No 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.

No abstract

Log logistics include sorting, processing, and transporting of logs from their place of harvest to demand locations. These activities account for a significant portion of the total log procurement costs; therefore, attempts were made in previous studies to optimize some aspects of log logistics. However, operational details, such as sorting decisions, truck compatibility requirements, and social objectives, are often disregarded in the optimization literature. Incorporating these details into the model makes the results more realistic and applicable. To address these gaps, a bi-objective mixed-integer programming model is developed in this paper to optimize log logistics. The first objective is to minimize total logistics costs, and the second objective is to provide a balanced workload for trucking contractors. The bi-objective model is solved using the goal programming approach. The model is applied to log logistics of a large Canadian forest company, where trucking contractors use heterogeneous fleet of trucks to carry various log sorts from cutblocks to sort yards for sorting. The planning horizon is 4 weeks with daily decisions. The goal programming model generates balanced workloads for the contractors with less than 0.4% increase in total costs compared to the single objective model where only the total cost is minimized.