ClouDiA: a deployment advisor for public clouds

An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and oper-ating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances non-contiguously, i.e., instances of a given application may end up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in signif-icant performance degradation, unless care is taken in how applica-tion components are mapped to instances. In this paper, we propose ClouDiA, a general deployment ad-visor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs mixed-integer programming and constraint programming techniques to ef-ficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applica-tions in Amazon EC2, we show that our techniques yield a 15 % to 55 % reduction in time-to-solution or service response time, without any need for modifying application code. 1

Simultaneous lotsizing and scheduling - extensions and solution approaches

The present thesis focuses on simultaneous lotsizing and scheduling. A comprehensive review of the literature is presented in which the historical development of the subject and the current research gaps are, based on a classification scheme, described. Additionally, a review focusing on so-called secondary resources (e.g., setup operators or raw materials), which are considered alongside the primary production resource, is provided. The insights on different types of secondary resources help to develop a new model formulation generalizing and extending the currently used approaches, which are specific to certain settings. Some illustrative examples demonstrate the functional principle and flexibility of this new formulation which can thus be used in a wide range of applications. Finally, a new heuristic to solve large-scaled simultaneous lotsizing and scheduling problems is presented. The heuristic creates a modified multi-line master problem by aggregating products into groups. The resulting problem is less complex and its solution can be used to define single-line sub problems. These sub problems are solved by heuristics present in the literature and the results are then combined to form a solution to the original problem. Numerical tests show the applicability of the aforementioned approach to solve problems of practical relevance.Die vorliegende Ausarbeitung betrachtet das Thema der simultanen Losgrößen- und Reihenfolgeplanung tiefergehend. Ein ausführlicher Literaturüberblick zeigt unter Zuhilfenahme eines Klassifizierungsschemas den Entwicklungsverlauf und aktuelle Forschungslücken in diesem Bereich auf. Weiterhin wird ein auf zusätzliche Ressourcen (sogenannte secondary resources) fokussierter Literaturüberblick erstellt. Diese Ressourcen (z.B. Personal zur Umrüstung oder Rohmaterial) werden zusätzlich zu der primären Produktionsressource benötigt. Die Erkenntnisse zu den verschiedenen Typen von zusätzlichen Ressourcen werden verwendet, um ein generelles Modell zu entwickeln, welches die bisherigen, auf bestimmte Anwendungsfälle spezialisierten, Formulierungen abbildet und erweitert. Testläufe mit Beispielszenarien demonstrieren die Funktionalität und die Flexibilität der neuen Modellformulierung welche für einen Vielzahl von Anwendungsfällen verwendet werden kann. Abschließend wird eine neue Heuristik zum Lösen von simultanen Losgrößen- und Reihenfolgeplanungsproblemen praxisrelevanter Größen vorgestellt. Innerhalb der Heuristik wird durch Produktaggregation ein modifiziertes Mehrlinien-Masterproblem generiert. Das resultierende Problem ist weniger komplex und die dafür gefundene Lösung kann zum Erstellen von Einlinien-Teilproblemen verwendet werden. Diese Teilprobleme werden mit aus der Literatur bekannten Heuristiken gelöst. Die Ergebnisse werden zu einer Lösung für das ursprüngliche Problem zusammengefasst. Numerische Tests belegen die Tauglichkeit des Verfahrens zum Lösen von praxisrelevanten Problemen

Meta-heuristic & hyper-heuristic scheduling tools for biopharmaceutical production

The manufacturing of biopharmaceuticals requires substantial investments and necessitates long-term planning. Complicating the task of determining optimal production plans are large portfolios of products and facilities which limit the tractability of exact solution methods, and uncertainties & stochastic events which often render plans obsolete when reality deviates from the expectation. This thesis therefore describes decisional tools that are able to cope with these complexities. First, a capacity planning problem for a network of facilities and multiple products was tackled. Inspired by meta-heuristic approaches to job shop scheduling, a tailored construction heuristic that builds a production plan based on a sequence — optimised by a genetic algorithm—of product demands was proposed. Comparisons to a mathematical programming model demonstrated its competitiveness on certain scenarios and its applicability to a multi-objective problem. Next, a custom object-oriented model was introduced for a manufacturing scheduling system that utilised a failure-prone perfusion-based bioprocess. With this, process design decisions such as cell culture run time and process configuration, and single-product facility scheduling strategies were evaluated whilst incorporating simulations of stochastic failure events and uncertain demand. This model was then incorporated into a larger hyper-heuristic to determine optimal scheduling policies for a multi-product problem. Various policy representations are tested and a few policies are adapted from the literature to fit this specific problem. In addition, a novel policy utilising a look-ahead heuristic is proposed. The benefit of parameter tuning using evolutionary algorithms is demonstrated and shows that tuned policies perform much better than a policy that estimates parameters based on service level considerations. In addition, the disadvantages of relying on a fixed or rigid production sequence policy in the face of uncertainty is highlighted

An agent-based visualisation system.

