On The Work-In-Process Control Of Production Networks

The effectiveness of evolutionary optimized fuzzy controllers for production scheduling has been proven in the past. The objective of the control/scheduling task in this context, is to continuously adjust the production rate in a way that: 1) satisfies the demand for final products, 2) keeps the inventory as low as possible. The evolutionary optimization identifies fuzzy control solutions which simultaneously satisfy those restrictions. The important question here is: How robust and generic is the outcome of the evolutionary process? In this paper we face this question by testing the evolutionary tuned fuzzy controllers under several demand patterns, as the actual demand might be different from those used for evolution\optimization. Extensive simulations of a supervisory controller identify the performance of the evolutionary-fuzzy strategy in comparison to a pure knowledge based one.