How to Obtain Fair Managerial Decisions in Sugarcane Harvest Using NSGA -

Transcription

How to Obtain Fair Managerial
Decisions in Sugarcane Harvest
Using NSGA-II
State University of Pernambuco – Recife (Brazil)
Diogo F. Pacheco
Tarcísio D. P. Lucas
Fernando B. de L. Neto
HIS’2007 – Kaiserslautern – Germany
1
Agenda
I.
Motivation & Problem
II. Productivity Factors and Indicators
III. Previous works
IV. Additional background
V. Fair harvest decisions for sugarcane
VI. Simulation and Results
VII. Conclusion
© Fernando Buarque – fbln@dsc.upe.br
Page 2
Part I
Motivation & Problem
Page 3
Sugarcane strategic importance
•
•
•
Sugarcane is a major source of
carbohydrates for human feeding
Sugarcane is also growing fast as a
source of renewable fuel (e.g. 20-30%
of the 23M vehicles of Brazilian fleet)
Tendencies are of marked increase on
sugarcane demand worldwide
Page 4
The managerial problem at hand
•
•
•
During one harvest season, the manager has
to select daily a variable number of lots
(cultivated with sugarcane) to be harvested
Every distinct species (not to mention the
hybrids) have different maturation curves
Various agronomical and industrial
requirements are sometimes orthogonal
between them
Page 5
Part II
Productivity Factors and Indicators
Page 6
Productivity factors x indicators
•
•
Factors:
-
Can be controlled directly
-
Exist in great numbers
-
Are contextual (time-space)
Indicators:
-
Can only be controlled indirectly, thru
productivity factors
-
Exist in small numbers
-
Can be inferred through induction
Page 7
Examples of productivity factors
-Cane variety (type);
-Soil/Topology;
-Climate;
-Sowing date;
-Age;
etc
Page 8
Examples of productivity indicators
i.
ii.
iii.
TCH (sucrose) – measure the
sugarcane tonnage per hectare;
PCC (biomass) – measure the
apparent percentage of sugar in the
cane juice;
Fiber (quality of) – measure the
calorific potential in the fibrous
residue remaining after the
extraction of juice.
Page 9
Part III
Previous works
Page 10
Landmark work
•
•
A Computational Intelligence technique –
ANN was successfully used to model
sugarcane maturation [Lima Neto, 1998]
This initial work utilized historical data
to help training MLP ANNs to predict
productivity indicators that were
identified as important to support
decision makers
Page 11
General idea (Generation 0)
[Lima Neto, 1998]
C. variety
TCH
Soil type
Climate
ANN
PCC
Sowing date
Age
Fiber
Year
Page 12
Follow-up work – Generation 1
•
•
•
…
Pacheco et al. (2005) refined predictions
of indicators
Pacheco et al. (2006) applied linear
decision models
Page 13
Some results – Generation 1
PCC
PCC - Real x Predição
17
16
15
14
13
12
Real
Previsão
- Real
1 2 3 4 5 6 7 Fibra
8 9 10
11 12x13Predição
14 15 16 17 18 19 20
Fibra
25
20
Real
15
Previsão
10
1
2
3
4
5
6
7
8 9 -10
11 12x 13
14 15 16 17
TCH
Real
Predição
18 19 20
TCH
170
120
Real
Previsão
70
20
1
2
3 4
5
6 7
8
9 10 11 12 13 14 15 16 17 18 19 20
Page 14
Follow-up work – Generation 2
•
•
Oliveira et al. (2006) used GA to achieve
better decisions and introduced a
