Finite Capacity Scheduling – Borrowing the Best Practices of Biological Control

Rules governing scheduling and sequencing have accumulated overtime, driven by the manufacturing firms quest for improved operations against key performance metrics. While lead time dictates the need to preserve a focus on manufacturing efficiency, these needs must be balanced with demand-driven requirements especially at the process level, based on the finite capacity that is true of real systems. Investigation into the processes of biological systems presents an intriguing way to investigate how autonomous decision making can be improved in industrial operation systems. There are a number of reasons for this, (i) biological systems are easy to conceptualise, (ii) they have been well characterised and (iii) the study of their operations has produced abundant genomic data currently available that has led to the construction of genomic-scale models of microbial metabolism. Most importantly though, protein synthesis for gene expression in biological systems is a highly autonomous process and it is this inherent ability to self regulate that is of acute interest to the industrial operations engineer. Increasing levels of autonomous decision making will be required when designing, planning and controlling the manufacturing process in order to respond effectively to changes in increasingly competitive market and service environments. This paper describes a framework for the development of an algorithm using biological control to aid in enabling autonomous decision making functionality in operations planning in manufacturing and service industry.