Process control with learning

Maqbool Dada, Richard Marcellus

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


We study the control of a production process which moves at a random time from an in-control state to an out-of-control state where an increased number of defective units is produced. After each unit is produced, a decision maker has three choices: continue production, invest in routine maintenance that restores the process to control, and invest in a more expensive learn maintenance that, in addition, may decrease the tendency of the process to go out of control. The optimal policy structure is shown to be of the control-limit type with the property that learning is not optimal if the tendency of the process to go out of control is small enough. If there is no opportunity to inspect the process's output, an optimal policy can be interpreted as a fixed production run. For this case an exact algorithm is developed. When the process's output is inspected, an optimal policy can be interpreted as a random production run. Approximation techniques are presented for this case. The fixed production run model is an alternative technique for determining production lot sizes. The random production run model is an alternative to traditional Shewhart process control.

Original languageEnglish (US)
Pages (from-to)323-336
Number of pages14
JournalOperations Research
Issue number2
StatePublished - Jan 1 1994
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research


Dive into the research topics of 'Process control with learning'. Together they form a unique fingerprint.

Cite this