Coil batching to improve productivity and energy utilization in steel production

This paper investigates a practical batching decision problem that arises in the batch annealing operations in the cold rolling stage of steel production faced by most large iron and steel companies in the world. The problem is to select steel coils from a set of waiting coils to form batches to be annealed in available batch annealing furnaces and choose a median coil for each furnace. The objective is to maximize the total reward of the selected coils less the total coil'coil and coil'furnace mismatching cost. For a special case of the problem that arises frequently in practical settings where the coils are all similar and there is only one type of furnace available, we develop a polynomial-time dynamic programming algorithm to obtain an optimal solution. For the general case of the problem, which is strongly NP-hard, an exact branch-and-price-and-cut solution algorithm is developed using a column and row generation framework. A variable reduction strategy is also proposed to accelerate the algorithm. The algorithm is capable of solving medium-size instances to optimality within a reasonable computation time. In addition, a tabu search heuristic is proposed for solving larger instances. Three simple search neighborhoods, as well as a sophisticated variable-depth neighborhood, are developed. This heuristic can generate near-optimal solutions for large instances within a short computation time. Using both randomly generated and real-world production data sets, we show that our algorithms are superior to the typical rule-based planning approach used by many steel plants. A decision support system that embeds our algorithms was developed and implemented at Baosteel to replace their rule-based planning method. The use of the system brings significant benefits to Baosteel, including an annual net profit increase of at least 1.76 million U.S. dollars and a large reduction of standard coal consumption and carbon dioxide emissions

Alocação de Máquinas Virtuais em Ambientes de Computação em Nuvem Baseada em Requisitos de Service Level Agreement

A computação em nuvem teve um avanço considerável nos últimos anos, trazendo grandes benefícios incluindo escalabilidade, flexibilidade, acessibilidade global, melhor utilização de recursos e redução de custos, entre outros. Apesar de todos os benefícios, esta adesão e crescimento trás consigo grandes desafios como otimização do uso de recursos computacionais, redução de custos, garantia da qualidade de serviço (Quality of Service (QoS)), segurança, etc. As garantias da qualidade de serviço são estabelecidas através de Service Level Agreements (SLAs), que são contratos estabelecidos entre o cliente e o fornecedor do serviço de computação em nuvem, visando especificar de forma mensurável as metas de nível de serviço a serem cumpridas, além dos papéis e responsabilidades das partes envolvidas. Este trabalho apresenta um estudo sobre cumprimento de SLAs por algoritmos de alocação de máquinas virtuais em ambientes de computação em nuvem. O trabalho tem em consideração métricas como disponibilidade, custo, tempo de conclusão de uma aplicação (task completion time) e nível de tolerância a faltas, avaliando o cumprimento de tais métricas em diferentes cenários. O estudo é realizado utilizando o framework CloudSim Plus para modelação e execução de simulações de computação em nuvem. São introduzidos dois módulos no framework visando: (i) especificação de SLAs e templates de máquinas virtuais em formato JavaScript Object Notation (JSON), seguindo padrões do Amazon Elastic Compute Cloud (Amazon EC2); (ii) injeção de faltas aleatórias, permitindo avaliar como os SLAs são afetados perante o surgimento de faltas nos servidores. Por fim, o trabalho apresenta uma proposta para automação da criação e alocação de máquinas virtuais, visando cumprir os SLAs e libertar o cliente da necessidade de especificar a quantidade mínima de máquinas virtuais para atendimento dos níveis de serviço exigidos. Mesmo com todo o nível de automação que os fornecedores de computação em nuvem possam oferecer, os resultados obtidos mostram que é possível melhorar a automação destes serviços, reduzindo a necessidade de intervenção do cliente e as violações de SLA devido a uma inadequada configuração de máquinas virtuais realizada pelo cliente.Cloud computing has made considerable progress in recent years, bringing great benefits including scalability, flexibility, global accessibility, improved resource utilization and cost savings, among others. Despite all the benefits, this adhesion and growth carries with it great challenges such as optimization of the use of computational resources, reduction of costs, Quality of Service (QoS) assurance, security, etc. Guarantees are provided through Service Level Agreements (SLAs), which are agreements between the customer and the cloud computing service provider to measurably specify the service level goals to be fulfilled, as well as the roles and responsibilities of the parties involved. This work presents a study on compliance with service level agreements by algorithms for allocating virtual machines in cloud computing environments. The work takes into account metrics such as availability, cost, task completion time and level of fault tolerance, evaluating the compliance of such metrics in different scenarios. The study is conducted using the CloudSim Plus framework for modeling and running cloud computing simulations. Two modules are introduced in the framework about: (i) specification of SLAs and virtual machine templates in JSON format, following Amazon Elastic Compute Cloud (Amazon EC2) standards; (ii) injection of random faults, allowing to evaluate how the SLAs are affected by the occurrence of faults in servers. Finally, this work presents a proposal for automation of the creation and allocation of virtual machines, aiming to comply with the SLAs and free the client from the need to specify the minimum number of virtual machines to meet the required service levels. Even with all the automation level provided by cloud service providers, the obtained results show it is possible to further improve the automation of these services by reducing the need for customer intervention and SLA violations due to an inadequate configuration of virtual machines performed by the client