Formulating preliminary design optimization problems using expert knowledge: Application to wood-based insulating materials

Abstract The design of wood-fiber based thermal insulating material with optimized properties and characteristics requires a good scientific knowledge of the latter. Currently, the technical, economic and ecological characteristics of a wood-fiber based composite mat are not well known. This article presents a methodology for the acquisition of expert knowledge so that the properties and characteristics can be modeled. The knowledge domain is multidisciplinary and it was delimited and decomposed into disciplines and domains of expertise. A panel of seven experts was constituted to cover the various disciplines and domains of expertise of the knowledge domain. Knowledge acquisition sessions, guided by the estimated importance and the availability of knowledge, were conducted using semi-structured interviews and the mapping of the existing causal relations between variables. A causal map was established to represent the causal knowledge of each of the experts and then, the established causal maps were assembled into a unique global causal map, which was subsequently validated by the experts. It contains the information necessary for formulating the properties and characteristics to be optimized, which were: thermal conductivity; thickness recovery of the material; the manufacturing cost and the product's environmental impact. Properties and characteristics are function of raw material type, their morphological properties and the manufacturing process variables. This methodology makes it possible to establish which objectives to optimize and which variables influence each objective. Consequently, the objective functions of the optimization problem can be clarified, specified and modeled.

[1]  Tomas Ekvall,et al.  System boundaries and input data in consequential life cycle inventory analysis , 2004 .

[2]  Andrew Kusiak,et al.  Decomposition in data mining: an industrial case study , 2000 .

[3]  Nicholas Ross Milton,et al.  Knowledge Acquisition in Practice: A Step-by-step Guide , 2007 .

[4]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[5]  Pavel V. Sevastjanov,et al.  A new approach to the rule-base evidential reasoning: Stock trading expert system application , 2010, Expert Syst. Appl..

[6]  Guy Della Valle,et al.  Qualitative modelling of a multi-step process: The case of French breadmaking , 2009, Expert Syst. Appl..

[7]  Robert Ivor John,et al.  Multi-objective optimisation in inventory planning with supplier selection , 2017, Expert Syst. Appl..

[8]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[9]  Kamal Kansou,et al.  Modelling Wheat Flour Dough Proofing Behaviour: Effects of Mixing Conditions on Porosity and Stability , 2013, Food and Bioprocess Technology.

[10]  Dewang Chen,et al.  Balise arrangement optimization for train station parking via expert knowledge and genetic algorithm , 2016 .

[11]  X. Thibault,et al.  3D CHARACTERIZATION OF WOOD BASED FIBROUS MATERIALS: AN APPLICATION , 2011 .

[12]  R. Likert.  “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[13]  R. Edwards,et al.  What Is Qualitative Interviewing , 2013 .

[14]  Shashi Shekhar,et al.  Cognitive Mapping , 2008, Encyclopedia of GIS.

[15]  Prakash P. Shenoy,et al.  A Bayesian network approach to making inferences in causal maps , 2001, Eur. J. Oper. Res..

[16]  Brahim Chaib-draa,et al.  Causal Maps: Theory, Implementation, and Practical Applications in Multiagent Environments , 2002, IEEE Trans. Knowl. Data Eng..

[17]  Sankaran Mahadevan,et al.  Evidential cognitive maps , 2012, Knowl. Based Syst..

[18]  J. Malvestio,et al.  3D mechanical analysis of low-density wood-based fiberboards by X-ray microcomputed tomography and Digital Volume Correlation , 2013, Journal of Materials Science.

[19]  Chen-Fu Chien,et al.  An AHP-based approach to ERP system selection , 2005 .

[20]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[21]  Carlos León,et al.  Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant , 2012, Expert Syst. Appl..

[22]  Laurent Orgéas,et al.  Towards the 3D in situ characterisation of deformation micro-mechanisms within a compressed bundle of fibres , 2011 .

[23]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[24]  Majid Nili Ahmadabadi,et al.  An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework , 2008, Expert Syst. Appl..

[25]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[26]  Ahmed Frikha,et al.  On the use of a multi-criteria approach for reliability estimation in belief function theory , 2014, Inf. Fusion.

[27]  Sondoss Elsawah,et al.  A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models. , 2015, Journal of environmental management.

[28]  Lior Rokach,et al.  Decomposition Methodology for Knowledge Discovery and Data Mining , 2005, The Data Mining and Knowledge Discovery Handbook.

[29]  Karima Sedki,et al.  Inference reasoning on fishers' knowledge using Bayesian causal maps , 2015, Ecol. Informatics.

[30]  Jean-Francis Bloch,et al.  Analysis of the hygroexpansion of a lignocellulosic fibrous material by digital correlation of images obtained by X-ray synchrotron microtomography: application to a folding box board , 2011 .

[31]  ラン ラン,et al.  Interview ラン・ラン , 2014 .

[32]  Dimitri N. Mavris,et al.  Methodology for Examining the Simultaneous Impact of Requirements, Vehicle Characteristics, and Technologies on Military Aircraft Design , 2000 .

[33]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[34]  Cédric Baudrit,et al.  Parameter elicitation in probabilistic graphical models for modelling multi-scale food complex systems , 2013 .

[35]  Luis F. Luna-Reyes,et al.  Collecting and analyzing qualitative data for system dynamics: methods and models , 2003 .

[36]  D. Jeulin,et al.  3D Morphological Characterization of Phonic Insulation Fibrous Media , 2011 .

[37]  Amadou Ndiaye,et al.  Meta-heuristic Methods Applied to the Design of Wood—Plastic Composites, with Some Attention to Environmental Aspects , 2009 .

[38]  Amadou Ndiaye,et al.  Elaboration d'un isolant thermique et acoustique à base végétale. Du concept à la réalisation , 2010 .

[39]  Herman van Dyk,et al.  An Innovative Wood-Fiber Composite Incorporating Nonwoven Textile Technologies , 2009 .

[40]  Valerie Belton,et al.  Causal maps and the evaluation of decision options—a review , 2006, J. Oper. Res. Soc..

[41]  Peter H. Gray,et al.  Knowledge sourcing methods , 2006, Inf. Manag..

[42]  Nancy J. Cooke,et al.  Knowledge Elicitation , 2003 .

[43]  Jérôme Lux,et al.  3D structural characterisation, deformation measurements and assessment of low-density wood fibreboard under compression. The use of X-ray microtomography. , 2008 .

[44]  Peter H. Gray,et al.  Knowledge Sourcing Effectiveness , 2004, Manag. Sci..

[45]  Frank Maurer,et al.  Requirements engineering and agile software development , 2003, WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003..

[46]  Yong Deng,et al.  A new fuzzy dempster MCDM method and its application in supplier selection , 2011, Expert Syst. Appl..

[47]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.