Integrating CBR and Heuristic Search for Learning and Reusing Solutions in Real-time Task Scheduling

Coello, Juan Manuel Adán; Santos, Ronaldo Camilo dos

doi:10.1007/3-540-48508-2_7

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…The human reasoning system can learn over time, reason in domains with incomplete or non-well-defined concepts, and provide means for explanation. The CBR is a well established methodology with broad applications such as medical science (Ahn and Kim, 2009), finance (Li and Ho, 2009), electronics (Chang et al, 2008), chemical engineering for quality design (Suh et al, 1998), intelligent Web-based sales service (Watson and Gardingen, 1999;Wilke et al, 1998), building and mechanical design (Nikolaychuk and Yurin, 2008;Rivard and Fenves, 2000;Mileman et al, 2002), material science (Amen and Vomacka, 2001;Mejasson et al, 2001), complex fault finding and troubleshooting (Aha et al, 1999) as well as planning and real-time scheduling tasks Coello and dos Santos, 1999).…”

Section: Cbr Backgroundmentioning

confidence: 99%

Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

Xing¹,

Ding²,

Chai³

et al. 2012

Engineering Applications of Artificial Intelligence

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a b s t r a c tIn this paper, a hybrid intelligent parameter estimation algorithm is proposed for predicting the strip temperature during laminar cooling process. The algorithm combines a hybrid genetic algorithm (HGA) with grey case-based reasoning (GCBR) in order to improve the precision of the strip temperature prediction. In this context, the hybrid genetic algorithm is formed by combining the genetic algorithm with an annealing and a local multidimensional search algorithm based on deterministic inverse parabolic interpolation. Firstly, the weight vectors of retrieval features in case-based reasoning are optimised using hybrid genetic algorithm in offline mode, and then they are used in grey case-based reasoning to accurately estimate the model parameters online. The hybrid intelligent parameter estimation algorithm is validated using a set of operational data gathered from a hot-rolled strip laminar cooling process in a steel plant. Experiment results show the effectiveness of the proposed method in improving the precision of the strip temperature prediction. The proposed method can be used in real-time temperature control of hot-rolled strip and has potential for parameter estimation of different types of cooling process.

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Section: Cbr Backgroundmentioning

confidence: 99%

Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

Xing¹,

Ding²,

Chai³

et al. 2012

Engineering Applications of Artificial Intelligence

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“…A case contains the description of the problem and its solution, or part of the solution. This method was used for the resolution of manufacturing scheduling problems [18,19] and university courses timetabling [13]. It was also used for constructing Metaheuristics' initial solutions, as Genetic Algorithms [20] and Simulated Annealing [21].…”

Section: Case-based Reasoningmentioning

confidence: 99%

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

Madureira

Pereira

2010

2010 10th International Conference on Hybrid Intelligent Systems

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Abstract:A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

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“…The authors started first by providing a solution to the travelling salesman problem and then to a single machine scheduling problem with sequence dependent setup times. Coello and DosSantos (1999) used CBR and heuristic search for a real-time scheduling system. The problem addressed involves scheduling sets of tasks with precedence, ready time and deadline constraints.…”

Section: Cbrmentioning

confidence: 99%

Intelligent systems in manufacturing: current developments and future prospects

Meziane

Vadera

Kobbacy

et al. 2000

Integrated Manufacturing Systems

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Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

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Integrating CBR and Heuristic Search for Learning and Reusing Solutions in Real-time Task Scheduling

Cited by 11 publications

References 9 publications

Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

Intelligent systems in manufacturing: current developments and future prospects

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