Scheduling of forest harvesting operations on multiple cut blocks using multi-task machines

ABSTRACT

The modernization of forest harvesting operations has significantly increased the cost of machine ownership and has turned forest harvesting into a capital-intensive process. To increase productivity and profitability, some companies have acquired multi-task harvesting machines. While many previous papers focused on optimizing the harvest scheduling to reduce the costs of harvesting, the assignment of multi-task machines was not considered in their models. In this work, an optimization model is developed for the detailed scheduling of harvesting activities on multiple cut blocks using multi-task machines. This model is a continuation of previous work on detailed harvest scheduling. It prescribes the start time and the end time of operations of each machine at each cut block, the number of machines to be assigned for each harvesting activity at each cut block, the cut block that the machine should move to after completing its operation at a cut block, and the type of activity it should perform. It is applied to a case study of a forest company in Canada. According to the results, the total harvesting cost decreased by Can$ 25,000 when multi-task machines were used compared to exclusive machines, due to less machine movement and the need for fewer machines.

KEYWORDS:

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

Acknowledgements

This research was funded by 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 Science and Engineering Research Board of India (award #6707). The authors are grateful for the research funding and appreciate the help from the forest company to obtain data and validate the model and results.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

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