Scheduling on parallel machines with a common server in charge of loading and unloading operations

This paper addresses the scheduling problem on two identical parallel machines with a single server in charge of loading and unloading operations of jobs. Each job has to be loaded by the server before being processed on one of the two machines and unloaded by the same server after its processing. No delay is allowed between loading and processing, and between processing and unloading. The objective function involves the minimization of the makespan. This problem referred to as P2, S1|sj , tj |Cmax generalizes the classical parallel machine scheduling problem with a single server which performs only the loading (i.e., setup) operation of each job. For this NP-hard problem, no solution algorithm was proposed in the literature. Therefore, we present two mixedinteger linear programming (MILP) formulations, one with completion-time variables along with two valid inequalities and one with time-indexed variables. In addition, we propose some polynomial-time solvable cases and a tight theoretical lower bound. In addition, we show that the minimization of the makespan is equivalent to the minimization of the total idle times on the machines. To solve large-sized instances of the problem, an efficient General Variable Neighborhood Search (GVNS) metaheuristic with two mechanisms for finding an initial solution is designed. The GVNS is evaluated by comparing its performance with the results provided by the MILPs and another metaheuristic. The results show that the average percentage deviation from the theoretical lower-bound of GVNS is within 0.642%. Some managerial insights are presented and our results are compared with the related literature.Comment: 40 pages, 4 figures, 16 table

Scheduling with processing set restrictions : a survey

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

The energy scheduling problem: Industrial case-study and constraint propagation techniques

This paper deals with production scheduling involving energy constraints, typically electrical energy. We start by an industrial case-study for which we propose a two-step integer/constraint programming method. From the industrial problem we derive a generic problem,the Energy Scheduling Problem (EnSP). We propose an extension of specific resource constraint propagation techniques to efficiently prune the search space for EnSP solving. We also present a branching scheme to solve the problem via tree search.Finally,computational results are provided

A Survey of Serverless Machine Learning Model Inference

Recent developments in Generative AI, Computer Vision, and Natural Language Processing have led to an increased integration of AI models into various products. This widespread adoption of AI requires significant efforts in deploying these models in production environments. When hosting machine learning models for real-time predictions, it is important to meet defined Service Level Objectives (SLOs), ensuring reliability, minimal downtime, and optimizing operational costs of the underlying infrastructure. Large machine learning models often demand GPU resources for efficient inference to meet SLOs. In the context of these trends, there is growing interest in hosting AI models in a serverless architecture while still providing GPU access for inference tasks. This survey aims to summarize and categorize the emerging challenges and optimization opportunities for large-scale deep learning serving systems. By providing a novel taxonomy and summarizing recent trends, we hope that this survey could shed light on new optimization perspectives and motivate novel works in large-scale deep learning serving systems.Comment: 13 page

