Multi-objective stochastic simulation-based optimisation applied to supply chain planning

    Liana Napalkova Info
    Galina Merkuryeva Info

Abstract

The paper discusses the optimisation of complex management processes, which allows the reduction of investment costs by setting the optimal balance between product demand and supply. The systematisation of existing methods and algorithms that are used to optimise complex processes by linking stochastic discrete-event simulation and multi-objective optimisation is given. The two-phase optimisation method is developed based on hybrid combination of compromise programming, evolutionary computation and response surface-based methods. Approbation of the proposed method is performed on the multi-echelon supply chain planning problem that is widely distributed in industry and its solution plays a vital role in increasing the competitiveness of a company. Three scenarios are implemented to optimise supply chain tactical planning processes at the chemical manufacturing company based on using different optimisation methods and software. The numerical results prove the competitive advantages of the developed two-phase optimisation method.

Keywords:

simulation optimisation, multi-objective evolutionary computation, multi-echelon supply chain, cyclic planning

How to Cite

Napalkova, L., & Merkuryeva, G. (2012). Multi-objective stochastic simulation-based optimisation applied to supply chain planning. Technological and Economic Development of Economy, 18(1), 132-148. https://doi.org/10.3846/20294913.2012.661190

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April 10, 2012
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2012-04-10

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How to Cite

Napalkova, L., & Merkuryeva, G. (2012). Multi-objective stochastic simulation-based optimisation applied to supply chain planning. Technological and Economic Development of Economy, 18(1), 132-148. https://doi.org/10.3846/20294913.2012.661190

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