framework to evaluate decision quality
Oliveira et al. (2007) used Fuzzy-Logic
controllers to achieve even better
decisions
Page 15
Comparisons of generations 0, 1 & 2
Method
Plots-ID
TCH
Avg.
Fiber
Avg.
PCC
Manual selection
34, 56, 102, 169,
199, 238, 365,
385, 404
649.0026
15.8376
16.5478
Linear method
[Pacheco, 2006]
26, 34, 56, 102,
131, 169, 199,
365, 385, 404
667.0466
15.8349
16.5431
Framework + G.A.
[Oliveira, 2006]
22, 314, 290,
335, 194, 147
649.8212
15.1324
16.1012
Page 16
Part IV
Additional Background
Page 17
Sugarcane
•
•
•
•
“Sugarcane” encompass 37 different
species (not to mention the hybrids)
Each specie has its own agronomical
behavior along the one year cycle
Several factors interfere with sugarcane
maturation
Modeling maturation curves of species
can have a profound economical impact
Page 18
Sugarcane harvest
•
•
•
Sugarcane harvest is a non-trivial
decision process due to the many
variables interfering with its maturation
The decision process encompass many
simultaneous objectives to be cared of
In many sugarcane plantations the
harvest decision is empirical because of
some inappropriateness of current
systems
Page 19
Decision Support Systems
•
•
•
•
Help on the decision making process of
semi-structured problems
Generally used on mid-managerial level
Users can select among possible
scenarios via decision dialogues
DSS should be friendly, fast and flexible
to consider daily basis variables, e.g.
sugarcane demand for the day.
Page 20
Multi-Objective Optimization
•
•
Real problems are usually multi-criterion
(objective)
A regular multi-objective optimization
problem (MOOP) presents at least two
challenges: (i) to find solutions as close
as possible to the Pareto-optimal front
and (ii) to obtain these solutions well
spread over this front
Page 21
NSGA-II: a fast non-dominated
sorting approach [Deb et. al, 2000]
•
•
•
A MOEA algorithm with elitism
Faster than PAES (notion of nondominating solutions)
Produces better distributions on the
Pareto front (notion of crowding)
Page 22
NSGA-II: a fast non-dominated
sorting approach [Deb et. al, 2000]
Page 23
Framework to harvest decisions
[Lima Neto et. al, 2007]
Decision Space over Problem P encompassing decisions, components & attributes
Page 24
Relevance of decision components
•
In this way, a suitable solution would be to
search thoroughly among the possible
solutions by assessing the relevance of
every component of each decision
∑ ( w * f i (a i ) )
R(C j) =
∑ w
n
i =0
i
n
i =0
i
Regarding attribute:
fi(ai) Mapping function
W = {w1,w2,...,wn}
weights
Page 25
Evaluation of decision components (past)
* Area * PCC
TCH
f ( PCC ) =
MAX (TCH * Area * PCC )
i
pcc
f
i
i
* Area * Fiber
TCH
( Fiber ) =
MAX (TCH * Area * Fiber)
i
Fiber
i
i
i
i
Decision maker has to inform his/her:
a) Business preferences: W = { w-fiber, w-pcc}
b) Needs: {desired ton of sugarcane, Maximum Area,
Minimum PCC, Minimum Fiber}
Page 26
Evaluation of decision components (past)
R(C j)
=
f
Fibra
( Fiber i ) * wFiber +
f
w +w
Fiber
pcc
( PCC i ) * wPCC
PCC
Page 27
Evaluation of decisions (past)