Intralogistics fleet management system for highly secure areas

With the end of traditional centralized applications for production control, a new industrial era (industry 4.0) introduces computational concepts and a vision of cyberphysical ecosystems associated with intelligent factories. Using automation technologies adopts the combination of the physical and the cyber world’s, to make the technologies involved more complex and precise [48].With the search for better resource use, time, cost, and quality has been introducing by the industry, new technological challenges as work agents, instead of the traditional man labor. Which has been replaced by autonomous robotic mechanisms. Many entities choose autonomous mobile units to fulfill the dispatching tasks and the supply of material to workstations. Thus, it is necessary to have a system capable of managing all activities and behaviors adjacent to these autonomous mobile robots. The objective of this dissertation is the implementation of a management system for a fleet of mobile robots that perform logistics processes in a secure environment. The robots will move between the production and expedition areas and autonomously carry out the loading and unloading of goods between workstations. Sharing spaces and resources among themselves and with factory operators, such as stations, corridors, security doors, and elevators. The proposed model explores the scheduling of tasks to robots as their monitoring and security in all processes, from autonomous navigation to area’s transition of areas by the present security devices. The presence of several sets of sensors and devices in these units is essential to provide sensorial information about the manufacturing environment and at the same time assisting in the execution of the tasks, such as navigation, transport, loading. To extract all the functionalities, this system will also be integrated with the management and security systems of the factory, to create an overview of the current state of the shop-floor and to plan accordingly. They are integrated to provide secure communication, based on reliable protocols that ensure the veracity and communication of the entities.Com o fim das tradicionais aplicações centralizadas para controle de produção a chegada de uma nova era industrial (indústria 4.0), introduz conceitos computacionais e uma visão de ecossistemas de ciber-fisicos associados a fábricas inteligentes. Utilizando tecnologias de automação, adota a combinação do mundo físico e do mundo cibernético, para tornar as tecnologias envolvidas mais complexas e precisas [48]. Com a procura de um melhor aproveitamento de recursos, de tempo, custo e qualidade por parte da indústria, esta tem vindo a introduzir novos desafios tecnológicos como ferramentas de trabalho, ao invés da tradicional mão de obra realizada pelo homem. Que tem vindo a ser substituída por mecanismos autônomos robotizados. Muitas entidades optam por unidades autônomas móveis para o cumprimento das tarefas de despacho e de fornecimento de material às estações de trabalho. Desta forma é necessário um sistema capaz de gerir todas as atividades e comportamentos adjacentes a estes robôs moveis autônomos. O objetivo desta dissertação é a implementação de um sistema de gestão para uma frota de robots movéis que realizam processos de logística em fábricas de segurança. Os robots transitarão entre as áreas de produção e áreas de expedição para realizar autonomamente processos de carga e descarga de mercadorias nas estações de trabalho. Compartilhando entre si e com os operadores da fábrica espaços e recursos, como estações, corredores, portas de segurança e elevadores. O modelo proposto explora o agendamento de tarefas aos robots como respetiva monitorização e segurança, em todos os processos desde navegação autónoma até á transição de espaços usando as estruturas de segurança presentes. Para que exista uma comunicação entre um sistema de gestão da frota e as unidades móveis é essencial a presença de vários conjuntos de sensores e dispositivos nestas unidades, para que estes estejam aptas a fornecer informação sensorial sobre ambiente fabril e ao mesmo tempo no auxiliar na execução de tarefas, como navegação, transporte etc. A fim de extrair todas as funcionalidades este sistema será também integrado com os sistemas de gestão e de segurança da fábrica, de forma a criar uma visão geral do estado atual da fábrica e planejar adequadamente. São integrados conjuntamente para fornecer uma comunicação segura, com base em protocolos entre as entidades

Melhoria do desempenho em sistemas de escalonamento-Híbrido SJF/FIFO através da gestão do tamanho de jobs

