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Planning cost-effective operational forest inventories.

Santeri Karppinen1, Liviu Ene2, Lovisa Engberg Sundström2

  • 1Department of Mathematics and Statistics, University of Jyvaskyla, Seminaarinkatu 15, Jyvaskyla 40014, Finland.

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Summary
This summary is machine-generated.

This study optimizes forestry inventory decisions by balancing accuracy costs with harvest scheduling needs. A novel approximation method helps determine the best inventory investment for operational forestry management.

Keywords:
Bayesian modelingdecision makingforestryquadratic assignment problemschedulingvalue of information

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Area of Science:

  • Operational Research
  • Forestry Science
  • Bayesian Decision Theory

Background:

  • Operational forestry involves complex decisions balancing resource estimation with harvesting logistics.
  • Pre-harvest inventory accuracy impacts timber volume estimation and subsequent scheduling, but higher accuracy incurs greater costs.
  • A Bayesian two-stage decision framework is suitable for optimizing these interdependent operational choices.

Purpose of the Study:

  • To develop a practical method for optimizing inventory accuracy decisions in operational forestry under budget constraints.
  • To formulate the inventory selection as a maximization of posterior value, considering harvesting schedules.
  • To estimate the economic value of information gained from different inventory accuracies.

Main Methods:

  • Formulated the outer stage problem as maximizing posterior value under a budget constraint.
  • Addressed the computationally intractable inner stage (NP-hard generalized quadratic assignment problem) using approximation.
  • Combined Monte Carlo sampling with a greedy, randomized method for solving the binary optimization problem.
  • Applied the method to a dataset of 100 Swedish forest tracts.

Main Results:

  • Derived optimal inventory decisions for various budget levels across the dataset.
  • Estimated the value of information obtainable from different pre-harvest inventory accuracies.
  • Demonstrated a practical approach to a complex Bayesian decision problem in forestry.

Conclusions:

  • The developed approximation method provides a viable solution for optimizing inventory decisions in operational forestry.
  • Balancing inventory accuracy and cost is crucial for efficient timber harvesting and resource management.
  • The study quantifies the value of information, aiding strategic investment in forest inventories.