@article {Yoshimoto:1994::365,
title = "A New Heuristic To Solve Spatially Constrained Long-Term Harvest Scheduling Problems",
journal = "",
parent_itemid = "",
publishercode ="",
year = "1994",
volume = "40",
number = "3",
publication date ="1994-08-01T00:00:00",
pages = "365-396",
itemtype = "ARTICLE",
url = "https://www.ingentaconnect.com/content/saf/fs/1994/00000040/00000003/art00003",
author = "Yoshimoto, Atsushi and Brodie, J. Douglas and Sessions, John",
abstract = "Because of capability limitations of the integer programming solution technique, a new heuristic algorithm was developed to solve spatially constrained long-term harvest scheduling problems. The proposed algorithm can handle multiple harvesting for each harvest unit over a long time horizon. The heuristic utilizes random ordering heuristic optimization and the PATH algorithm adapted from stand level optimization. Employing the proposed algorithm, a harvest scheduling system was constructed. The performance of the proposed algorithm is presented compared to the branch-and-bound algorithm in terms of the computational time as well as the objective value. Using two example forests, solutions by the proposed algorithm are stable in terms of the objective value and have harvest flow fluctuation much less than 3%. For short-term problems, solutions by the proposed algorithm tend to be optimal. For those problems, for which an optimal solution is found by the branch-and-bound algorithm, the solution can produce an objective value with deviation less than 2% from the optimum. The proposed algorithm yields better solutions for long-term problems than the branch-and-bound algorithm with the 1,000,000 limited number of iterations, and the lower bound derived by the proposed algorithm. Computational results reveal that as the time horizon increases, the proposed algorithm significantly and increasingly outperforms the "limited" branch-and-bound algorithm in terms of required computational time. The advantage of the proposed algorithm results from partitioning the problem into subproblems period by period using the PATH algorithm, and defining the objective function of the subproblem by minimizing absolute infeasibility on harvest flow constraints at each period under a two-period sequential feasibility condition. For. Sci. 40(3):365-396.",
}