TORSCHE Scheduling Toolbox for Matlab

Šůcha, Přemysl; Kutil, Michal; Sójka, Michał; Hanzálek, Zdeněk

doi:10.1109/cacsd.2006.285479

Cited by 11 publications

(8 citation statements)

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“…We used Time Optimization of Resources SCHEduling (TORSCHE) for task scheduling simulations, which is intended as a tool to handle task scheduling problems . A task is defined as a data structure, including all scheduling process parameters of task as processing, arrival, starting, release, deadline, due date, etc.…”

Section: Problem Definitionmentioning

confidence: 99%

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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Summary Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.

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Section: Problem Definitionmentioning

confidence: 99%

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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“…The simulation horizon considered is that of 52 periods. Schedules are generated at the beginning of each period using the TORSCHE scheduling toolbox . For simplicity, list scheduling is used for schedule generation.…”

Section: Case Studiesmentioning

confidence: 99%

“…In the interest of space, the reader is referred to the formulation of Kondili et al that is followed in this work. The resulting mixed integer linear programming problem (MILP) is solved using the internal ILP solver “glpk” of TORSCHE scheduling toolbox . The schedule thus generated is executed by the Simulink model of the fermenter.…”

Section: Case Studiesmentioning

confidence: 99%

Integrating Production Control and Scheduling in Multisite Enterprises on the Basis of Real-Time Detection of Divergence

Rathi

Bhumireddy

Nandola³

et al. 2016

Ind. Eng. Chem. Res.

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Scheduling and process control have been long recognized as the two critical building blocks in many manufacturing execution systems. Operating at the interface between the supply chain and the process, the scheduler generates a detailed schedule that has to be executed by the process so as to meet the demands originating from the supply chain. Given the tight interactions between the two, there has been wide interest in integrating scheduling and process control. Our key insight is that abnormalities which occur after generation of the original schedule trigger a divergence between the operational targets defined by the schedule and its execution. If left uncorrected, then the abnormalities will propagate between the process and the supply chain. A timely response could eliminate or minimize such effects. However, this is a challenge particularly in large multisite enterprises where the scheduling and production responsibilities are typically separated across departments and even across geographical locations. Recognizing this, we propose a novel, scalable framework for integrating scheduling and process control that detects in real time when a divergence occurs between the original schedule and its execution in the process. It then identifies the root-cause(s) of the divergence, i.e., the abnormality, and triggers a suitable response from the scheduler and the process so as to nullify or minimize its effect. In this paper, we will describe the proposed approach and illustrate it using two industrially motivated case studies.

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“…A ferramenta Jitterbug apresenta uma grande abstração na definição dos atrasos de comunicação, porém permite o cálculo de uma função custo relacionada à qualidade de controle do NCS sob diferentes condições temporais do NCS. As ferramentas AIDA (REDELL, EL-KHOURY & TORNGREN, 2004) e TORSCHE (SUCHA et al, 2006) foram desenvolvidas para a análise de características de tempo real de NCS. A ferramenta AIDA é usada para análise temporal de NCS enquanto que a aplicação de algoritmos de escalonamento para otimização da resposta do NCS representa a principal finalidade da ferramenta TORSCHE.…”

Section: De Acordo Comunclassified

“…DESCRIÇÃO DA FERRAMENTA A ferramenta TORSCHE (Time Optimization of Resources, Scheduling), desenvolvida pelo Departamento de Engenharia de Controle da Universidade Técnica Tcheca em Praga na República Tcheca, é um toolbox do Matlab composto por algoritmos de escalonamento que podem ser utilizados com o intuito de otimizar a resposta temporal de um NCS. Com essa otimização consegue-se atingir parâmetros de tempo real desejados para o sistema de controle, como tempo de resposta e atraso de comunicação (delay)(SUCHA et al, 2006). A ferramenta pode ser utilizada também para verificar o desempenho e operação de um NCS antes de sua implementação física, através da comparação de métricas de desempenho relacionadas ao sistema de tempo real com os requisitos definidos no projeto.De acordo com o algoritmo de controle e recursos computacionais especificados para o sistema, a ferramenta permite analisar parâmetros como período de amostragem e jitter.…”

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Desenvolvimento de sistemas de controle via rede (NCS) para aplicações em redes com protocolo CAN

Godoy¹

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, companheirismo, dedicação e orientação. Ao Professor Ricardo Yassushi Inamasu e a todos os amigos e funcionários do Departamento de Engenharia Mecânica da EESC/USP, em especial aos do Laboratório de Simulação e Controle, pela convivência e colaboração. À Professora Dawn M. Tilbury e amigos da Universidade de Michigan nos Estados Unidos pela oportunidade e aprendizagem durante a realização do estágio de doutorado sanduíche. Aos amigos da minha cidade Rio Claro e a minha família pelo apoio e incentivo nas horas de desânimo e pela alegria e descontração nas horas de felicidade. À Fundação de Amparo a Pesquisa do Estado de São Paulo-FAPESP pela concessão da Bolsa de Doutorado (Processo 2007/05019-0) e pelo apoio financeiro junto ao Projeto de Auxílio a Pesquisa (Processo 2008/03481-1) imprescindíveis para a realização desta pesquisa. RESUMO GODOY, E. P. Desenvolvimento de Sistemas de Controle via Rede (NCS) para Aplicações em Redes com Protocolo CAN. 252f.

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TORSCHE Scheduling Toolbox for Matlab

Cited by 11 publications

References 0 publications

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Integrating Production Control and Scheduling in Multisite Enterprises on the Basis of Real-Time Detection of Divergence

Desenvolvimento de sistemas de controle via rede (NCS) para aplicações em redes com protocolo CAN

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