Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

Datacenter management for on-site intermittent and uncertain renewable energy sources

Les technologies de l'information et de la communication sont devenues, au cours des dernières années, un pôle majeur de consommation énergétique avec les conséquences environnementales associées. Dans le même temps, l'émergence du Cloud computing et des grandes plateformes en ligne a causé une augmentation en taille et en nombre des centres de données. Pour réduire leur impact écologique, alimenter ces centres avec des sources d'énergies renouvelables (EnR) apparaît comme une piste de solution. Cependant, certaines EnR telles que les énergies solaires et éoliennes sont liées aux conditions météorologiques, et sont par conséquent intermittentes et incertaines. L'utilisation de batteries ou d'autres dispositifs de stockage est souvent envisagée pour compenser ces variabilités de production. De par leur coût important, économique comme écologique, ainsi que les pertes énergétiques engendrées, l'utilisation de ces dispositifs sans intégration supplémentaire est insuffisante. La consommation électrique d'un centre de données dépend principalement de l'utilisation des ressources de calcul et de communication, qui est déterminée par la charge de travail et les algorithmes d'ordonnancement utilisés. Pour utiliser les EnR efficacement tout en préservant la qualité de service du centre, une gestion coordonnée des ressources informatiques, des sources électriques et du stockage est nécessaire. Il existe une grande diversité de centres de données, ayant différents types de matériel, de charge de travail et d'utilisation. De la même manière, suivant les EnR, les technologies de stockage et les objectifs en termes économiques ou environnementaux, chaque infrastructure électrique est modélisée et gérée différemment des autres. Des travaux existants proposent des méthodes de gestion d'EnR pour des couples bien spécifiques de modèles électriques et informatiques. Cependant, les multiples combinaisons de ces deux parties rendent difficile l'extrapolation de ces approches et de leurs résultats à des infrastructures différentes. Cette thèse explore de nouvelles méthodes pour résoudre ce problème de coordination. Une première contribution reprend un problème d'ordonnancement de tâches en introduisant une abstraction des sources électriques. Un algorithme d'ordonnancement est proposé, prenant les préférences des sources en compte, tout en étant conçu pour être indépendant de leur nature et des objectifs de l'infrastructure électrique. Une seconde contribution étudie le problème de planification de l'énergie d'une manière totalement agnostique des infrastructures considérées. Les ressources informatiques et la gestion de la charge de travail sont encapsulées dans une boîte noire implémentant un ordonnancement sous contrainte de puissance. La même chose s'applique pour le système de gestion des EnR et du stockage, qui agit comme un algorithme d'optimisation d'engagement de sources pour répondre à une demande. Une optimisation coopérative et multiobjectif, basée sur un algorithme évolutionnaire, utilise ces deux boîtes noires afin de trouver les meilleurs compromis entre les objectifs électriques et informatiques. Enfin, une troisième contribution vise les incertitudes de production des EnR pour une infrastructure plus spécifique. En utilisant une formulation en processus de décision markovien (MDP), la structure du problème de décision sous-jacent est étudiée. Pour plusieurs variantes du problème, des méthodes sont proposées afin de trouver les politiques optimales ou des approximations de celles-ci avec une complexité raisonnable.In recent years, information and communication technologies (ICT) became a major energy consumer, with the associated harmful ecological consequences. Indeed, the emergence of Cloud computing and massive Internet companies increased the importance and number of datacenters around the world. In order to mitigate economical and ecological cost, powering datacenters with renewable energy sources (RES) began to appear as a sustainable solution. Some of the commonly used RES, such as solar and wind energies, directly depends on weather conditions. Hence they are both intermittent and partly uncertain. Batteries or other energy storage devices (ESD) are often considered to relieve these issues, but they result in additional energy losses and are too costly to be used alone without more integration. The power consumption of a datacenter is closely tied to the computing resource usage, which in turn depends on its workload and on the algorithms that schedule it. To use RES as efficiently as possible while preserving the quality of service of a datacenter, a coordinated management of computing resources, electrical sources and storage is required. A wide variety of datacenters exists, each with different hardware, workload and purpose. Similarly, each electrical infrastructure is modeled and managed uniquely, depending on the kind of RES used, ESD technologies and operating objectives (cost or environmental impact). Some existing works successfully address this problem by considering a specific couple of electrical and computing models. However, because of this combined diversity, the existing approaches cannot be extrapolated to other infrastructures. This thesis explores novel ways to deal with this coordination problem. A first contribution revisits batch tasks scheduling problem by introducing an abstraction of the power sources. A scheduling algorithm is proposed, taking preferences of electrical sources into account, though designed to be independent from the type of sources and from the goal of the electrical infrastructure (cost, environmental impact, or a mix of both). A second contribution addresses the joint power planning coordination problem in a totally infrastructure-agnostic way. The datacenter computing resources and workload management is considered as a black-box implementing a scheduling under variable power constraint algorithm. The same goes for the electrical sources and storage management system, which acts as a source commitment optimization algorithm. A cooperative multiobjective power planning optimization, based on a multi-objective evolutionary algorithm (MOEA), dialogues with the two black-boxes to find the best trade-offs between electrical and computing internal objectives. Finally, a third contribution focuses on RES production uncertainties in a more specific infrastructure. Based on a Markov Decision Process (MDP) formulation, the structure of the underlying decision problem is studied. For several variants of the problem, tractable methods are proposed to find optimal policies or a bounded approximation

Dynamic Control for Batch Process Systems Using Stochastic Utility Evaluation

Most research studies in the batch process control problem are focused on optimizing system performance. The methods address the problem by minimizing single criterion such as cycle time and tardiness, or bi-criteria such as cycle time and tardiness, and earliness and tardiness. This research demonstrates the use of Stochastic Utility Evaluation (SUE) function approach to optimize system performance using multiple criteria. In long production cycles, the earliness and tardiness weight (utility) of products vary depending on the time. As the time approaches the due-date, it affects contractual penalties, loss of customer goodwill and the storage period for the completed products. It is necessary to reflect the weight of products for earliness and tardiness at decision epochs to decide on the optimal strategy. This research explores how stochastic utility function using stochastic information can be derived and used to strategically improve existing approaches for the batch process control problem. This research first explores how SUE function can be applied to existing model for bi-objective problem such as cycle time and tardiness. Benchmark strategies using SUE function (NACH-SUE, MBS-SUE, No idle and full batch) are compared to each other. The experimental results show that NACH-SUE effectively improves mean cycle time and tardiness performance respectively than other benchmark strategies. Next, SUE function for earliness and tardiness is used in an existing model to develop a tri-objective problem. Typically, this problem is very complex to solve due to its trade-off relationship. However SUE function makes it relatively easy to solve the tri-objective problem since SUE function can be incorporated in an existing model. It is observed that SUE function can be effectively used for solving a tri-objective problem. Performance improvement for averaged value of cycle time, earliness and tardiness is observed under a comprehensive set of experimental conditions