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CIIA
2009
15 years 5 months ago
Dynamic Scheduling in Petroleum Process using Reinforcement Learning
Petroleum industry production systems are highly automatized. In this industry, all functions (e.g., planning, scheduling and maintenance) are automated and in order to remain comp...
Nassima Aissani, Bouziane Beldjilali
128
Voted
VTC
2007
IEEE
130views Communications» more  VTC 2007»
15 years 11 months ago
Data Stream Processing Infrastructure for Intelligent Transport Systems
— Intelligence Transportation Systems are critical to improve the efficiency of modern transportation by reducing congestions, providing real time road conditions. A system that...
Eric Bouillet, Mark Feblowitz, Zhen Liu, Anand Ran...
LCTRTS
2001
Springer
15 years 9 months ago
ILP-based Instruction Scheduling for IA-64
The IA-64 architecture has been designed as a synthesis of VLIW and superscalar design principles. It incorporates typical functionality known from embedded processors as multiply...
Daniel Kästner, Sebastian Winkel
142
Voted
GECCO
2006
Springer
155views Optimization» more  GECCO 2006»
15 years 8 months ago
Comparison of genetic representation schemes for scheduling soft real-time parallel applications
This paper presents a hybrid technique that combines List Scheduling (LS) with Genetic Algorithms (GA) for constructing non-preemptive schedules for soft real-time parallel applic...
Yoginder S. Dandass, Amit C. Bugde
141
Voted
CDC
2010
IEEE
130views Control Systems» more  CDC 2010»
14 years 11 months ago
Decomposability of global tasks for multi-agent systems
Abstract-- Multi-agent system is a rapidly developing research area with strong support from both civilian and military applications. One of the essential problems in multi-agent s...
Mohammad Karimadini, Hai Lin 0002