Loading...

Production planning of reconfigurable manufacturing systems with stochastic demands using Tabu search

Abbasi, M ; Sharif University of Technology | 2009

884 Viewed
  1. Type of Document: Article
  2. DOI: 10.1504/IJMTM.2009.023782
  3. Publisher: 2009
  4. Abstract:
  5. In the new competitive dynamic market, manufacturing success and survival are becoming more and more difficult to ensure. In other words, getting the right product with low cost and high quality is not the only key to success. New requirements such as production responsiveness and flexibility should be considered. Reconfigurable Manufacturing System. (RMS) is a new paradigm that enables manufacturing systems to achieve rapid response to market demand. The effectiveness of an RMS depends on implementing key characteristics and capabilities of RMS in system design stage and benefiting from them in utilisation stage. In this paper, we introduced a methodology to adjust rapidly and productively scalable production capacities and the functionality of system to market demands. It is supposed that arrival orders follow Poisson distribution and they are missed, if they are not available. According to these assumptions, a Mixed Integer Non-linear Programming (MINLP) model is developed to determine optimum sequence of production tasks, corresponding configurations and batch sizes. A Tabu search based procedure is used to solve the model. Finally a numerical example is used to illustrate the procedure. Copyright © 2009 Inderscience Enterprises Ltd
  6. Keywords:
  7. Production planning ; Reconfigurable manufacturing system ; RMS ; Stochastic demands ; Tabu search ; Batch sizes ; Competitive dynamics ; High qualities ; Key characteristics ; Low costs ; Manufacturing systems ; Market demands ; Mixed-integer non-linear programming ; Numerical examples ; Optimum sequences ; Production planning ; Rapid response ; Reconfigurable manufacturing system ; RMS ; Scalable productions ; System designs ; Computer aided manufacturing ; Instrument scales ; Integer programming ; Linearization ; Manufacture ; Planning ; Poisson distribution ; Production control ; Production engineering ; Project management ; Random processes ; Stochastic control systems
  8. Source: International Journal of Manufacturing Technology and Management ; Volume 17, Issue 1-2 , 2009 , Pages 125-148 ; 13682148 (ISSN)
  9. URL: https://www.inderscience.com/info/inarticle.php?artid=23782