@inproceedings{dfe2c42184f5439e927ea9af0eeefa80,
title = "Multi-objective adaptive job shop scheduling using genetic algorithms",
abstract = "The job shop scheduling problem (JBSP) is one of the hardest combinatorial optimization problems. To meet customer requirements profitably it is often necessary to minimize the mean tardiness and mean flow time simultaneously. Moreover adaptive scheduling is necessary to deal with internal and external disruptions in real time manufacturing environments. This paper presents a method to solve the adaptive, multi-objective JBSP. An asexual reproduction genetic algorithm (GA) with multiple mutation strategies is developed to solve the multi-objective optimization problem. The findings indicate that the GA model can find good solutions within a short computational time.",
keywords = "Adaptive scheduling, Asexual reproduction, Genetic algorithms, Job shop scheduling, Multi-objective optimization",
author = "Haritha Metta and Fazleena Badurdeen",
year = "2009",
language = "English",
isbn = "0872638626",
series = "Transactions of the North American Manufacturing Research Institution of SME",
pages = "517--524",
booktitle = "Transactions of the North American Manufacturing Research Institution of SME - 37th Annual North American Manufacturing Research Conference, NAMRC 37",
note = "37th Annual North American Manufacturing Research Conference, NAMRC 37 ; Conference date: 19-05-2009 Through 22-05-2009",
}