Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.5/1427
Título: A Comparison of Discrete and Continuous Neural Network Approaches to Solve the Class/Teacher Timetabling Problem
Autor: Carrasco, Marco Paulo
Pato, Margarida Vaz
Palavras-chave: Timetabling
Metaheuristics
Neural Networks
Data: 2001
Editora: Centro de Investigação Operacional - Universidade de Lisboa
Citação: Carrasco, Marco Paulo e Margarida Vaz Pato. 2001. "A Comparison of Discrete and Continuous Neural Network Approaches to Solve the Class/Teacher Timetabling Problem". Universidade de Lisboa - Centro de Investigação Operacional - CIO – Working paper nº 4/2001
Resumo: This study explores the application of neural network-based heuristics to the class/teacher timetabling problem (CTTP). The paper begins by presenting the basic CTTP characteristics in terms of hard and soft constraints and proposing a formulation for the energy function required to map the problem within the artificial neural network model. There follow two distinct approaches to simulating neural network evolution. The first uses a Potts mean-field annealing simulation based on continuous Potts neurons, which has obtained favorable results in various combi¬natorial optimization problems. Afterwards, a discrete neural network simulation, based on discrete winner-take-all neurons, is proposed. The paper concludes with a comparison of the computational results taken from the application of both heuris¬tics to hard hypothetical and real CTTP instances. This experiment demonstrates that the discrete approach performs better, in terms of solution quality as well as execution time.
URI: http://hdl.handle.net/10400.5/1427
Aparece nas colecções:DM - Documentos de trabalho / Working Papers

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