Application of Robust Optimization to the Sawmill Planning Problem

Álvarez, Pamela; Vera, Jorge

doi:10.1007/s10479-011-1002-4

Cited by 42 publications

(23 citation statements)

References 33 publications

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“…With a focus on sawmill operations optimization, Maturana, Pizani, and Vera (2010) and Alvarez and Vera (2011) highlight the need for reliable information in the context of robust optimization and heuristic techniques applications. The need to deal with information uncertainty has also been addressed in other contexts of forestry.…”

Section: General Methodological Approachmentioning

confidence: 99%

Log classification in the hardwood timber industry: method and value analysis

Gil

Frayret

2015

International Journal of Production Research

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My infinite gratitude is also to my research advisor, Mr. Jean-Marc Frayret, who not only believed in me since the beginning, but also welcomed me in his research team, and encouraged me to follow my research. His experience and great knowledge has given him a wide vision to find the right track every time I was clueless. After the long sessions we spend finding solutions to these and many other dilemmas, I realized that more than a good teacher, he is a great human being. I am proud of have working with him. All my acknowledgements goes also to Francis Fournier and Jean McDonald of FP Innovationsfor guiding this research and for providing us the technical information necessary to understand and solve the puzzle we had three years ago when this journey begin.Finally, I'd like to thank the VCO NSERC network for the financial support and for all the research and networking activities that I had the opportunity to be part of, which gave me the chance to have a better vision of the forest products industry and its challenges. v RÉSUMÉ Les industries avec différentes entrées, telles que : l'industrie des produits forestiers (FPI), l'industrie minière ou l'industrie du recyclage, doivent faire face à l'incertitude de matière primaire, ce qui affecte leur capacité à prévoir le rendement de sortie. Pour régler ce problème, les industries peuvent réduire l'incertitude à la source, ou de planifier les opérations en tenant compte de l'incertitude. Dans le FPI, la première approche est généralement utilisée. Par exemple, l'industrie du bois d'oeuvre a implémenté des technologies de transformation sophistiquées pour adapter le processus du sciage aux caractéristiques des billes en utilisant la technologie de numérisation pour obtenir des informations précises sur l'état des travaux en cours de fabrication.Une autre approche pour réduire l'incertitude est la classification de matière primaire. Certaines caractéristiques spécifiques peuvent être mesurées à l'entrée pour classer la matière primaire et en conséquence, augmenter la certitude des attentes de production dans chaque classe. Toutefois, si le processus implique les journaux, les minerais des mines ou des papiers recyclés, la ABSTRACTIndustries with variable inputs, such as the forest product industry (FPI), the mining industry or the recycling industry, must cope with material uncertainty, which affects their ability to predict output yields. To deal with this, one can either reduce uncertainty at the source, or plan operations taking uncertainty into account. In the FPI, the first approach is generally used. For instance, the softwood lumber industry has adopted sophisticated transformation technologies that adapt sawing patterns to the log characteristic using scanners technology to acquire accurate information about work-in-process status. Another approach to reduce uncertainty is input material classification. Specific characteristics can be measured to classify input material and therefore reduce uncertainty within each class. However, whether the process involv...

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Section: General Methodological Approachmentioning

confidence: 99%

Log classification in the hardwood timber industry: method and value analysis

Gil

Frayret

2015

International Journal of Production Research

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“…Bohle et al (2010) develop a static RO model using the budget uncertainty set approach for wine grape harvesting scheduling where productivity of the manual harvesting method (i.e., yield) is uncertain. Alvarez and Vera (2011) formulate a static RO model for a sawmill planning problem with yield uncertainty using the budget uncertainty set approach. Similarly, Varas et al (2014) formulate a static RO model for a sawmill planning problem under demand and raw material supply uncertainty using the budget uncertainty set approach.…”

Section: Ro: Supply Chain Applications Under Supply Uncertaintymentioning

confidence: 99%

Robust inventory control under demand and lead time uncertainty

Thorsen

Yao

2015

Ann Oper Res

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In this paper a general methodology is proposed based on robust optimization for an inventory control problem subject to uncertain demands and uncertain lead times. Several lead time uncertainty sets are proposed based on the budget uncertainty set, and a set based on the central limit theorem (CLT). Robust optimization models are developed for a periodic review, finite horizon inventory control problem subject to uncertain demands and uncertain lead times. We develop an approach based on Benders' decomposition to compute optimal robust (i.e., best worst-case) policy parameters. The proposed approach does not assume distributional knowledge, makes no assumption regarding order crossovers, and is tractable in a practical sense. Comparing the new approach to an epigraph reformulation method, we demonstrate that the epigraph reformulation approach is overly conservative even when costs are stationary. The approach is benchmarked against the sample average approximation (SAA) method. Computational results indicate that the approach provides more stable and robust solutions compared to SAA in terms of standard deviation and worst-case solution, especially when the realized distribution is different than the sampled distribution. Keywords robust optimization • inventory control • lead time uncertainty • demand uncertainty

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“…Caglayan et al [8] proponen una taxonomía actualizada para los modelos de optimización en esta área. Se clasifican estás problemáticas en programación de cosecha [9], riesgo [5], incertidumbre [10,11], cadena de suministro [12], selección de áreas a cosechar [13]. Además, de manera transversal, la mayor cantidad de estudios se concentra en la programación de cosecha, vinculados al apoyo en la planificación de mediano plazo.…”

Section: Introductionunclassified