A Parallelized Variable Fixing Process for Solving Multistage Stochastic Programs with Progressive Hedging
Autor: | Weikko S. Jaross, Sándor F. Tóth, Andrés Weintraub, Martin B. Bagaram |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Mathematical optimization 021103 operations research 020901 industrial engineering & automation Article Subject TS155-194 Computer science 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Production management. Operations management |
Zdroj: | Advances in Operations Research, Vol 2020 (2020) |
ISSN: | 1687-9155 1687-9147 |
Popis: | Long time horizons, typical of forest management, make planning more difficult due to added exposure to climate uncertainty. Current methods for stochastic programming limit the incorporation of climate uncertainty in forest management planning. To account for climate uncertainty in forest harvest scheduling, we discretize the potential distribution of forest growth under different climate scenarios and solve the resulting stochastic mixed integer program. Increasing the number of scenarios allows for a better approximation of the entire probability space of future forest growth but at a computational expense. To address this shortcoming, we propose a new heuristic algorithm designed to work well with multistage stochastic harvest-scheduling problems. Starting from the root-node of the scenario tree that represents the discretized probability space, our progressive hedging algorithm sequentially fixes the values of decision variables associated with scenarios that share the same path up to a given node. Once all variables from a node are fixed, the problem can be decomposed into subproblems that can be solved independently. We tested the algorithm performance on six forests considering different numbers of scenarios. The results showed that our algorithm performed well when the number of scenarios was large. |
Databáze: | OpenAIRE |
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