ABSTRACT

An aspatial mathematical model has been developed to simultaneously support log processing investment decisions with respect to processing scale, facility location and log procurement when data are scarce. A key design principle was to make the model suitable for use by industry. The objective function maximises gross margins per hour of log processing time, and the model accounts for potential processing efficiencies with larger-diameter logs. To support log procurement decisions, the model estimates mill-delivered log costs at which a log procurement officer should be indifferent between purchasing alternative log types. The utility of the model is demonstrated with an application to rotary veneer processing of hardwood logs in subtropical eastern Australia. Complex interactions between processing scale, facility location and log procurement strategies were revealed by substantial differences in gross margins between modelled scenarios. Log procurement decisions were found to have the greatest potential impact on gross margins, followed by facility location and processing scale. The model highlighted that substantially higher returns can be earned from optimal log procurement strategies relative to approaches that either minimise log costs, maximise product recovery or do not differentiate between log types and simply utilise all available log volume.

摘要

已经开发了一种非空间数学模型，以在数据稀缺时同时支持有关加工规模、设施位置和原木采购的原木加工投资决策。一个关键的设计原则是使模型适合行业使用。目标函数使每小时原木处理时间的毛利率最大化，并且该模型考虑了大直径原木的潜在处理效率。为了支持原木采购决策，该模型估算了工厂交付的原木成本，原木采购官员在购买替代原木类型之间应该无动于衷。该模型的实用性通过在澳大利亚东部亚热带地区的硬木原木旋切加工中得到证明。加工规模之间的复杂相互作用，设施位置和原木采购策略通过模拟情景之间毛利率的显着差异来揭示。原木采购决策对毛利率的潜在影响最大，其次是设施位置和加工规模。该模型强调，相对于最小化原木成本、最大化产品回收或不区分原木类型并简单地利用所有可用原木量的方法，最佳原木采购策略可以获得更高的回报。