s

1. A decision-making tool to provide sustainable
solutions to a consumer
Ricardo Santos 1 Antonio Abreu 3
João Matias 2
01-03 July, 2020 | Caparica, Lisbon – Portugal
1 University of Aveiro, Portugal 2Dept. of Economics, Management, Industrial
Engineering and Tourism (DEGEIT)
University of Aveiro, Portugal
C-MasT – University of Beira Interior
Govcopp - University of Aveiro
3 ISEL- Instituto Superior de Engenharia de Lisboa,
Instituto Politécnico de Lisboa
CTS Uninova, Faculdade de Ciências e Tecnologia,
Universidade Nova de Lisboa, Portugal
ajfa@dem.isel.ipl.pt
Technological Innovation for Life
Improvement
Govcopp - University of Aveiro

2. • Building sector
Represents about
30-40 % final energy
consumed
• 27% of final energy
(building sector)
concerns to residential
group
Potential energy savings on residential sector
INE/DGEG, 2010
Fonte: EIA,2017
1.Introduction|Motivation
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
• High “weight” of
electrical energy
on total consumer
energy expenses
Fonte: EIA,2017
• High
dependency on
electrical
energy
producion
through fóssil
fuels
INE/DGEG, 2010
2
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer

3. 1.Introduction|Motivation
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
3
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
Research Question
Is it possible to develop a holistic model (Economic, Social and Environmental dimensions) to
support decision making, based on evolutionary algorithms (EA), that allows the decision
agent to obtain sustainable solutions?
H1: If by using Multi-objective Evolutionary Algorithms (EA), combined with Multicriteria analysis, it’s
possible to achieve several sustainable solutions to the consumer, by selecting household appliances
from the market.
Hypothesis

4. Methodological Synthesis
Maq. Lavar
roupa
Maq. Lavar
Louça
Frigorifico Forno
Ar
condicionado
Iluminação
Maq. Secar
Roupa
x12 x13 x14 x15 x16 x17
x11
x22 x23 x24 x25 x26 x27
x21
x32 x33 x34 x35 x36 x37
x31
x102 x103 x104 x105 x106 x107
x101
Opção
i
N
Soluções
Opção
Xij
Dimensão j
Air Conditioner
Dish Washing Machine
Fridge
Electric
Furnace
Dryer washing
Machine
Lighting
Evolutionary
Algorithms
Solutions from
the market
Cloth Washing
Machine
Consumer’s
Preferences
(design,
reliability,
appliance’s
noise,…)
n Clusters
of data
Sustainable
solutions,
suitable to
the consumer
…….
Consumer’s space decision
Multi-Attribute Value Theory
What washing
machine to buy?
……
What Electrical
furnace to buy?
Consumer’s needs
(nº occupants, area, available budget, …)
4
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
2.Research method

5. 2.Research method
Proposed
Approach
5
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

6. 2.Research method
Criteria, used to
define the decision
space, regarding
each energy service,
considered to be
acquired and based
on the Nr of
occupants
from the building
Dimension Criteria used Quantity
Air Conditioner
 types of air conditioner considered:
 zone to be heated/ cooled by the air
conditioner
 minimum capacity required
 wall (mono split)
wall (multisplit)
Portable
 living room
 9905,6 BTU
Washing
Machine
 capacity, based on the number of
household’s occupants [18]
 7 kg
Dishwasher
machine
 load capacity.  12 cutlery
Oven
 useful volume, available for cooking
according to the nr. of occupants [18]
 47 cm x 68 cm
Dryer machine
 type of dryer machines
 load capacity from [18]
 by exhaust ;
 7 kg
Lighting
 technology Halogen
CFL
Fluorescent
Led
Refrigerator
 capacity of the refrigerators [18]
 type of refrigerator according to the
number of occupants [18]
 150 liters
 refrigerator
Combined type.
6
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

7. 2.Research method
A total of 178 Attributes
defined, regarding
Environment and
Economic Problem
Dimensions, related to
each energy service
considered
7
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

8. 2.Research method
( ) ( ) ( )
( )
jt jt jt
g g g
ij ij ij
x v x


Based on MAVT, there is a value 𝑣𝑖𝑗
(𝑔𝑗𝑡)
𝑥𝑖𝑗
(𝑔𝑗𝑡)
, associated to the attribute 𝑥𝑖𝑗
(𝑔𝑗𝑡)
, such as:
 
 
(g ) (g )
(1)
.
(2)
(g ) (g )
better. .
( ) ( )
( )
( ) ( )
jt jt
ij
jt jt
ij ij
ij ij worst ij
ij ij
ij worst ij
v x v x
v x
v x v x



