifically, we obtain an accuracy of 93.88% on single crop, and an accuracy of 97.81% using a majority voting approach on all of the images of a tree. We also empirically demonstrate that, for a fixed number of images, it is better to maximize the number of tree individuals in the training database, thus directing future data collection efforts.

Mixed-Initiative Systems (MIS) are hybrid decision systems where collaboration is possible between humans and machines. However, current systems sometimes override user preferences when provided with new ones. We studied linear optimization problems, where the decision maker is specifying preferences for variable values through an iterative process. We proposed a goal programming framework to deal with hierarchies of preferences. Two reoptimization algorithms were evaluated: sequential simplex and lexicographic simplex. Compared with the sequential simplex, the lexicographic simplex algorithm demonstrated greater speed and numerical stability.

Combinatorial optimization problems are often very difficult to solve and the choice of a search strategy has a tremendous influence over the solver's performance. A search strategy is said to be adaptive when it dynamically adapts to the structure of the problem instance and identifies the areas of the search space that contain good solutions. We introduce an algorithm (RLBS) that learns to efficiently backtrack when searching non-binary trees. Branching can be carried on using any usual variable/value selection strategy. However, when backtracking is needed, the selection of the node to target involves reinforcement learning. As the trees are non-binary, we have the opportunity to backtrack many times to each node during the search, which allows learning which nodes generally lead to the best rewards (that is, to the most interesting leaves). RLBS is evaluated for a scheduling problem using real industrial data. It outperforms classic (nonadaptive) backtracking strategies (DFS, LDS) as well as an adaptive branching strategy (IBS).

Mixed-Initiative-Systems (MIS) are hybrid decision-making systems in which human and machine collaborate in order to produce a solution. This paper described an MIS system adapted to business optimization problems. These problems can be solved in less than an hour as they show a linear structure. However, this delay is unacceptable for iterative and interactive decision-making contexts where users need to provide their input. Therefore, we propose a system providing the decision-makers with a convex hull of optimal solutions minimizing/maximizing the variables of interest. The users can interactively modify the value of a variable and the system is able to recompute a new optimal solution in a few milliseconds. Four real-time reoptimization methods are described and evaluated.

This work describes a framework for the elaboration and evaluation of management policies for production and transportation supply chains in the forest product industry. The approach deals with the issue of coordination between the tactical and operational decision levels. First, we introduce LogiLab, a software system allowing to model the network and to optimize product flows in the supply chain. We than show how one can use this tactical aggregated plan to identify management policies that will guide day to day operations at the operational level. Finally, a discrete event simulation model allows assessing with more details what would be the impact of implementing these policies at the operational/execution level.

A tree search strategy is said to be adaptive when it dynamically identifies which areas of the tree are likely to contain good solutions, using information that is gathered during the search process. This study shows how an adaptive approach can be used to enhance the efficiency of the coordination process of an industrial supply chain. The result is a new adaptive method (called the adaptive discrepancy search), intended for search in nonbinary trees, and that is exploitable in a distributed optimization context. For the industrial case studied (a supply chain in the forest products industry), this allowed reducing nearly half the time needed to obtain the best solution in comparison with a standard nonadaptive method. The method has also been evaluated for use with synthesized problems in order to validate the results that are obtained and to illustrate different properties of the algorithm.

Distributed constraint optimization is increasingly used for problem solving by multiple agents. However, there are situations where the system is made up of heterogeneous agents, for which the context, the structure, and the business rules define the interactions that are possible between them. As an example, supply chains are made up of interdependent business units having some form of customer-supplier hierarchical relationships. The coordination space for these hierarchical situations can be described as a tree. Therefore, we propose a distributed algorithm (MacDS) that performs discrepancy-based search which is known to perform well for centralized problems. The proposed algorithm is complete and aims at producing good solutions in a short amount of time. It allows concurrent computation and is tolerant to message delays. It has been evaluated using real industrial supply chain problems, for which it showed good performance.