This thesis explores the concepts of visual supercomputing, where complex distributed systems are used toward interactive visualisation of large datasets. Such complex systems inherently trigger management and optimisation problems; in recent years the concepts of autonomic computing have arisen to address those issues. Distributed visualisation systems are a very challenging area to apply autonomic computing ideas as such systems are both latency and compute sensitive, while most autonomic computing implementations usually concentrate on one or the other but not both concurrently. A major contribution of this thesis is to provide a case study demonstrating the application of autonomic computing concepts to a computation intensive, real-time distributed visualisation system. The first part of the thesis proposes the realisation of a layered multi-agent system to enable autonomic visualisation. The implementation of a generic multi-agent system providing reflective features is described. This architecture is then used to create a flexible distributed graphic pipeline, oriented toward real-time visualisation of volume datasets. Performance evaluation of the pipeline is presented. The second part of the thesis explores the reflective nature of the system and presents high level architectures based on software agents, or visualisation strategies, that take advantage of the flexibility of the system to provide generic features. Autonomic capabilities are presented, with fault recovery and automatic resource configuration. Performance evaluation, simulation and prediction of the system are presented, exploring different use cases and optimisation scenarios. A performance exploration tool, Delphe, is described, which uses real-time data of the system to let users explore its performance

Analysis and Control of Batch Order Picking Processes Considering Picker Blocking

Order picking operations play a critical role in the order fulfillment process of distribution centers (DCs). Picking a batch of orders is often favored when customers’ demands create a large number of small orders, since the traditional single-order picking process results in low utilization of order pickers and significant operational costs. Specifically, batch picking improves order picking performance by consolidating multiple orders in a "batch" to reduce the number of trips and total travel distance required to retrieve the items. As more pickers are added to meet increased demand, order picking performance is likely to decline due to significant picker blocking. However, in batch picking, the process of assigning orders to particular batches allows additional flexibility to reduce picker blocking. This dissertation aims to identify, analyze, and control, or mitigate, picker blocking while batch picking in picker-to-part systems. We first develop a large-scale proximity-batching procedure that can enhance the solution quality of traditional batching models to near-optimality as measured by travel distance. Through simulation studies, picker blocking is quantified. The results illustrate: a) a complex relationship between picker blocking and batch formation; and b) a significant productivity loss due to picker blocking. Based on our analysis, we develop additional analytical and simulation models to investigate the effects of picker blocking in batch picking and to identify the picking, batching, and sorting strategies that reduce congestion. A new batching model (called Indexed order Batching Model (IBM)) is proposed to consider both order proximity and picker blocking to optimize the total order picking time. We also apply the proposed approach to bucket brigade picking systems where hand-off delay as well as picker blocking must be considered. The research offers new insights about picker blocking in batch picking operations, develops batch picking models, and provides complete control procedures for large-scale, dynamic batch picking situations. The twin goals of added flexibility and reduced costs are highlighted throughout the analysis

Lot-Sizing of Several Multi-Product Families

Production planning problems and its variants are widely studied in operations management and optimization literature. One variation that has not garnered much attention is the presence of multiple production families in a coordinated and capacitated lot-sizing setting. While its single-family counterpart has been the subject of many advances in formulations and solution techniques, the latest published research on multiple family problems was over 25 years ago (Erenguc and Mercan, 1990; Mercan and Erenguc, 1993). Chapter 2 begins with a new formulation for this coordinated capacitated lot-sizing problem for multiple product families where demand is deterministic and time-varying. The problem considers setup and holding costs, where capacity constraints limit the number of individual item and family setup times and the amount of production in each period. We use a facility location reformulation to strengthen the lower bound of our demand-relaxed model. In addition, we combine Benders decomposition with an evolutionary algorithm to improve upper bounds on optimal solutions. To assess the performance of our approach, single-family problems are solved and results are compared to those produced by state-of-the-art heuristics by de Araujo et al. (2015) and Süral et al. (2009). For the multi-family setting, we first create a standard test bed of problems, then measure the performance of our heuristic against the SDW heuristic of Süral et al. (2009), as well as a Lagrangian approach. We show that our Benders approach combined with an evolutionary algorithm consistently achieves better bounds, reducing the duality gap compared to other single-family methods studied in the literature. Lot-sizing problems also exist within a vendor-managed-inventory setting, with production-planning, distribution and vehicle routing problems all solved simultaneously. By considering these decisions together, companies achieve reduced inventory and transportation costs compared to when these decisions are made sequentially. We present in Chapter 3 a branch-and-cut algorithm to tackle a production-routing problem (PRP) consisting of multiple products and customers served by a heterogeneous fleet of vehicles. To accelerate the performance of this algorithm, we also construct an upper bounding heuristic that quickly solves production-distribution and routing subproblems, providing a warm-start for the branch-and-cut procedure. In four scenarios, we vary the degree of flexibility in demand and transportation by considering split deliveries and backorders, two settings that are not commonly studied in the literature. We confirm that our upper bounding procedure generates high quality solutions at the root node for reasonably-sized problem instances; as time horizons grow longer, solution quality degrades slightly. Overall costs are roughly the same in these scenarios, though cost proportions vary. When backorders are not allowed (Scenarios 1 and 3), inventory holding costs account for over 90% of total costs and transportation costs contribute less than 0.01%. When backorders are allowed (Scenarios 2 and 4), most of the cost burden is shouldered by production, with transportation inching closer to 0.1% of total costs. In our fifth scenario for the PRP with multiple product families, we employ a decomposition heuristic for determining dedicated routes for distribution. Customers are clustered through k-means++ and a location-alloction subproblem based on their contribution to overall demand, and these clusters remain fixed over the entire planning horizon. A routing subproblem dictates the order in which to visit customers in each period, and we allow backorders in the production-distribution routine. While the branch-and-cut algorithm for Scenarios 1 through 4 quickly finds high quality solutions at the root node, Scenario 5's dedicated routes heuristic boasts high vehicle utilization and comparable overall costs with minimal computational effort

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field