Scaling function, to preform 𝑣𝑖𝑗
(1)
𝑥𝑖𝑗
(𝑔𝑗𝑡)
 𝑣𝑖𝑗
(2)
𝑥𝑖𝑗
(𝑔𝑗𝑡)
transformation:
Evaluation table, belonging to each energy service j.
Problem formulation – Defining attributes and applying MAVT for each energy service
8
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

9. 2.Research method
Problem formulation – Defining attributes and applying MAVT for each energy service
Example of evaluation table - Air Conditioner (AC) energy service
𝑥𝑗
(𝑔𝑗𝑡)
𝑣𝑖𝑗 𝑥𝑗
𝑔𝑗𝑡
 
 
(g ) (g )
(1)
.
(2)
(g ) (g )
better. .
( ) ( )
( )
( ) ( )
jt jt
ij
jt jt
ij ij
ij ij worst ij
ij ij
ij worst ij
v x v x
v x
v x v x



Scaling function, to preform 𝑣𝑖𝑗
(1)
𝑥𝑖𝑗
(𝑔𝑗𝑡)
 𝑣𝑖𝑗
(2)
𝑥𝑖𝑗
(𝑔𝑗𝑡)
transformation:
9
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
2 2 2
2 2 2
21 22 2 21 22 2 21 22 2
2 2
21 22 2 21 22 2
21 22 2 21 22 2 21 22 2
( ) ( ) ( )
( ) ( ) ( ) ( ) ( ) ( )
12 12 12 12 12 12 12 12 12 12
( ) ( )
( ) ( ) ( ) ( )
22 22 22 22 22 22 22 22
. . ... ... ...
... ... ...
... ...
A B C
n n n
A B C
n n
A B
n n n
A B C
A A B B C C
A B
A A B B
AC A A A B B B C C C
X x x x x x x x x x
X x x x x x x x
2
21 22 2
2 2 2
21 22 2 21 22 2 21 22 2
( )
( ) ( )
22 22
( ) ( ) ( )
( ) ( ) ( ( ) ( ) ( )
102 102 102 102 102 102 102 102 102 102
...
... ... ... ... ... ... ... ... ... ... ... ...
... ... ...
nC
n n n
A B C
C
C C
A B C
A A B B C C
x x
X x x x x x x x x x
2 2 2
2 2 2
21 22 2 21 22 2 21 22 2
21
21 22 2 21 22 2 21 22 2
( ) ( ) ( )
( ) ( ) ( ) ( ) ( ) ( )
12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
( )
22 22 22 22
. . ... ... ...
( ) ( ) ... ( ) ( ) ( ) ... ( ) ( ) ( ) ... ( )
( )
A B C
n n n
A B C
n n n
A B C
A A B B C C
A
AC A A A B B B C C C
X v x v x v x v x v x v x v x v x v x
X v x v
2 2 2
22 2 21 22 2 21 22 2
21 22
( ) ( ) ( )
( ) ( ) ( ) ( ) ( )
22 22 22 22 22 22 22 22 22 22 22 22 22 22 22
(
( ) ( )
102 102 102 102 102 102 102
( ) ... ( ) ( ) ( ) ... ( ) ( ) ( ) ... ( )
... ... ... ... ... ... ... ... ... ... ... ...
( ) ( ) ... (
n n n
A B C
A B C
A B B C C
A A
x v x v x v x v x v x v x v x
X v x v x v x
2 2 2
2 21 22 2 21 22 2
) ( ) ( )
( ) ( ) ( ) ( )
102 102 102 102 102 102 102 102 102 102 102 102
) ( ) ( ) ... ( ) ( ) ( ) ... ( )
n n n
A B C
A B C
B B B C
v x v x v x v x v x v x

10. 2.Research method
Washing
machine Dishwasher
Refrigerator Oven
Air Conditioner
Lighting Dryer machine
x12 x13 x14 x15 x16 x17
x11
x22 x23 x24 x25 x26 x27
x21
x32 x33 x34 x35 x36 x37
x31
x102 x103 x104 x105 x106 x107
x101
Option
i
Solutions
Option
Xij
J Dimension
j
i
Consumer’s decisions space
Problem formulation – Defining the decision space
10
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

11. 2.Research method
subject to
with
   
dim dim
1 . .
1 1
: ( ) jt
n n
A
j j disp j disp
j j
r I x availablebudget x
 
 
  