Orientadores: Michel Daoud Yacoub, Edson Luiz UrsiniTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Esta tese propõe um método de simulação discreta para planejar e gerenciar o desempenho de sistemas de filas M/G/1 de relativa complexidade. O sistema é formado por servidores em paralelo submetidos a mudanças dinâmicas entre as políticas de escalonamento SJF e FIFO. O tráfego de entrada de entidades é aleatório com oscilações até a sobrecarga do sistema. As entidades são formadas por multiplas classes de jobs e cada servidor processa uma única classe desses jobs. O método gerencia o desempenho das classes de jobs que provocam perda no desempenho do sistema por atrasos no tempo médio de residência. O modelo de simulação obtém o tempo médio de residência relativo de cada classe de job para calcular o atraso relativo dessas classes no atraso total do sistema. Os jobs que ultrapassam os limites dos requisitos podem ser gerenciados, e.g., direcionados para outros servidores, ou serem bloqueados temporária ou definitivamente. Como exemplo de um problema complexo, apresentamos um estudo de caso logístico de carregamento e movimentação de cargas dentro da área de produção industrial. As cargas são formadas por múltiplas classes de produtos simultaneamente carregados em diferentes servidores. A implementação desse modelo logístico é iniciada com um modelo reconhecido e validado e prossegue com pequenos incrementos validados até a representação de um modelo o mais próximo possível da realidade. A técnica de Escalonamento Híbrido de Sistemas com Gestão do Tamanho de Jobs permite a mudança dinâmica de políticas de escalonamento do sistema entre SJF e FIFO, ainda que sujeita a variações abruptas de tráfego de entrada. Essa técnica é efetiva para reduzir os tempos médios, conter os tempos máximos e habilitar a identificação dos jobs que provocam atrasos, permitindo dessa forma, ações de gestão para mitigar resultados indesejadosAbstract: This thesis proposes a discrete-event simulation method to plan and manage the performance of M/G/1 queuing systems of relative complexity. The system has parallel servers undergoing dynamic changes under system instabilities (e.g., spontaneous oscillations of the incoming traffic to the system overload). Entities have multi classes of jobs and each server performs a single class of this jobs. The method manages the size of jobs that may cause loss of performance, e.g. the delays in average residence time. Performance management is carried out via the monitoring of the impact of classes of job sizes on the total system delay. Jobs that exceed a certain threshold value may then be managed accordingly, e.g. by moving them to different servers or by (temporarily or permanently) blocking them. We present a case study of loading and moving cargoes within an industrial production area. Each cargo consists of multiple product classes which are simultaneously loaded on different servers. This logistic-model implementation begins with a well-known validated model extended by small validated increments to better be able to represent the real-world. The technique of Improving the Performance of SJF/FIFO Hybrid-Scheduling Systems through the Management of Job Size under dynamic conditions, i.e. when subject to toggling SJF and FIFO policies and fluctuations of inbound traffic, has shown to be effective in the reduction of the time average, in the decrease of the mean time maximum, and in the identification of jobs that cause delays to the system, and thus enable the management of these jobs to mitigate unwanted system performanceDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétrica138.553/2014CAPE

Using hierarchical scheduling to support soft real-time applications in general-purpose operating systems

Journal ArticleThe CPU schedulers in general-purpose operating systems are designed to provide fast response time for interactive applications and high throughput for batch applications. The heuristics used to achieve these goals do not lend themselves to scheduling real-time applications, nor do they meet other scheduling requirements such as coordinating scheduling across several processors or machines, or enforcing isolation between applications, users, and administrative domains. Extending the scheduling subsystems of general-purpose operating systems in an ad hoc manner is time consuming and requires considerable expertise as well as source code to the operating system. Furthermore, once extended, the new scheduler may be as inflexible as the original. The thesis of this dissertation is that extending a general-purpose operating system with a general, heterogeneous scheduling hierarchy is feasible and useful. A hierarchy of schedulers generalizes the role of CPU schedulers by allowing them to schedule other schedulers in addition to scheduling threads. A general, heterogeneous scheduling hierarchy is one that allows arbitrary (or nearly arbitrary) scheduling algorithms throughout the hierarchy. In contrast, most of the previous work on hierarchical scheduling has imposed restrictions on the schedulers used in part or all of the hierarchy. This dissertation describes the Hierarchical Loadable Scheduler (HLS) architecture, which permits schedulers to be dynamically composed in the kernel of a general-purpose operating system. The most important characteristics of HLS, and the ones that distinguish it from previous work, are that it has demonstrated that a hierarchy of nearly arbitrary schedulers can be efficiently implemented in a general-purpose operating system, and that the behavior of a hierarchy of soft real-time schedulers can be reasoned about in order to provide guaranteed scheduling behavior to application threads. The flexibility afforded by HLS permits scheduling behavior to be tailored to meet complex requirements without encumbering users who have modest requirements with the performance and administrative costs of a complex scheduler. Contributions of this dissertation include the following. (1) The design, prototype implementation, and performance evaluation of HLS in Windows 2000. (2) A system of guarantees for scheduler composition that permits reasoning about the scheduling behavior of a hierarchy of soft real-time schedulers. Guarantees assure users that application requirements can be met throughout the lifetime of the application, and also provide application developers with a model of CPU allocation to which they can program. (3) The design, implementation, and evaluation of two augmented CPU reservation schedulers, which provide increase scheduling predictability when low-level operating system activity steals time from applications

