Sheet-Metal Production Scheduling Using AlphaGo Zero

This work investigates the applicability of a reinforcement learning (RL) approach, specifically AlphaGo Zero (AZ), for optimizing sheet-metal (SM) production schedules with respect to tardiness and material waste. SM production scheduling is a complex job shop scheduling problem (JSSP) with dynamic operation times, routing flexibility and supplementary constraints. SM production systems are capable of processing a large number of highly heterogeneous jobs simultaneously. While very large relative to the JSSP literature, the SM-JSSP instances investigated in this work are small relative to the SM production reality. Given the high dimensionality of the SM-JSSP, computation of an optimal schedule is not tractable. Simple heuristic solutions often deliver bad results. We use AZ to selectively search the solution space. To this end, a single player AZ version is pretrained using supervised learning on schedules generated by a heuristic, fine-tuned using RL and evaluated through comparison with a heuristic baseline and Monte Carlo Tree Search. It will be shown that AZ outperforms the other approaches. The work’s scientific contribution is twofold: On the one hand, a novel scheduling problem is formalized such that it can be tackled using RL approaches. On the other hand, it is proved that AZ can be successfully modified to provide a solution for the problem at hand, whereby a new line of research into real-world applications of AZ is opened

Learning dispatching rules via an association rule mining approach

This thesis proposes a new idea using association rule mining-based approach for discovering dispatching rules in production data. Decision trees have previously been used for the same purpose of finding dispatching rules. However, the nature of the decision tree as a classification method may cause incomplete discovery of dispatching rules, which can be complemented by association rule mining approach. Thus, the hidden dispatching rules can be detected in the use of association rule mining method. Numerical examples of scheduling problems are presented to illustrate all of our results. In those examples, the schedule data of single machine system is analyzed by decision tree and association rule mining, and findings of two learning methods are compared as well. Furthermore, association rule mining technique is applied to generate dispatching principles in a 6 x 6 job shop scheduling problem. This means our idea can be applicable to not only single machine systems, but also other ranges of scheduling problems with multiple machines. The insight gained provides the knowledge that can be used to make a scheduling decision in the future

Réservation de capacité de production dans le domaine du traitement de surface pour l’aéronautique

RÉSUMÉ : Les entreprises de traitement de surface dans le secteur aéronautique font face à de nombreux problèmes concernant leur charge de travail, les délais accordés par leur client et leur réactivité. Deux des principales causes sont la variabilité et la très grande diversité de la demande. Afin de mieux planifier l’utilisation de leur ressource, les manufacturiers du secteur ont besoin de construire des relations basées sur une plus grande collaboration avec leur clientèle. Alors que de très nombreuses pratiques collaboratives ont été proposées dans la littérature, la plupart d’entre elles sont centrées sur la production sur stock dans le secteur de la distribution et ne peuvent pas être directement applicables dans notre cas d’étude, à savoir une production sur commande. Aussi, la gestion des priorités est un sujet largement traité par la littérature et il existe des règles d’ordonnancement très performantes et adaptées à des situations bien identifiées. Notre cas d’étude ne permet pas non plus l’application directe de ces règles, notamment en raison de la grande diversité des produits impliqués. L’objet de cette recherche est d’évaluer les impacts et la faisabilité d’une approche collaborative innovante basée sur une réservation de la capacité de la production et une gestion des priorités qui s’y associe de manière pertinente. Une modélisation du processus actuellement utilisé est d’abord réalisée et sert de base à la modélisation du nouveau processus. Le processus proposé s’accompagne, d’une part, des termes d’un contrat de réservation de la capacité de la production du manufacturier qui stipule les engagements du client en termes d’échange d’information sur sa demande future, et d’autre part, des engagements du manufacturier au niveau de la prise en charge des commandes issues de la réservation. La relation contractuelle se base notamment sur des niveaux de priorité des commandes associés à des niveaux de partage de la demande future des clients. La simulation à évènements discrets est utilisée pour confronter la solution proposée à différentes mises en situation, notamment des variations conséquentes de la demande qui engendrent une dégradation des performances de manière irrégulière et imprévisible. Ainsi, les résultats obtenus nous permettent de conclure quant à l’intérêt du processus proposé : on observe un lissage et une augmen-tation des performances globales. Aussi, les résultats montrent qu’une augmentation du nombre de clients engagés dans un contrat de réservation de la capacité est bénéfique pour les acteurs de la collaboration.----------ABSTRACT : Surface finishing manufacturers in the aeronautics industry face numerous problems related to their workload, deadlines, and reactivity. The main causes of these challenges are the variability and diversity of demand. Manufacturers need to improve plan production resource utilization to foster more collaborative relationships with their clients. Academic literature presents many collaborative practices, but most are dedicated to make-to-stock production and retail business; such practices cannot be used directly in relation to surface finishing manufacturers, i.e. make-to-order activities. There are many existing studies about priority management, and it is easy to find efficient and relevant scheduling rules, but few studies specifically concern surface finishing manufacturing. One possible reason for the gap in literature is the diversity of involved products. One of the goals of our study is to evaluate the feasibility and the impacts of an innovative manufacturing approach based on production capacity reservation and relevant priority rules. First, a model of the current process was tested. The study compares it to a new proposed model. The new proposed model involves production capacity reservation contracts, client commitments and surface finishing manufacturer commitments to contract-based orders processing. The proposed collaborative relationship is based on different order priority levels related to the way clients share their future demand or not. The proposed model is evaluated in different situations using discrete event simulation, with particular attention to high variability and uncertain demand, which worsens performance. The study concludes with the benefits of the proposed model: it smoothens performances over the observed horizon. Also, more client involvement in the collaboration results in greater benefits to all stakeholder

Evolutionary methods for the design of dispatching rules for complex and dynamic scheduling problems

Three methods, based on Evolutionary Algorithms (EAs), to support and automate the design of dispatching rules for complex and dynamic scheduling problems are proposed in this thesis. The first method employs an EA to search for problem instances on which a given dispatching rule performs badly. These instances can then be analysed to reveal weaknesses of the tested rule, thereby providing guidelines for the design of a better rule. The other two methods are hyper-heuristics, which employ an EA directly to generate effective dispatching rules. In particular, one hyper-heuristic is based on a specific type of EA, called Genetic Programming (GP), and generates a single rule from basic job and machine attributes, while the other generates a set of work centre-specific rules by selecting a (potentially) different rule for each work centre from a number of existing rules. Each of the three methods is applied to some complex and dynamic scheduling problem(s), and the resulting dispatching rules are tested against benchmark rules from the literature. In each case, the benchmark rules are shown to be outperformed by a rule (set) that results from the application of the respective method, which demonstrates the effectiveness of the proposed methods