Asee peer logo

Minimum Inventory Variability Dispatching Policies (Mivp)

Download Paper |


2000 Annual Conference


St. Louis, Missouri

Publication Date

June 18, 2000

Start Date

June 18, 2000

End Date

June 21, 2000



Page Count


Page Numbers

5.453.1 - 5.453.15



Permanent URL

Download Count


Request a correction

Paper Authors

author page

Jose'-Job Flores-Godoy

author page

Frank C. Hoppensteadt

author page

Donald W. Collins

author page

Kostas Tsakalis

Download Paper |

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Session 2563

Minimum Inventory Variability Dispatching Policies - MIVP

Donald Collins, Ph.D., Manufacturing Engineering Technology, José-Job Flores-Godoy, M.S., Electrical Engineering Frank Hoppensteadt, Ph.D., Math and Electrical Engineering, Kostas Tsakalis, Ph.D., Electrical Engineering Arizona State University


This paper illustrates the use of discrete event stochastic simulation modeling to compare two scheduling (dispatching) policies for machines in a factory when a machine becomes available for processing. The two policies are first-in-first-out (FIFO) and Minimum Inventory Variability Policies (MIVP), both control the items in the queue (buffers) in front of the machine or resource 8,9,10,11. The simulation model is run with FIFO for each queue for 100 days to establish a baseline set of data. This baseline cycle time and work-in-progress (WIP) data are collected for comparison to MIVP. The only change between the model runs is that the queues in the model are switched to run the rule set of MIVP.

With discrete event simulation modeling, the user can play “what if” scenarios without expended a lot of capital 4,5,8,9,10,11,19,20. The results from simulation give the user an additional input in making decisions. Examples of such a simulator use include the analysis of machine utilization, queue statistics, mean cycle time and mean WIP and production throughput, etc. This analysis can serve to push the bottleneck capacity to its limit, setup and test scheduling rules and preventive maintenance schedules, and determine personnel (operator) availability requirements. Thus, a good simulator allows for the investigation of complex “what-if” scenarios at a minimal cost, high speed, and without disturbing the normal production.

The System Model

The following figure and specification was taken from a test-bed designed by Karl Kempf, Manufacturing Systems Principal Scientist, of the INTEL Corporation. This test-bed is an example of a very small section in the Semiconductor FAB and is referred to as a Mini-FAB 6,14,16 .

The Mini-FAB included two products and test wafers with their process flows (production recipes) of six steps utilizing three different machine sets. There was one re-entrant step at each machine group, steps 4, 5 and 6. The machine groups emulate (in a small scale) Diffusion-C1 (2

Flores-Godoy, J., & Hoppensteadt, F. C., & Collins, D. W., & Tsakalis, K. (2000, June), Minimum Inventory Variability Dispatching Policies (Mivp) Paper presented at 2000 Annual Conference, St. Louis, Missouri. 10.18260/1-2--8567

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2000 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015