A genetic algorithm based optimisation procedure for a class of inventory control systems
Abstract
The paper describes a procedure for optimising the performance of an industrially designed inventory control system. This has the three classic control policies utilising sales, inventory and pipeline information to set the order rate so as to achieve a desired balance between capacity, demand and minimum associated stock level. A first step in optimisation is the selection of appropriate “benchmark” performance characteristics. Five are considered herein and include inventory recovery to “shock” demands; in built filtering capability; robustness to production leadtime variations; robustness to pipeline level information fidelity; and systems selectivity. A genetic algorithm for optimising system performance, via these five vectors is described. The optimum design parameters are presented for various vector weightings. This leads to a Decision Support System for the correct setting of the system controls under various operating scenarios. The paper focuses on a single supply chain interface, however the methodology is also applicable to complete supply chains.