BARIS TAN
Graduate School of Business
Koç University
btan@ku.edu.tr
A methodology that automatically generates the state space models of pull-controlled production systems is presented. The state space model
generated by the algorithm is then used to evaluate the performance of these systems. In addition to long run measures such as the production
rate, WIP levels, average backlog, fill rate, etc., the exact distribution of the number of parts produced in a short period of time, the distribution
of the time to produce a given number of parts, and the distribution of the cycle time are also derived. This methodology can be applied to a
wide range of discrete material flow production systems that can be modelled as Markov chains. The deterministic processing time model is
considered to explain the methodology in detail. The state space models of unreliable automated production lines controlled by the
kanban, constant WIP, Control Point Policy, Base Stock, and hybrids of these policies are generated and analyzed.
The Matlab codes that generate the state space of pull controlled production systems are available for research purposes. The algorithm is explained in detail in the above paper.