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dc.contributor.supervisor ElMekkawy, Tarek (Mechanical and Manufacturing Engineering) en_US
dc.contributor.author Al-Hinai, Nasr
dc.date.accessioned 2011-10-07T16:10:05Z
dc.date.available 2011-10-07T16:10:05Z
dc.date.issued 2011
dc.identifier.citation Al-Hinai, N. and ElMekkawy, T. (2011a) ‘An efficient hybridized genetic algorithm architecture for the flexible job-shop scheduling problem’, Flexible Services and Manufacturing Journal, Vol. 23, pp. 64-85, doi: 10.1007/s10696-010-9067-y en_US
dc.identifier.citation Al-Hinai, N. And ElMekkawy, T.Y (2011b) ‘Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm’, International Journal of Production Economics, Vol. 132, pp. 279-291, doi: 10.1016/j.ijpe.2011.04.020 en_US
dc.identifier.uri http://hdl.handle.net/1993/4955
dc.description.abstract Flexible job-shop scheduling problem (FJSP) is a generalization of the classical job-shop scheduling problem (JSP). It takes shape when alternative production routing is allowed in the classical job-shop. However, production scheduling becomes very complex as the number of jobs, operations, parts and machines increases. Until recently, scheduling problems were studied assuming that all of the problem parameters are known beforehand. However, such assumption does not reflect the reality as accidents and unforeseen incidents happen in real manufacturing systems. Thus, an optimal schedule that is produced based on deterministic measures may result in a degraded system performance when released to the job-shop. For this reason more emphasis is put towards producing schedules that can handle uncertainties caused by random disruptions. The current research work addresses solving the deterministic FJSP using evolutionary algorithm and then modifying that method so that robust and/or stable schedules for the FJSP with the presence of disruptions are obtained. Evolutionary computation is used to develop a hybridized genetic algorithm (hGA) specifically designed for the deterministic FJSP. Its performance is evaluated by comparison to performances of previous approaches with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. After that, the previously developed hGA is modified to find schedules that are quality robust and/or stable in face of random machine breakdowns. Consequently, a two-stage hGA is proposed to generate the predictive schedule. Furthermore, the effectiveness of the proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods. Subsequently, the hGA is modified to consider FJSP when processing times of some operations are represented by or subjected to small-to-medium uncertainty. The work compares two genetic approaches to obtain predictive schedule, an approach based on expected processing times and an approach based on sampling technique. To determine the performance of the predictive schedules obtained by both approaches with respect to two types of robustness, an experimental study and Analysis of Variance (ANOVA) are conducted on a number of benchmark problems. en_US
dc.publisher Flexible Services and Manufacturing Journal en_US
dc.publisher International Journal of Production Economics en_US
dc.rights info:eu-repo/semantics/openAccess
dc.subject Scheduling en_US
dc.subject Job-Shop en_US
dc.subject Breakdown en_US
dc.subject genetic algorithm en_US
dc.title OPTIMIZING THE FLEXIBLE JOB-SHOP SCHEDULING PROBLEM USING HYBRIDIZED GENETIC ALGORITHMS en_US
dc.type info:eu-repo/semantics/doctoralThesis
dc.degree.discipline Mechanical and Manufacturing Engineering en_US
dc.contributor.examiningcommittee Peng, Qingjin (Mechanical and Manufacturing Engineering) Cai, Jun (Electrical and Computer Engineering) Tu, Yiliu (University of Calgery) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note February 2012 en_US


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