Loading...

Production planning and performance optimization of reconfigurable manufacturing systems using genetic algorithm

Abbasi, M ; Sharif University of Technology

1708 Viewed
  1. Type of Document: Article
  2. DOI: 10.1007/s00170-010-2914-x
  3. Abstract:
  4. To stay competitive in the new dynamic market having large fluctuations in product demand, manufacturing companies must use systems that not only produce their goods with high productivity but also allow for rapid response to market changes. Reconfigurable manufacturing system (RMS) is a new paradigm that enables manufacturing systems to respond quickly and cost effectively to market demand. In other words, RMS is a system designed from the outset, for rapid changes in both hardware and software components, in order to quickly adjust its production capacity to fluctuations in market demand and adapt its functionality to new products. The effectiveness of an RMS depends on implementing its key characteristics and capabilities in the design as well as utilization stage. This paper focuses on the utilization stage of an RMS and introduces a methodology to effectively adjust scalable production capacities and the system functionalities to market demands. It is supposed that arrival orders of product families follow the Poisson distribution. The orders are lost if they are not met immediately. Considering these assumptions, a mixed integer nonlinear programming model is developed to determine optimum sequence of production tasks, corresponding configurations, and batch sizes. A genetic algorithm-based procedure is used to solve the model. The model is also applied to make decision on how to improve the performance of an RMS. Since there is no practical RMS, a numerical example is used to validate the results of the proposed model and its solution procedure
  5. Keywords:
  6. Genetic algorithm ; Production planning ; Reconfigurable manufacturing system ; Stochastic demand ; Batch sizes ; Dynamic market ; Hardware and software components ; High productivity ; Key characteristics ; Manufacturing companies ; Manufacturing system ; Market changes ; Market demand ; Mixed integer nonlinear programming models ; New product ; Numerical example ; Optimum sequences ; Performance optimizations ; Product demand ; Product families ; Production capacity ; Rapid changes ; Rapid response ; Scalable production ; Solution procedure ; System functionality ; Computer aided manufacturing ; Genetic algorithms ; Integer programming ; Optimization ; Planning ; Poisson distribution ; Production control ; Production engineering ; Project management ; Stochastic systems ; Systems analysis ; Commerce
  7. Source: International Journal of Advanced Manufacturing Technology ; Volume 54, Issue 1-4 , 2011 , Pages 373-392 ; 02683768 (ISSN)
  8. URL: http://link.springer.com/article/10.1007%2Fs00170-010-2914-x