Detailed scheduling of forest harvesting at the operational level incorporating decisions on multiple machine assignment

ABSTRACT

It is crucial to efficiently schedule harvesting activities in order to reduce the delivered cost of logs. Mathematical models have been used to optimize the harvest scheduling at the operational level. However, in the existing literature, the number of machines assigned for each activity at each cut block was not considered as a decision variable. Also, the impact of the slope of cut blocks on the precedence relationship between harvesting activities was not considered in tprevious studies. In this work, a mathematical model is developed with the possibility of assigning multiple machines for the same harvest activity at each cut block, considering the precedence relationship between activities based on the slope of cut blocks in order to minimize the total cost of harvesting. This work is an extension of our previous work on detailed scheduling of harvesting. The model is applied to the harvesting operations of a large forest company in Coastal British Columbia, Canada. The model’s result for operating cost is only 3.3% higher than the lowest possible operating cost. Only one machine has an idle time. For the same case study, the total cost of the developed model is about 34% lower than that of the previous model.

KEYWORDS:

• Mixed-integer linear programming
• operational harvest scheduling
• precedence relationship
• harvesting costs
• machine assignment

Acknowledgements

The authors would like to thank the 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 the Science and Engineering Research Board of India (award #6707) for their financial support in conducting this research. We also want to thank the forest company for assisting us in gathering data and validating our model and results.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The work was supported by the Mitacs [MITACS IT12394]; NSERC [RGPIN-2019-04563]; University of British Columbia and the Science and Engineering Research Board of India [#6707]