Conception d'un modèle architectural collaboratif pour l'informatique omniprésente à la périphérie des réseaux mobiles

Le progrès des technologies de communication pair-à-pair et sans fil a de plus en plus permis l’intégration de dispositifs portables et omniprésents dans des systèmes distribués et des architectures informatiques de calcul dans le paradigme de l’internet des objets. De même, ces dispositifs font l'objet d'un développement technologique continu. Ainsi, ils ont toujours tendance à se miniaturiser, génération après génération durant lesquelles ils sont considérés comme des dispositifs de facto. Le fruit de ces progrès est l'émergence de l'informatique mobile collaborative et omniprésente, notamment intégrée dans les modèles architecturaux de l'Internet des Objets. L’avantage le plus important de cette évolution de l'informatique est la facilité de connecter un grand nombre d'appareils omniprésents et portables lorsqu'ils sont en déplacement avec différents réseaux disponibles. Malgré les progrès continuels, les systèmes intelligents mobiles et omniprésents (réseaux, dispositifs, logiciels et technologies de connexion) souffrent encore de diverses limitations à plusieurs niveaux tels que le maintien de la connectivité, la puissance de calcul, la capacité de stockage de données, le débit de communications, la durée de vie des sources d’énergie, l'efficacité du traitement de grosses tâches en termes de partitionnement, d'ordonnancement et de répartition de charge. Le développement technologique accéléré des équipements et dispositifs de ces modèles mobiles s'accompagne toujours de leur utilisation intensive. Compte tenu de cette réalité, plus d'efforts sont nécessaires à la fois dans la conception structurelle tant au matériel et logiciel que dans la manière dont il est géré. Il s'agit d'améliorer, d'une part, l'architecture de ces modèles et leurs technologies de communication et, d'autre part, les algorithmes d'ordonnancement et d'équilibrage de charges pour effectuer leurs travaux efficacement sur leurs dispositifs. Notre objectif est de rendre ces modèles omniprésents plus autonomes, intelligents et collaboratifs pour renforcer les capacités de leurs dispositifs, leurs technologies de connectivité et les applications qui effectuent leurs tâches. Ainsi, nous avons établi un modèle architectural autonome, omniprésent et collaboratif pour la périphérie des réseaux. Ce modèle s'appuie sur diverses technologies de connexion modernes telles que le sans-fil, la radiocommunication pair-à-pair, et les technologies offertes par LoPy4 de Pycom telles que LoRa, BLE, Wi-Fi, Radio Wi-Fi et Bluetooth. L'intégration de ces technologies permet de maintenir la continuité de la communication dans les divers environnements, même les plus sévères. De plus, ce modèle conçoit et évalue un algorithme d'équilibrage de charge et d'ordonnancement permettant ainsi de renforcer et améliorer son efficacité et sa qualité de service (QoS) dans différents environnements. L’évaluation de ce modèle architectural montre des avantages tels que l’amélioration de la connectivité et l’efficacité d’exécution des tâches. Advances in peer-to-peer and wireless communication technologies have increasingly enabled the integration of mobile and pervasive devices into distributed systems and computing architectures in the Internet of Things paradigm. Likewise, these devices are subject to continuous technological development. Thus, they always tend to be miniaturized, generation after generation during which they are considered as de facto devices. The success of this progress is the emergence of collaborative mobiles and pervasive computing, particularly integrated into the architectural models of the Internet of Things. The most important benefit of this form of computing is the ease of connecting a large number of pervasive and portable devices when they are on the move with different networks available. Despite the continual advancements that support this field, mobile and pervasive intelligent systems (networks, devices, software and connection technologies) still suffer from various limitations at several levels such as maintaining connectivity, computing power, ability to data storage, communication speeds, the lifetime of power sources, the efficiency of processing large tasks in terms of partitioning, scheduling and load balancing. The accelerated technological development of the equipment and devices of these mobile models is always accompanied by their intensive use. Given this reality, it requires more efforts both in their structural design and management. This involves improving on the one hand, the architecture of these models and their communication technologies, and, on the other hand, the scheduling and load balancing algorithms for the work efficiency. The goal is to make these models more autonomous, intelligent, and collaborative by strengthening the different capabilities of their devices, their connectivity technologies and the applications that perform their tasks. Thus, we have established a collaborative autonomous and pervasive architectural model deployed at the periphery of networks. This model is based on various modern connection technologies such as wireless, peer-to-peer radio communication, and technologies offered by Pycom's LoPy4 such as LoRa, BLE, Wi-Fi, Radio Wi-Fi and Bluetooth. The integration of these technologies makes it possible to maintain the continuity of communication in the various environments, even the most severe ones. Within this model, we designed and evaluated a load balancing and scheduling algorithm to strengthen and improve its efficiency and quality of service (QoS) in different environments. The evaluation of this architectural model shows payoffs such as improvement of connectivity and efficiency of task executions

