A solution method for creating laminated wood panels with revalorized wood boards.
Spécia, Tanguy
Promotor(s) : Paquay, Célia
Date of defense : 21-Jun-2023/28-Jun-2023 • Permalink : http://hdl.handle.net/2268.2/16974
Details
Title : | A solution method for creating laminated wood panels with revalorized wood boards. |
Translated title : | [fr] Une méthode de résolution pour créer des panneaux de bois lamellé-collé avec des planches de bois revalorisées |
Author : | Spécia, Tanguy |
Date of defense : | 21-Jun-2023/28-Jun-2023 |
Advisor(s) : | Paquay, Célia |
Committee's member(s) : | Baratto, Marie |
Language : | English |
Number of pages : | 74 |
Keywords : | [en] Optimization, [en] Cutting and Packing Problem [en] CLT [en] Glulam [en] Revalorization [en] NP-hard [en] Combinatorial analysis [en] Mathematical model [en] Heuristic [en] Constructive algorithm [en] Best-Fit |
Discipline(s) : | Business & economic sciences > Production, distribution & supply chain management Business & economic sciences > Quantitative methods in economics & management |
Institution(s) : | Université de Liège, Liège, Belgique |
Degree: | Master en ingénieur de gestion, à finalité spécialisée en Supply Chain Management and Business Analytics |
Faculty: | Master thesis of the HEC-Ecole de gestion de l'Université de Liège |
Abstract
[en] The increasing demand for natural resources and the environmental commitments towards
sustainable practices have led to a surge of interest in finding efficient solutions for the
two-dimensional problem of creating laminated wood panels with revalorized wood boards.
To address this challenge, sustainable and circular solutions are imperative. This thesis
presents an exact solution to the problem, establishing its NP-hardness and justifying the
need for an approximation method. This final solution method is developed in the form of
a construction heuristic inspired by the concept of strip creation and draws inspiration from
Best-Fit algorithms commonly used in Bin Packing.
File(s)
Document(s)
Annexe(s)
MM1_Dataset1.xlsx
Description: Dataset 1 for the first mathematical model
Size: 11.79 kB
Format: Microsoft Excel XML
Description: Dataset 1 for the first mathematical model
Size: 11.79 kB
Format: Microsoft Excel XML
MM1_Dataset2.xlsx
Description: Dataset 2 for the first mathematical model
Size: 11.46 kB
Format: Microsoft Excel XML
Description: Dataset 2 for the first mathematical model
Size: 11.46 kB
Format: Microsoft Excel XML
MM1_Sol_Dataset1.xlsx
Description: Solution obtained after testing MM1 with Dataset 1
Size: 10.53 kB
Format: Microsoft Excel XML
Description: Solution obtained after testing MM1 with Dataset 1
Size: 10.53 kB
Format: Microsoft Excel XML
MM1_Sol_Dataset2.xlsx
Description: Solution obtained after testing MM1 with Dataset 2
Size: 7.86 kB
Format: Microsoft Excel XML
Description: Solution obtained after testing MM1 with Dataset 2
Size: 7.86 kB
Format: Microsoft Excel XML
MM2_Dataset1.xlsx
Description: Dataset 1 for the second mathematical model
Size: 11.67 kB
Format: Microsoft Excel XML
Description: Dataset 1 for the second mathematical model
Size: 11.67 kB
Format: Microsoft Excel XML
MM2_Dataset2.xlsx
Description: Dataset 2 for the second mathematical model
Size: 11.35 kB
Format: Microsoft Excel XML
Description: Dataset 2 for the second mathematical model
Size: 11.35 kB
Format: Microsoft Excel XML
MM2_Dataset3.xlsx
Description: Dataset 3 for the second mathematical model
Size: 11.34 kB
Format: Microsoft Excel XML
Description: Dataset 3 for the second mathematical model
Size: 11.34 kB
Format: Microsoft Excel XML
MM2_RealData.xlsx
Description: Real Data for the second mathematical model
Size: 16.12 kB
Format: Microsoft Excel XML
Description: Real Data for the second mathematical model
Size: 16.12 kB
Format: Microsoft Excel XML
MM2_Sol_Dataset1.xlsx
Description: Solution obtained after testing MM2 with Dataset 1
Size: 8.8 kB
Format: Microsoft Excel XML
Description: Solution obtained after testing MM2 with Dataset 1
Size: 8.8 kB
Format: Microsoft Excel XML
MM2_Sol_Dataset2.xlsx
Description: Solution obtained after testing MM2 with Dataset 2
Size: 8.65 kB
Format: Microsoft Excel XML
Description: Solution obtained after testing MM2 with Dataset 2
Size: 8.65 kB
Format: Microsoft Excel XML
MM2_Sol_Dataset3.xlsx
Description: Solution obtained after testing MM2 with Dataset 3
Size: 8.66 kB
Format: Microsoft Excel XML
Description: Solution obtained after testing MM2 with Dataset 3
Size: 8.66 kB
Format: Microsoft Excel XML
MM2_Sol_RealData.xlsx
Description: Solution obtained after testing MM2 with Real Data
Size: 8.62 kB
Format: Microsoft Excel XML
Description: Solution obtained after testing MM2 with Real Data
Size: 8.62 kB
Format: Microsoft Excel XML
MM3_Tests_15-39.xlsx
Description: Solutions obtained after testing MM3
Size: 15.97 kB
Format: Microsoft Excel XML
Description: Solutions obtained after testing MM3
Size: 15.97 kB
Format: Microsoft Excel XML
Dataset0.xlsx
Description: Dataset 0 for MM3 and the Heuristic
Size: 19.14 kB
Format: Microsoft Excel XML
Description: Dataset 0 for MM3 and the Heuristic
Size: 19.14 kB
Format: Microsoft Excel XML
Heuristic_Tests_5000-100000.xlsx
Description: Solutions obtained after testing MM3
Size: 17.52 kB
Format: Microsoft Excel XML
Description: Solutions obtained after testing MM3
Size: 17.52 kB
Format: Microsoft Excel XML
Cite this master thesis
All documents available on MatheO are protected by copyright and subject to the usual rules for fair use.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.
The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.