Maximizing overall relevance
F(d k) = ∑ R(C j)
n
j =0

Minimizing overall penalty
1
F (d k ) = ∑
R(C j)
n
j =0
Page 28
Part V
Fair harvest decisions for sugarcane
Page 29
This work approach – overview
Page 30
Representing candidate solutions

The individuals utilized represent the
available (i.e. not harvested) lots;
A bit stream representation is used, where
0 indicates assigned to be harvested, and
1 means the opposite;
At each generation, the number of lots
available decrease so does the size of
individuals.
Genes of an individual (i.e. available lots)
1001001000 1110000010 1000110001 0010101001
Figure . Individual representation of a suggestion of 15 lots in 40 available ones
Page 31
GA Initial Population

Individuals are randomly generated
according to the quantity of available
lots in the field
Each bit in the genotype has a 50%
likelihood of being activated
Page 32
This approach animation
Predictions of
N available
lots
Generates
ANN – Multilayer
Perceptron
Process
End
Mapping
lots into
genotype
Chooses a
solution
Optimal
Pareto-front
Evolves
Page 33
Part VI
Simulation and Results
Page 34
Objectives of this work
1) To incorporate agronomical performance
indicators into an Intelligent Decision
System to help decision makers (to
better deciding on sugarcane harvest)
2) To produce and test a prototype of a
computer system that couples in a
fairway the sugarcane-mill demands
with acceptable agronomical indicators
Page 35
The problem (new) definition

To find the combination of lots that
maximizes the production of PCC and
Fiber, constrained to a minimal tonnage
(that guarantees energy power for the
sugar mill to operate without interruption)
Page 36
Two flavors for the problem simulation
A) considers 2 objectives and uses a
penalty function;
B) converts the constraint into another
objective. Hence, the optimization
considers 3 objectives.
Page 37
Formalism
• Approach A:
• Approach B:
Page 38
NSGA-II Parameters

The parameters
used in NSGA-II
were
experimentally
chosen;
A new stop
criterion was used
in parallel to the
number of
generations.
Page 39
Experimental Setup

Data set: 418 training patterns obtained
from a sugarcane mill from Brazil;
We assumed a hypothetical scenario:
• The minimal desired tonnage for each prediction
was fixed at 4000 tons.
• The heuristic is applied accepting boundaries from
less 0.5% to more 5% tons;
• Two predictions per month, i.e. one for each
fortnight;
• The harvest must be finished at most in 12
interactions (6 months);
• In the12th interaction, if the limit of 4200 ton is
achieved and there are still remaining lots to be
cropped, they will all be harvested.
Page 40
Results of Experiments
Approach
A
Approach
B
Page 41
Comparisons with other techniques
Page 42
Part VII
Conclusion
Page 43
Conclusion

The application of EA within a MO
formulation can be very beneficial to the
sugarcane harvest decision process
NSGA-II as utilized can aggregate fairness
to the manager decision of the problem
The consideration of a constraint as
another goal was found to be a good
avenue of thinking (as the attribution of a
penalty function may cause the same
problems faced by MO classical methods)
Page 44
Future work

