Algorithmes pour la prise de décision distribuée en contexte hiérarchique

RÉSUMÉ Cette thèse a pour objet la coordination entre entités autonomes. De manière plus précise, nous nous intéressons à la coordination dans un contexte hiérarchique. Les problèmes étudiés montrent les caractéristiques suivantes : (1) il s’agit de problèmes d’optimisation distribués, (2) le problème est naturellement décomposé en sousproblèmes, (3) il existe a priori une séquence selon laquelle les sous-problèmes doivent être résolus, (4) les sous-problèmes sont sous la responsabilité de différentes entités et (5) chaque sous-problème est défini en fonction des solutions retenues pour les sousproblèmes précédents. Parmi les principaux domaines d’application, on trouve les systèmes d’aide à la décision organisationnels et les problèmes de synchronisation dans les chaînes logistiques industrielles. Ce dernier domaine sert de fil conducteur dans cette thèse : le travail de plusieurs unités de production est nécessaire pour fabriquer et livrer les commandes des clients. Différentes alternatives sont possibles en ce qui a trait aux pièces à utiliser, au choix des processus de fabrication, à l’ordonnancement des opérations et au transport. Chaque partenaire désire établir son plan de production (quoi faire, où et quand le faire), mais il est nécessaire pour eux de coordonner leurs activités. Les méthodes utilisées en pratique industrielle peuvent être qualifiées d’heuristiques de coordination. À l’opposé, il existe des algorithmes d’optimisation distribués et exacts, notamment les techniques de raisonnement sur contraintes distribuées (Distributed Constraint Optimization Problems, ou DCOP). Cependant, ces derniers algorithmes s’accommodent mal de la nature hiérarchique des problèmes étudiés et pourraient difficilement être utilisés en pratique. Les forces et les faiblesses des méthodes heuristiques et exactes nous ont donc amené à proposer de nouvelles approches.---------- ABSTRACT This thesis concerns multiagent coordination in hierarchical settings. These are distributed optimization problems showing the following characteristics: (1) the global problem is naturally decomposed into subproblems, (2) a sequence, defined a priori, exists in which the subproblems must be solved, (3) various agents are responsible for the subproblems, and (4) each subproblem is defined according to the solutions adopted for the preceding subproblems. Organizational distributed decision making and Supply chain coordination are among the main application domains. The latter case is more thoroughly studied in this thesis. In this kind of problem, the cooperation of several facilities is needed to produce and deliver the products ordered by external customers. However, different alternatives are possible regarding the parts to use, the manufacturing processes to follow, the scheduling of operations and the choice of transportation. Therefore, supply chain partners must coordinate their local decisions (e.g. what to do, where and when), with the common objective of delivering the ordered products with the least possible delay. The most commonly used coordination mechanisms can be described as heuristics. In contrast, some generic and complete distributed algorithms exist – researchers in Distributed Artificial Intelligence (DAI) have proposed a generic framework called Distributed Constraint Optimization Problem (DCOP). However, there are certain difficulties in mapping the actual business context (which is highly hierarchical) into the DCOP framework. Thus, based on the strengths and weaknesses of both the complete and heuristic approaches, we propose new approaches

Quality of sawmilling output predictions according to the size of the lot - The size matters!

Lors de l'évaluation de modèles d'apprentissage automatique supervisé, on considère généralement le rendement de prédiction moyen obtenu sur les tests individuels comme mesure de choix. Toutefois, lorsque le modèle est destiné à prédire quels produits du bois seront obtenus lors du sciage de certains billots, c'est généralement la performance pour un lot complet qui importe. Dans cet article, nous montrons l'impact de cette nuance en termes d'évaluation du modèle. En fait, la qualité d'une prédiction (globale) s'améliore considérablement lorsque l'on augmente la taille des lots, ce qui offre un solide soutien à l'utilisation de ces modèles en pratique.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 this by evaluating a model performance for various batch sizes. The quality of a (global) prediction improves tremendously when batch size increases, which offers a strong support for the use of such models in practice

Supply chain coordination using an adaptive distributed search strategy

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 algorith

Integrating revenue management and sales and operations planning in a Make-To-Stock environment : softwood lumber case study

Most research regarding revenue management in manufacturing has considered only a short-term planning horizon, assuming supply and production data exogenously given. Motivated by the case of the Canadian softwood lumber industry, this paper offers additionally a medium-term visibility for firms with limited capacity and faced with seasonal markets. We propose a demand management process for Make-To-Stock environments, integrating sales and operations planning (S&OP) and order promising based on revenue management concepts. Given heterogeneous customers, divergent product structure and multiple sourcing locations in a multi-period context, we first define a multi-level decision framework in order to support medium-term, short-term and real-time sales decisions in a way to maximize profits and to enhance the service level offered to high-priority customers. We further propose a mathematical formulation integrating an S&OP network model in the Canadian softwood lumber industry and an order promising model using nested booking limits. This new formulation allows reviewing previous order promising decisions while respecting sales commitments. A rolling horizon simulation is used to evaluate the performance of the proposed process in various demand scenarios and provides evidence that better performances can be achieved compared to common demand management practices by integrating S&OP and revenue management concepts

Kriging analysis of an integrated demand management process in softwood industry

Objective: This paper aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at time. Yet, we can get additional insights by employing design of experiments (DOE). Methodology: For making promises, we compare a First-Come First-Served approach to an approach using nested booking limits and giving advantage to profitable customers and attractive periods. Considering various sequences of order arrival, we generate Kriging metamodels that best describe the nonlinear relationships between the simulation responses and system factors for Canadian softwood lumber firms. We employ a Latin hypercube design to take into account different environmental scenarios. Results: Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling less-priority orders and if we use nested booking limits concept

Toward digital twins for sawmill production planning and control : benefits, opportunities and challenges

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 severalfactors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied

Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling

Objective: This research aims to develop a basic understanding of a demand management process inte- grating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies with limited capacity. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at a time. Yet, additional insights can be obtained by employing a space- filling design and Kriging metamodeling for analysis. Methodology: We compare two configurations of the integrated demand management process. While the First-Come First-Served concept is used at the order promising level for the first configuration, the sec- ond configuration uses nested booking limits and gives advantage to profitable customers and attractive periods. Considering various order arrival sequences, we generate Kriging metamodels that best describe the nonlinear relationships between four environmental factors (demand intensity, demand forecast er- ror, customer heterogeneity and coefficient of variation) and three performance measures (yearly profit margin, yearly sales and high-priority fill rate) for Canadian softwood lumber firms. Since our simulation experiments are time-consuming, we employ a Latin hypercube design to efficiently take into account different market situations. Results: Our analysis reveals the potential to improve the performance of the demand management pro- cess if we know high-priority customers needs before fulfilling low-priority orders and if we use nested booking limits concept

ADS : an adaptive search strategy for efficient distributed decision making

This paper concerns distributed decision-making in hierarchical settings. For this class of problems, the coordination space can be naturally modeled as a tree. A collective of agents can thus perform a distributed tree search in order to coordinate. Previous results have shown that search strategies based on discrepancies (e.g. LDS) can be adapted to a distributed context. They are more effective than chronological backtracking in such setting. In this paper we introduce ADS, an adaptive backtracking strategy based on the analysis of discrepancies. It enables the agents to collectively and dynamically learn which areas of the tree are most promising in order to visit them first. We evaluated the method using a real coordination problem in an industrial supply chain. This makes it possible for the team of agents to obtain high-quality solutions much more quickly than with previous methods