Simulation-optimisation based framework for Sales and Operations Planning taking into account new products opportunities in a co-production context

Wery, Jean; Gaudreault, Jonathan; Thomas, André; Marier, Philippe

doi:10.1016/j.compind.2017.10.002

Cited by 23 publications

(21 citation statements)

References 7 publications

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“…Owing to these characteristics, predicting in advance the set of lumber (called a basket of products in the following) resulting from the sawing of a specific log, is a difficult problem. This complicates the optimization of operational or strategic planning, as well as the running of simulations needed to make long-term decisions, such as the remodeling of a sawmill, or simply the acceptation of a command containing unusual products, since acceptation of such a command would require the modification of mill setup and impact the whole mix of lumber produced in a way difficult to predict (Wery et al (2018)). However, the wood F 3: Breaking a log into a set of lumber.…”

Section: Sawing Simulation In the Wood Industrymentioning

confidence: 99%

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Chabanet

El-Haouzi

Thomas

2021

Computers in Industry

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Although digital simulations are becoming increasingly important in the industrial world owing to the transition toward Industry 4.0, as well as the development of digital twin technologies, they have become increasingly computationally intensive. Many authors have proposed the use of Machine Learning (ML) metamodels to alleviate this cost and take advantage of the enormous amount of data that are currently available in industry. In an industrial context, it is necessary to continuously train predictive models integrated into decision support systems to ensure the consistency of their prediction quality over time. This led the authors to investigate Active Learning (AL) concepts in the particular context of the sawmilling industry. In this paper, a method based on AL is proposed to combine simulation and an ML metamodel that is trained incrementally using only selected data (smart data). A case study based on the sawmilling industry and experiments are shown, the results of which prove the possible advantages of this approach.

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Section: Sawing Simulation In the Wood Industrymentioning

confidence: 99%

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Chabanet

El-Haouzi

Thomas

2021

Computers in Industry

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“…Computational simulations of log cutting have been tested for specific variables and scenarios, as well as for different species, and the results have shown the importance of this technique for the optimization of the industrial process of log sawing methods and maximization of yield (Buehlmann and Thomas, 2001;Ikonen et al, 2003;Moberg and Nordmark, 2006;Pinto et al, 2006;Knapic et al, 2011;Berglund et al, 2013;Fredriksson et al, 2015;Stängle et al, 2015;Wery et al, 2018).…”

Section: Santos Et Almentioning

confidence: 99%

“…In most cases the decision is random, and this will rarely result in a high level of yield (Biasi and Rocha, 2007). Currently, with the advent of computer programs it is possible to create a method for log cutting that involves their classification and the establishment of sawn pattern which allow for optimization of yield and, up to a certain point, minimization of costs (Ikonen et al, 2003;Wery et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Conicity and Yield for Lumber From Commercial Species From the Amazon

Santos

Gama

Filho

et al. 2019

CERNE

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“…Maturana et al [8] propose a mathematical model for scheduling problem at a sawmill to estimate the required log supply and fulfilling orders with minimum cost. Wery et al [9] conduct a study to define an optimal sawing pattern using Optitek. Ouhimmou et al [10] develope a mathematical model to minimize cost at a competitive level of service for one furniture company.…”

Section: * Corresponding Authormentioning

confidence: 99%

Optimal Kiln Dry Allocation for Dry Timber Preparation with Minimal Cost and Available Capacity

Theresia

Widyadana

Wahjudi

2019

jti

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Optimization models are increasingly developed for planning and scheduling natural resources manufacture. However, the uncertainty of material from natural resources makes it more difficult to develop a model. In this paper, we concern on the cost of dry timber preparation to finishing sections in a wood-board manufacturer. Based on the characteristic of the material and wood-board production process, we develop two models to a minimize transportation and production cost of wood supply chain which is consist of dry timber preparation process after the sawing process. The models consider facilities capacity, the distance between facilities and drying periods based on timber specification. The model is solved using linear programming, result in drying allocation on the chamber in kiln dry with a minimum cost of the process. A sensitivity analysis is conducted to verify the models and show the effect of variation of two parameters to the objective value. The results show varies of external capacity of kiln dry is more sensitive to total cost than varies of internal capacity.

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Simulation-optimisation based framework for Sales and Operations Planning taking into account new products opportunities in a co-production context

Cited by 23 publications

References 7 publications

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Coupling digital simulation and machine learning metamodel through an active learning approach in Industry 4.0 context

Conicity and Yield for Lumber From Commercial Species From the Amazon

Optimal Kiln Dry Allocation for Dry Timber Preparation with Minimal Cost and Available Capacity

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