Graduate Thesis Or Dissertation

 

Development and comparison of stand-level bucking optimization methods Public Deposited

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  • Heuristics based on Monte-Carlo Integer Programming (MCIP) and Tabu Search (TS) techniques were developed for generating easily implementable bucking rules that are applicable to entire stands (as opposed to individual stem classes), and for selecting the rule-set that provides the best feasible solution (given log prices and market constraints). A rule-set correspond to the definition of the attributes (e.g., minimum end-diameter and acceptable grades) that the different log-types must satisfy to be acceptable for bucking from a stem, and its role is to provide guidelines for the bucking activities. The MCIP method consists of randomly generating the bucking rules, with the log-types' attributes subjected to certain limitations. To generate candidate solutions efficiently, several Monte-Carlo simulation controls were analyzed. Such controls include the stopping criteria, the search region, and the probability distribution used for generating bucking rules. The TS method corresponds to a search process, with restrictions imposed on the export logs' attributes for guiding the search. The imposition of tabu-restrictions on candidate bucking rules helps the search process to avoid becoming trapped at locally optimal solutions --a condition frequently encountered in patterned searches. Refinements of Tabu Search used in this study are: [1] parallel tabu-lists of different time-span for the different tabu-restrictions, and [2] an aspiration criterion consisting of improving upon the best feasible solution. Both rule-based models (TS and MCIP) were applied to a set of radiata pine (Pinus radiata, D.Don) stands, considering different market constraints and price sets. The TS and MCIP results were compared to solutions obtained by an iterative two-stage Linear Programming / Shortest Path model and an integer programming (IP) model that were designed for selecting the best set of stem-specific bucking patterns. The rule-based approaches lead to bucking outcomes that are reasonably close to the optimal solutions defined by the IP model. The comparatively sophisticated TS technique, however, provided solutions to the sample problems that are only marginally better than those obtained using a simple MCIP approach.
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