Multiple parts process planning in serial-parallel flexible flow lines: part I—process plan modeling framework

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• 作者：F. Musharavati ; A. M. S. Hamouda
• 关键词：Process planning ; Serial ; parallel flexible flow lines ; Multiple parts process planning (MPPP) ; Modeling ; Simulated annealing (SA) ; Simulated annealing with a mutation operator
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：78
• 期：1-4
• 页码：115-137
• 全文大小：1,267 KB
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• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering
  Production and Logistics
  Mechanical Engineering
  Computer-Aided Engineering and Design
  
• 出版者：Springer London
• ISSN：1433-3015

文摘

In recent years, integrated process planning and scheduling models have been proposed as solutions that can bridge the gap between practical process planning and production scheduling. However, most structures of these models have been algorithm-based and hence may not be very useful when a problem contains process and operational aspects that are difficult to capture in an algorithm template. In dynamic manufacturing environments, examples of such aspects include process and operational flexibilities that enable manufacturers to cope with unexpected variations in production and product mix. Appropriate process planning models that take cognizance of such aspects can be proven more useful to human process planners. In this paper, an innovative multiple parts process planning (MPPP) model for solving process planning problems with process and operational flexibilities is introduced. This model strikes a balance between process- and operations-related meta-data in a bid to capture process and operational flexibilities in the search for an optimal process planning solution. Merits of this model are discussed with reference to the operations of a typical serial-parallel flexible flow line. An illustrative example of the modeling framework is outlined. In seeking a feasible solution, a relative comparative analysis is carried out between; (a) a simulated annealing (SA) algorithm and (b) a simulated annealing algorithm that implements a mutation operator. Results show that the SA algorithm with a mutation operator outperforms the SA algorithm without a mutation operator.