Optimization of job shop scheduling with material handling by automated guided vehicle

Job Shop Scheduling with Material Handling has attracted increasing attention in both industry and academia, especially with the inception of Industry 4.0 and smart manufacturing. A smart manufacturing system calls for efficient and effective production planning. On a typical modern shop floor, jobs of various types follow certain processing routes through machines or work centers, and automated guided vehicles (AGVs) are utilized to handle the jobs. In this research, the optimization of a shop floor with AGV is carried out, and we also consider the planning scenario under variable processing time of jobs. The goal is to minimize the shop floor production makespan or other specific criteria correlated with makespan, by scheduling the operations of job processing and routing the AGVs. This dissertation includes three research studies that will constitute my doctoral work. In the first study, we discuss a simplified case in which the scheduling problem is reformulated into a vehicle dispatching (assignment) problem. A few AGV dispatching strategies are proposed based on the deterministic optimization of network assignment problems. The AGV dispatching strategies take future transportation requests into consideration and optimally configure transportation resources such that material handling can be more efficient than those adopting classic AGV assignment rules in which only the current request is considered. The strategies are demonstrated and validated with a case study based on a shop floor in literature and compared to classic AGV assignment rules. The results show that AGV dispatching with adoption of the proposed strategy has better performance on some specific criterions like minimizing job waiting time. In the second study, an efficient heuristic algorithm for classic Job Shop Scheduling with Material Handling is proposed. Typically, the job shop scheduling problem and material handling problem are studied separately due to the complexity of both problems. However, considering these two types of decisions in the same model offers benefits since the decisions are related to each other. In this research, we aim to study the scheduling of job operations together with the AGV routing/scheduling, and a formulation as well as solution techniques are proposed. The proposed heuristic algorithm starts from an optimal job shop scheduling solution without limiting the size of AGV fleet, and iteratively reduces the number of available vehicles until the fleet size is equal to the original requirements. The computational experiments suggest that compared to existing solution techniques in literature, the proposed algorithm can achieve comparable solution quality on makespan with much higher computational efficiency. In the third study, we take the variability of processing time into consideration in optimizing job shop scheduling with material handling. Variability caused by random effects and deterioration is discussed, and a series of models are developed to accommodate random and deteriorating processing time respectively. With random processing time, the model is formulated as a Stochastic Programming Job Shop Scheduling with Material Handling model, and with deteriorating processing time the model can be nonlinear under specific deteriorating functions. Based on a widely adopted dataset in existing literature, the stochastic programming model were solved with Pyomo, and models with deterioration were linearized and solved with CPLEX. By considering variable processing time, the JSSMH models can better adapt to real production scenarios