February 29, 2020 Journal article Open Access

G.Geetha; T.Hemamalini

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    <subfield code="a">Job Shop, Parallel Machine, Scheduling, Ant Colonized, Taguchi, Processing time, Setup time</subfield>
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    <subfield code="u">Assistant professor, Department of Mathematics,  Government Arts College,Coimbatore-18.</subfield>
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    <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT)</subfield>
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    <subfield code="u">Research Scholar, Department of Mathematics,  Government Arts College,Coimbatore-18.</subfield>
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    <subfield code="a">Ant Colonized and Taguchi Parallel Scheduliing  with Sequence Independent Setup Time</subfield>
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    <subfield code="a">&lt;p&gt;The Job Shop Parallel Machine Scheduling (JSPMS) is a hybrid production system, and hence has received significant attention in the past few years. The JSPMS problem is a rationalization of the traditional job shop scheduling problem in computer science and operation research that permits to process operations on single machine out of a set of possible parallel machines. To maximize the job completion rate and minimize job completion time, a hybrid production system is necessary. With this objective, a novel meta-heuristic method is designed. This paper develops a scheduling method called, Ant Colonized and Taguchi Parallel Operation Scheduling (ACTPOS), for JSPMS, aimed to minimize job completion time and maximize job completion rate. The design of AC-TPOS method involves two different models, namely, Ant Colonized Parallel Machine Selection (ACPMS) model and Taguchi Parallel Operation Scheduling (TPOS) model. In ACPMS model, optimal selection of machine is done via operation being processed by parallel machines using local pheromone updating rule concentrating on the makespan time. In addition, the processing time and sequence-independent setup time are considered. Next, in TPOS model, optimal scheduling of operation is performed using Taguchi method concentrating on the makespan rate. Finally, the test results first show that our algorithm outperforms existing methods in terms of job completion rate, job completion time and computational complexity involved in scheduling operations.&lt;/p&gt;</subfield>
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