ABSTRACT
The competitiveness of forest companies is strongly affected by the costs associated with getting the raw material to the mills. As harvesting costs contribute significantly to this cost, mathematical programming models were developed to optimize the scheduling of harvest activities within and between cut blocks to reduce the overall cost. However, the precedence relationship between harvesting activities occurring concurrently across multiple cut blocks has not been considered in the existing literature. In this paper, a mixed-integer linear programming model is developed to optimize the scheduling of harvesting activities, considering the precedence relationship among harvesting activities. The objective of the model is to minimize the total costs. The model determines the start time and end time of each harvesting activity at each cut block, considering the movement time of machines between cut blocks. The model is applied to the case of a large forest company in British Columbia, Canada. The model’s harvesting cost is only 1.37% higher than the lowest possible harvesting cost, and only 3 assigned machines have an idle time. The detailed harvesting schedule is generated based on the start time, the end time, and the operating time for each activity at each cut block.
Acknowledgements
The authors are grateful for the financial support provided by Natural Sciences and Engineering Research Council of Canada (NSERC RGPIN-2019-04563); Mitacs and the forest company (MITACS IT12394); and The University of British Columbia and Science and Engineering Research Board of India (award #6707) to conduct this research. We also thank the forest company for helping us to obtain data and validate our model and results.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1. AAC is the maximum average level of timber harvest permitted for forest management areas, it represents a harvest level that balances environmental, economic and social considerations (Government of BC Citation2021)