Summer school, Value chain optimization network. Lakehead university, Thunder Bay ON May 2012

1. Motivation
This project aims to investigate new manufacturing strategies for sawmills in order to produce
application-based hardwood lumber, instead of the traditional methods based on NHLA classification,
so as to increase the rate of lumber recovery and hence, maximize the profit.
Agent-Based Approach
Classification trees are nonparametric statistical learning methods that incorporate feature selection
and interactions to predict the membership of each observation into a categorical variable. Each
branch of a classification tree can be interpreted as a rule, so it can be considered as a set of rules (see
example for cabinets below)
Overview
Typical Transformation of Hardwood
1. Floor 2. Wardrobe 3. Staircase 4. Panel 5. Cabinet 6. Molding 7. Pallete
1. Sorting 2. Sawing
There are 5 big steps from the harvesting to the
final users
At the sawmill level there are four main process
Several research projects have been done to
study sawing, drying and planing
This research focuses in the sorting process in
order to improve log allocation to the next pro-
cesses by considering the needs of the second
transformation
It’s necessary to first identify the logs attributes
that increase the yield of each type of transfor-
mation.
Data Base
The analysis starts from a data
base with several logs mea-
sures (inputs) and it’s procure-
ment cost ($/Pmpm) and yield
(m3/mpm) for each type of
transformation (outputs).
In order to identify
the best logs for each
category, it is neces-
sary to identify an es-
timation method.
However, the predic-
Classification Trees
These binary matrices are used to built the classification trees
(one for each category), which are represented as equations
that can be transformed into classification diagrams
InputOutput
Cost distribution Percentiles Cabinet sample Threshold
classification
tion accuracy of traditional methodologies is very low (<50%).
The data-mining field offer some interesting approaches for
solving this issue, like classification trees
This procedure allows to construct a set of binary matrices for each percentile
threshold, where each row classifies the membership of each log to each
threshold and category (F, W, S, P, C, M and T)
3. Drying
4. PlaningFinal product
ready to second transformation
Classification Diagram for Cabinets
They can also be used to
build a joint classification
grid by using only 5 of
the set of the original 13
attributes:
By using the classification grid, it is possible to transform the reception process into a demand-
driven approach.
Log Handling and Sorting Implementation
Classification Grid
Preliminary Results
Classification accuracy: 81.5%
Procurement cost reduction: 44%
This methodology implies however an extra work force at the logs reception as well as more loaders
and space for storage, which means an increase in the handling costs
In order to evaluate this, we use agent-based simulation to test several manufacturing control strate-
gies in order to find the best way to implement the solution.
Before After
All categories pile
Category 1 pile Category 2 pile
Category 3 pile Category 4 pile
The actual phase of the project is the construction of a simulation platform which combine discrete
event with agent-based simulation.
The discrete event approach simulates the push process : Sawing, Drying and Planning
The agent-based approach simulates the decision process of the sorting and allocation
of logs by using the classification grid for the sawing according to an external and
random demand (pull).
External demand in
terms of secondary
transformation
Planning Agent
Recieve the exeternal
demand and the internal
processing information
Secondary
transformation
market
Final storage
ready to be sold
Sawing / Planin /
Drying (PUSH)
Loader Agent
Takes decisions about logs
sorting and allocation
Reception process
Sorting process
(piles by category)
A preliminary discret-event simulation evaluation leads to an average procurement cost re-
duction of 44%. In the next phase of the research project, we are building an agent-based
simulation in order to evaluate the impact of various log handling and sorting techniques to
implement these classification trees.
Sawmill
Optimization of Sorting and Allocation Activities in Sawmills
with Data-MiningTechniques and Agent-Based Modeling
Alvaro Gil
M.Sc. Student École Polytechnique de Montréal
Supervisor: Jean-Marc Frayret, Ph.D.