Design and testing of stand-specific bucking instructions for use on modern cut-to-length harvesters

This study addresses three important issues in tree bucking optimization in the context of cut-to-length harvesting. (1) Would the fit between the log demand and log output distributions be better if the price and/or demand matrices controlling the bucking decisions on modern cut-to-length harvesters were adjusted to the unique conditions of each individual stand? (2) In what ways can we generate stand and product specific price and demand matrices? (3) What alternatives do we have to measure the fit between the log demand and log output distributions, and what would be an ideal goodness-of-fit measure? Three iterative search systems were developed for seeking stand-specific price and demand matrix sets: (1) A fuzzy logic control system for calibrating the price matrix of one log product for one stand at a time (the stand-level one-product approach); (2) a genetic algorithm system for adjusting the price matrices of one log product in parallel for several stands (the forest-level one-product approach); and (3) a genetic algorithm system for dividing the overall demand matrix of each of the several log products into stand-specific sub-demands simultaneously for several stands and products (the forest-level multi-product approach). The stem material used for testing the performance of the stand-specific price and demand matrices against that of the reference matrices was comprised of 9 155 Norway spruce (Picea abies (L.) Karst.) sawlog stems gathered by harvesters from 15 mature spruce-dominated stands in southern Finland. The reference price and demand matrices were either direct copies or slightly modified versions of those used by two Finnish sawmilling companies. Two types of stand-specific bucking matrices were compiled for each log product. One was from the harvester-collected stem profiles and the other was from the pre-harvest inventory data. Four goodness-of-fit measures were analyzed for their appropriateness in determining the similarity between the log demand and log output distributions: (1) the apportionment degree (index), (2) the chi-square statistic, (3) Laspeyres quantity index, and (4) the price-weighted apportionment degree. The study confirmed that any improvement in the fit between the log demand and log output distributions can only be realized at the expense of log volumes produced. Stand-level pre-control of price matrices was found to be advantageous, provided the control is done with perfect stem data. Forest-level pre-control of price matrices resulted in no improvement in the cumulative apportionment degree. Cutting stands under the control of stand-specific demand matrices yielded a better total fit between the demand and output matrices at the forest level than was obtained by cutting each stand with non-stand-specific reference matrices. The theoretical and experimental analyses suggest that none of the three alternative goodness-of-fit measures clearly outperforms the traditional apportionment degree measure. Keywords: harvesting, tree bucking optimization, simulation, fuzzy control, genetic algorithms, goodness-of-fit

The Quest for Well-being in Growth Industries: Contexts, Research Design and Methodological Development

This Working Paper reports the background to the first stage of the ongoing research project, The Quest for Well-being in Growth Industries: A Collaborative Study in Finland and Scotland, conducted under the auspices of the Academy of Finland research programme The Future of Work and Well-being (2008-2011). This collaborative project provides national and transnational data, analysis and outputs. The study is being conducted in the Department of Management and Organisation, Hanken School of Economics, Finland, in collaboration with Glasgow Caledonian University, University of East London, Heriot-Watt University and Reading University, UK. The project examines policies and practices towards the enhancement of work-related well-being in growth industries, and contradictory pressures and tensions posed in this situation. The overall aim is to evaluate the development, implementation and use of work-related well-being policies in four selected growth industries. These sectors – electronics, care, finance and accounting, and tourism – have been selected on the basis of European Union and national forecasts, and demographic and socio-economic trends in employment. In this working paper we outline the background to the research study, the initial research plan, and how the survey of employers has been constructed. The working paper concludes with a brief discussion of general ongoing research issues arising in the project.