Multiple Period Combined Optimization Approach to Forest Production Planning

Maness, Thomas C.; Norton, Scott E.

doi:10.1080/028275802320435478

Cited by 36 publications

(14 citation statements)

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“…However, the model could be improved if a multi-period framework is considered. In this case, temporal changes in the model inputs can be evaluated (Maness and Norton 2002), and orders with different completion dates could be combined in the same planning process and therefore take advantage of the variety of products that a cutting pattern can produce. In other words, a better use of the patterns diversity could be observed.…”

Section: Discussionmentioning

confidence: 99%

“…Different optimization models have been built to deal with the cutting pattern problem as described, and linear programming (LP) has been the most common technique (Maness and Adams 1991, Maness and Norton 2002, Todoroki and Rönnqvist 2002. Although the problem has been formulated in literature in a similar way, in some cases it has been combined with bucking decisions Adams 1991, Maness andNorton 2002) and timber transfer decisions among plants in a supply chain context (Singer and Donoso 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Although the problem has been formulated in literature in a similar way, in some cases it has been combined with bucking decisions Adams 1991, Maness andNorton 2002) and timber transfer decisions among plants in a supply chain context (Singer and Donoso 2007). In Todoroki and Rönnqvist (2002), a sawing optimization model is implemented in a sawing simulator hence the optimal cutting pattern is determined on a log-by-log basis; an approach that does not find the optimal log-pattern mix that best satisfies the customer demand.…”

Section: Introductionmentioning

confidence: 99%

“…In Todoroki and Rönnqvist (2002), a sawing optimization model is implemented in a sawing simulator hence the optimal cutting pattern is determined on a log-by-log basis; an approach that does not find the optimal log-pattern mix that best satisfies the customer demand. Multi-period models (Maness and Norton 2002, Singer and Donoso 2007, Maturana et al 2010 have also been used with the possibility of handling inventory, of either logs or lumber, to add more flexibility. Maturana et al (2010) compare a sawing optimization model with a heuristic that emulates the decision behavior of the sawmill scheduler.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of optimization models for lumber production planning

Vergara¹,

Palma

Sepúlveda³

2015

Bosque (Valdivia)

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SUMMARYThe performance of sawmills is strongly dependent on how logs are sawn into lumber in order to satisfy the customer demands. To do this, sawmill managers have to decide which cutting patterns have to be applied to logs of different dimensions. Optimization models have been proposed to assist decision makers in this process, but only the profit maximization and the cost minimization of the decisions have been considered as the models objective. In this paper, a linear optimization model was formulated to address lumber production planning and applied to a real problem. The current decisions at sawmills were compared with five different objective functions: the two previously mentioned plus waste minimization, log number minimization and production time minimization. Only profit maximization and waste minimization models reported positive economic returns. Although the current decision at sawmills also reported a positive economic return, the same economic result was obtained with significantly fewer resources using the waste minimization model. The effects of the different objectives on the production indicators were discussed.Key words: linear programming, cutting patterns, sawmill production. RESUMENEl desempeño de un aserradero depende principalmente de cómo las trozas son aserradas para producir las piezas de madera demandas por los clientes. Para hacer esto, el planificador de la producción debe decidir qué patrones de corte deben ser aplicados a trozas de distintos tamaños. Los modelos de optimización han ayudado a tomar estas decisiones, pero solo se han considerado como objetivo de estos modelos la maximización de beneficios y la minimización de costos. En este artículo se formula un modelo de planificación de la producción de aserraderos, se aplica a un problema real, y se comparan las decisiones del planificador con las decisiones generadas por distintas funciones objetivo usadas en el proceso de optimización: las dos ya mencionadas además de la minimización de excedentes, minimización del número de trozas y minimización del tiempo de producción. Sólo los modelos de maximización de beneficio y minimización de excedentes mostraron retornos positivos. Aunque la solución actual del aserradero también mostró un retorno económico positivo, el mismo retorno fue obtenido con significativamente menos recursos, usando el modelo de minimización de excedentes. El efecto de diferentes funciones objetivo en los indicadores de producción es discutido.Palabras clave: programación lineal, patrones de corte, producción en aserraderos.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparison of optimization models for lumber production planning

Vergara¹,

Palma

Sepúlveda³

2015

Bosque (Valdivia)

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“…(1) deterministic optimization models where the average process yields are taken into account; (2) combined optimization type solutions linked to real-time simulation sub systems (Maness and Adams, 1991;Mendoza et al, 1991;Maness and Norton, 2002). In Gaudreault et al (2009), a deterministic LP model was proposed for sawmill production planning by considering the expected values of random process yields.…”

Section: Introductionmentioning

confidence: 98%

Production planning with uncertainty in the quality of raw materials: a case in sawmills

Zanjani

Nourelfath

Ait-Kadi

2011

Journal of the Operational Research Society

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Motivated by sawmill production planning, this paper investigates multi-period, multi-product (MPMP) production planning in a manufacturing environment with non-homogeneous raw materials, and consequently random process yields. A two-stage stochastic program with recourse is proposed to address the problem. The random yields are modelled as scenarios with stationary probability distributions during the planning horizon. The solution methodology is based on the sample average approximation (SAA) scheme. The stochastic sawmill production planning model is validated through the Monte Carlo simulation. The computational results for a real medium capacity sawmill highlight the significance of using the stochastic model as a viable tool for production planning instead of the mean-value deterministic model, which is a traditional production planning tool in many sawmills.

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Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

2023

Studies in Computational Intelligence

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Ce document constitue la conclusion d'un long projet, n'ayant pu être mené et aboutir dans de bonnes conditions que grâce à l'engagement de nombreuses personnes que je tiens à saluer ici.Je remercie en premier lieu Hind Bril El-Haouzi et Philippe Thomas pour leur forte implication dans la direction de cette thèse, de l'écriture du sujet à la soutenance. C'était une chance d'avoir des encadrants aussi accessibles et disponibles, que ce soit pour échanger des idées et des pistes de recherche, relire et corriger documents et présentations, ou simplement discuter et s'aérer l'esprit. Pour tout cela, merci.Evidemment, je tiens à remercier les membres du jury : Pr. Nathalie Julien et Pr. Jonathan Weber, pour avoir acccepté de rapporter cette thèse, ainsi que Pr. Samir Lamouri et Pr. Robert Pellerin pour avoir accepté d'en être examinateurs. Je suis heureux que vous ayez accepté de consacrer votre temps à l'évaluation de mes travaux et d'avoir pu bénéficier de vos différents retours.Tout mes remerciements à Emmanuel Zimmermann, pour son implication et son soutient technique et moral à de nombreuses thèses du site du CRAN d'Epinal. Je remercie également Michael Morin et Jonathan Gaudreault pour nos différentes collaborations qui furent très agréables.Et enfin, pêle-mêle, je remercie (et félicite !) ma famille, mes amis, et mes collègues, pour m'avoir supporter tout au long de ce projet. Discussions, soirées, jeux de société, vacances et voyages : toutes ces choses permettent de garder à l'esprit qu'il y a une vie en dehors du doctorat et qu'il est important de garder un équilibre entre la recherche et les loisirs.

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Multiple Period Combined Optimization Approach to Forest Production Planning

Cited by 36 publications

References 1 publication

A comparison of optimization models for lumber production planning

A comparison of optimization models for lumber production planning

Production planning with uncertainty in the quality of raw materials: a case in sawmills

Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future

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