Determining optimal primary sawing and ripping machine settings in the wood manufacturing chain

Abstract

for wood manufacturers around the world, the single biggest cost factor is known to be its raw material. Maximum volume recovery of this raw material is, therefore, of key importance for the industry. The wood products industry consists of several interrelated manufacturing steps for converting trees into logs and logs into finished lumber. Each operation usually optimises its functionality in isolation from the preceding and following operations. It is a well documented fact that the optimisation of decisions through the whole chain of operations is considerably more profitable than the optimisation of individual operations. The objective of this study was to determine the optimal machine settings for two interrelated operations, namely the sawing and ripping operations which have tradition‑ ally been optimised individually. A model, having two decision variables, was developed which aims to satisfy market demand at a minimal cost. The first decision was how to saw the log supply into different thicknesses by choosing specific sawing patterns. The second was to decide on a rip saw's priority value settings, which determines how the products from the primary sawing operation are ripped into products of a certain thickness and width. The techniques used to determine the machine settings included static simulation with the SIMSAW software to represent the sawing operation and mixed integer programming to model the ripping operation. A metaheuristic, namely the Population Based Incremental Learning algorithm, was used to link the simulation and mixed integer models and to determine the optimal settings for the combined process. The model's objective function was to minimise the cost of production. This cost included the raw material waste cost and the over or under production cost. The over production cost included the stock keeping costs and the under production cost was estimated as the buy‑in cost of under supplied products from another wood supplier. The model performed well against current decision software available, namely the Sawmill Production Planning System package, which combines simulation and mixed integer programming techniques to maximise profit. The model added further value by modelling and determining the ripping priority settings in addition to the primary sawing patterns.