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dc.contributor.authorKang, Parminderen
dc.contributor.authorKhalil, R. A. (Riham A.)en
dc.contributor.authorStockton, Daviden
dc.identifier.citationKang, P. S., Khalil, R. and Stockton, D. (2013) Job Sequence Optimisation Using Combinatorial Evolutionary Approach in High Variety/Low Volume Manufacturing Environment. International Journal of Scientific and Engineering Research, 4 (6) pp. 2145-2150en
dc.description.abstractToday’s manufacturing industry is been through unprecedented degree of change in terms of high variety and low volume, high value, global competition, shortened product life cycles, change is management strategies, increasing quality requirements and customer expectations and increased process complexity. As a result, in recent years organisations have adopted towards optimisation of the manufacturing operations in order to stay in competition, sustain their operational performance and maximise their economic benefits. This paper exemplifies a novel approach for development of combinatorial optimisation framework using evolutionary algorithms and Discrete Event Simulation modelling to determine the optimal job sequence by taking in account multiple organisational constraints. Simulation model used in this research represents the working area at Perkins Engines Limited. This may enable organisations to deal with such a highly diversified product portfolio without jeopardizing the benefits of an efficient flow-production. In the proposed methodology, two objectives used are manufacturing lead time and total inventory holding cost to measure the effectiveness of proposed solution. However, chosen objectives can be changed according to the organisational priorities.en
dc.publisherInternational Journal of Scientific & Engineering Researchen
dc.subjectCombinatorial Optimisationen
dc.subjectJob Sequencingen
dc.subjectLean Manufacturingen
dc.subjectProcess Improvementen
dc.subjectSimulation Modellingen
dc.subjectProcess Synchronisationen
dc.subjectGenetic Algorithmsen
dc.titleJob Sequence Optimisation Using Combinatorial Evolutionary Approach in High Variety/Low Volume Manufacturing Environmenten

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