Title : ( Extremal Optimization for Solving Job Shop Scheduling Problem )
Authors: Masoud Gharehjanloo , Majid Vafaei Jahan , Mohammad Reza Akbarzadeh Totonchi , Masoud Nosratabadi ,
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Abstract
Job shop is one of a well known NP-hard optimization problems. In this paper, extremal optimization is proposed for job shop scheduling. Extremal optimization is an evolutionary meta-heuristic method that consecutively substitutes undesirable variables in current solution with a random value and evolves itself toward optimal solution. For EO, the quality of generated initial solution plays an important role in convergence rate and reaching global optimum; hence GT method is utilized for initial solution. This algorithm is implemented on several sample problems on LA datasets and show that optimal solution can be reached quickly on most of the datasets.
Keywords
, Extremal optimization, job shop scheduling problem, GT algorithm, local fitness
@inproceedings{paperid:1026980,
author = {Masoud Gharehjanloo and Majid Vafaei Jahan and Akbarzadeh Totonchi, Mohammad Reza and Masoud Nosratabadi},
title = {Extremal Optimization for Solving Job Shop Scheduling Problem},
booktitle = {International Conference on Computer and Knowledge Engineering},
year = {2011},
location = {مشهد, IRAN},
keywords = {Extremal optimization; job shop scheduling problem; GT algorithm; local fitness},
}
%0 Conference Proceedings
%T Extremal Optimization for Solving Job Shop Scheduling Problem
%A Masoud Gharehjanloo
%A Majid Vafaei Jahan
%A Akbarzadeh Totonchi, Mohammad Reza
%A Masoud Nosratabadi
%J International Conference on Computer and Knowledge Engineering
%D 2011