To model the sugarcane harvest considering, not
only agronomical factors, but also logistics
To consider fuzzy-logic to soften the thresholds
used;
Further investigations on better transforming penalty
functions into objectives
Further investigations on how to incorporate decision
preferences a posteriori (upon the Pareto landscape)
Page 45
Related references
F.B. Lima Neto, “Managerial Decision Support, based on Artificial Neural
Networks” (in Portuguese), Master Dissertation. Department of
Informatics, Federal University of Pernambuco, Recife - Brazil, 1998.
D.F. Pacheco, F.S. Regueira and F.B.L. Neto, “Using Artificial Neural
Networks in Sugar Cane Harvest to Predict PCC, TCH and Fiber” (in
Portuguese), Alcoolbrás Magazine, S. Paulo - Brazil, v. 90, 2005, pp. 60-63.
D.F. Pacheco, “An Intelligent Decision Support System for Agriculture
Harvest” (in Portuguese), Technical Report presented as Graduation
Monograph, Department of Computing Systems, Polytechnic School of
Engineering, Pernambuco State University, Recife - Brazil, 2006.
F.R.S. Oliveira, D.F. Pacheco and F.B.L. Neto, “Intelligent Support Decision
in Sugarcane Harvest”, Proceedings 4th World Congress of Computers in
Agriculture, Orlando, Florida, 2006, pp. 456-462.
F.R.S. Oliveira, D.F. Pacheco and F.B.L. Neto, “Hybrid Intelligent Suite For
Decision Support in Sugarcane”, 6th Brazilian Congress of AgroInformatics (SBIAgro), São Pedro (SP) – Brasil, 2007.
F.B. Lima Neto, F.R.S. Oliveira, D.F. Pacheco “HIDS: Hybrid Intelligent Suite
for Decision Support”. In: Seventh International Conference on Intelligent
Systems Design and Applications (ISDA), Rio de Janeiro - Brasil, 2007.
Page 46
Other references
[1] Biodiesel and Ethanol Investing, “Ethanol Fuel Benefits”, available on
http://www.biodieselinvesting.com/ethanol-fuel-benefits/, accessed in May 2007.
[2] Food and Agricultural Organization of United Nations, “Major Food and Agricultural
Commodities and Producers”, The Statistics Division, Economic and Social
Department, available on http://www.fao.org/es/ess/top/commodity.ht
ml?lang=en&item=156&year=2005, accessed in May 2007.
[5] C.H. Papadimitriou and K. Steiglitz, “Combinatorial Optimization: Algorithms and
Complexity”. Prentice-Hall, Inc., 1982.
[8] S. Haykin, “Neural Networks – A Comprehensive Foundation”. Prentice-Hall
International Editions. New Jersey, USA, 1994.
[9] A.E. Eiben and J.E. Smith, “Introduction to Evolutionary Computing”, Springer, New
York, 2003.
[10] K. Deb. “Multi-Objective Optimization using Evolutionary Algorithms”, John Wiley &
Sons, UK, 2001.
[11] I. Linkov, et. al., “Multi-criteria decision analysis: A framework for structuring
remedial decisions at the contaminated sites”, Springer, New York, 2004, pp. 15-54.
[12] J. Fülöp, “Introduction to decision-making methods”, BDEI-3 Workshop,
Washington, 2005.
[13] J. Figueira et. al., “Multiple Criteria Decision Analysis: State of the Art Surveys”,
Springer, New York, 2004.
[14] C.A.C. Coello, “Metaheuristics for Multiobjective Optimization”, Tutorial on IEEE
Symposium Series on Computational Intelligence, 2007.
[15] K. Deb et. al., “A Fast Elitist Non-Dominated Sorting Genetic Algorithm for MultiObjective Optimization: NSGA-II”, KanGAL report 200001, Indian Institute of
Technology, Kanpur, India, 2000.
Page 47
Thank you !
Fernando Buarque
http://www.fbln.pro.br
fbln@dsc.upe.br
Page 48
Extra: Oliveira, 2006-GA parameters
Desired scenario:
• Validity criteria
• PCC (minimum) = 16
• Fiber (minimum) = 15
• TCH (target) = 650 T
• Area (maximum) = 10 plots
• Weights of attributes:
• wpcc = 10
• wfiber = 5
Page 49
Extra: Oliveira, 2006-conclusion

Contributed DSS is a great advance when
compared current harvest decision process
because:
• Speed-up the decision process
• Reduce the number of plots selected with
neither compromising quality nor biomass
• Help on reducing human error
Contributed approach allows re-runs
generating different suggestions and distinct
scenarios
Page 50
Extra: Oliveira, 2006-steps
1. Gather candidate decisions (ANN)
2. Define components and attributes
3. Set validity criteria
4. Proceed the search (gDSS)
5. Evaluate decision
6. Finish or re-start from step 4.
Page 51
Extra: Oliveira, 2006-references

Diogo Pacheco. An Intelligent Decision Support System for Agriculture Harvest (in
Portuguese), Technical Report presented as Graduation Monograph to Department
of Computing Systems – Polytechnic School of Engineering – Pernambuco State
University, 2006.
Efrain Turban. Decision Support Systems and Expert Systems, 4th. Edition ,
Prentice-Hall International Editions. New Jersey, USA, 1995.
Fernando Buarque de Lima Neto. Managerial Decision Support, based on Artificial
Neural Networks (in Portuguese), Master Dissertation presented to Department of
Informatics, Federal University of Pernambuco, Recife, Brazil, 1998.
Randy L. Haupt and Sue E. Haupt. Practical Genetic Algorithms. 2nd ed. WileyInterscience, 2004.
Simon Haykin. Neural Networks – A Comprehensive Foundation. Prentice-Hall
International Editions. New Jersey, USA, 1994.
Ralph Sprague Jr. and Hugh J. Watson. Decision Support for Management.
Prentice-Hall International Editions. New Jersey, USA, 1996.
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach.2nd
Edition. Prentice-Hall International Editions. New Jersey, USA, 2003.
Page 52