A simulation-based scheduler for flexible flowlines

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A flexible flowline is a generalized production model employed in a wide variety of manufacturing assembly applications to produce a range of products in small batch quanitites. The MRP systems, which are commonly used in such environments, lack the detail to carry out daily scheduling. Recently, finite capacity planning tools have been suggested as a means of generating short-term production schedules. These tools employ techniques that take a global view of the problem and are based on static ‘snapshots’ of the manufacturing system. However, they ignore any inherent dynamics and therefore are inadequate in managing disruptions that can be very costly in terms of the inability to meet throughput requirements and prior delivery commitments. A simulation-based scheduler, on the contrary, is effective in accommodating the dynamic and/or stochastic nature of the system in arriving at a sheduling solution. In this paper, we introduce a simulation-based scheduler which is used to study a general class of parametric dispatching rules for flexible flowlines, under a variety of shop loading conditions. A scaled model of an actual manufacturing system is used to study the apparent tardiness cost based scheduling rule. The simulation-based scheduler has the facility to model a variety of manufacturing scenarios ranging from PCB lines to complex flexible flowlines. The scheduler is designed on pragmatic considerations and provides a test platform for studying static as well as dynamic scheduling rules for real manufacturing environments. It serves as an expedient to evaluating the practical effectiveness of scheduling rules. Additionally, it can also be used to develop detailed short-term capacity plans that form the basis for finite capacity scheduling.

Original languageEnglish (US)
Pages (from-to)321-344
Number of pages24
JournalInternational Journal of Production Research
Issue number2
StatePublished - Feb 1994

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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