Title: Facility layout design for multiple production scenarios in a dynamic environment
Authors: Krishna K. Krishnan, S. Hossein Cheraghi, Chandan N. Nayak
Addresses: Department of Industrial and Manufacturing Engineering, Wichita State University, Wichita, KS 67260-0035, USA. ' Department of Industrial and Manufacturing Engineering, Wichita State University, Wichita, KS 67260-0035, USA. ' Department of Industrial and Manufacturing Engineering, Wichita State University, Wichita, KS 67260-0035, USA
Abstract: Facility design approaches often focus on a single production demand scenario and develop a layout that minimises the Material Handling (MH) costs for that scenario. In a volatile and uncertain production environment, facility layouts should be designed while taking into consideration multiple demand scenarios to minimise the effects of uncertainty. In a risk-averse situation, where multiple probable demand scenarios exist, designing the layout for any one scenario could lead to high losses if any other scenario occurs. In such situations, all demand scenarios have to be considered and the facility should be designed to minimise the maximum loss (Minmax approach) due to MH costs in a manufacturing plant by considering all possible scenarios. This paper proposes a new facility layout design model to determine a compromise layout that can minimise the maximum loss in MH cost both for single and multiple periods. The model developed has been further modified to address minimisation of the total expected loss as well. The resulting mathematical models are solved to generate improved layouts using Genetic Algorithm (GA) approach. The proposed models are solved for single-period and multiperiod case studies. Results indicate that the proposed models generate compromise layouts which are efficient in reducing maximum possible loss and as well in minimising the total expected loss.
Keywords: uncertain demand; stochastic layout modelling; facility layout problem; multiple production scenarios; dynamic manufacturing; layout design; uncertainty; materials handling costs; genetic algorithms.
DOI: 10.1504/IJISE.2008.016740
International Journal of Industrial and Systems Engineering, 2008 Vol.3 No.2, pp.105 - 133
Published online: 21 Jan 2008 *
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