  Economic – Budget:
Environment – Noise
 
: . .
jt
B
j j j j j
r Noise Max Noise x Max Noise
  
Objective functions:
Problem formulation – Defining the objective functions and the constraints used
11
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
 
 
max , / , ,
/ ( ) ( ), ( ), ( )
m
T
m A B C
V x c m A B C
subject to x X c V x V x V x V x

 
 
 
 
 
 
 
. . . . . .
melhor. . melhor. . melhor.
( ) ( ) (B ) (B ) (C ) (C )
( ) ( ) (B ) (B ) (C )
1 1
. . . 1
jt jt jt jt jt jt
A B
j j
efect j pior j efect j pior j efect j pior j
jt jt jt jt jt
j pior j j pior j j p
A A
n n
Total A B C
A A
t t
x x x x x x
V x
x x x x x x
  
 
   
  
   
   
   
  
   
 
 
.
(C )
1 1
C
j j
jt
ior j
n
n
j t
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
( )
1 1
:max ( ) ( )
A
j j
jt
n
n
A
A j j
j t
Economic Well being V x v x
 
  
(B )
1 1
:max ( ) ( )
B
j j
jt
n
n
B j j
j t
Social Well being V x v x
 
  
 
( )
1 1
:max ( ) 1 ( )
C
j j
jt
n
n
C
C j j
j t
Environment Well being V x v x
 
  


12. 2.Research method
Problem formulation – Defining the objective functions and the constraints used (cont.)
12
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
subject to
Economic – Budget:
     
dim
4 25
1 . 2 .
1 1
2
: ( )
j
j
n
n
A A
j j disp ij i disp
j j
j
r I x availablebudget x x
 
 

   
 
Social (Comfort) - Lighting
(minimum Illuminance)
 
15
1
2 1 min 1
.
: ( )
B
u
d
F n
x
r K E with K light properties
A F
 
 
23
3 2 . .( .)
:
B
th Aquec proj
r x Q

 
24
4 2 . .( .)
:
B
th Arref proj
r x Q

Social (Comfort) - Heating/Cooling needs
Environment – Noise:
 
 
 
 
 
11
21
61
71
51 1 .1
52 2 .2
56 6 .6
57 7 .7
:
:
/ 5 2,3,4,5,7
:
:
B
i def
B
i def
B
i def
B
i def
r x Noise
r x Noise
c i e j
r x Noise
r x Noise
 

 


 








2
2
( .3.6.)
61 3
( .7.5.)
62 7
: 1/
: 1/
A
i H O MLR
A
i H O MLL
r x C
r x C


  


  

Environment – Water consumption

13. 2.Research method
NSGAs adopted parameters:
Non-Sorting Genetic Algorithm II (NSGA II) - NSGAII’s individual framework (codification)
Real codification
Initial population: 100 individuals
Tournament method
Single crossover point method
Crossover rate: 0,45
Mutation operator: Normal Random
Mutation rate: 0,015
13
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

14. 3.Implementation
• 4 Occupants
• 7 Energy services to acquire (Lighting,
Air Conditioner, Washing Machine,
Dryer Machine, Oven, Refrigerator.
Dishwasher Machine)
• Available budget: 2700 €
Case study:
17 m
16
m
• Air conditioner and lighting are
dependent on house dimensions
14
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

15. 3.Implementation
2 Stopping criteria used (the first to be satisfied)
• Nr. Of iterations/generations: 100
• Convergence rate 3 x 10^-3
Software implementation: Matlab code, importing (pre-calculated) data from VBA Excel Spreadsheet
Optimization method
Electrical appliances 'Data-Base
Ms Excel Matlab code
𝑥𝑗
(𝑔𝑗𝑡)
𝑣𝑖𝑗 𝑥𝑗
𝑔𝑗𝑡
𝑥𝑖𝑗
15
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

16. 4.Simulation & Results
16
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
Pareto frontier for
different, regarding
different tests made for
NSGA II parameters
(Santos, 2019b): a)
Population b) Crossover
rate and mutation rate
Different Pareto frontiers
obtained, considering several
scenarios (Santos, 2019a)
Economical vs
Environmental (ωA = 0,72,
ωB = 0,00, ωC = 0,28)
Economical vs Social
(ωA = 0,72, ωB = 0,28,
ωC = 0,00)
Social vs Environmental
(ωA = 0,00, ωB = 0,72, ωC =
0,28)

17. 4.Simulation & Results
17
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
Pareto surface, regarding the last
generation (ωA = 0.65, ωB = 0.25
and ωC = 0.10)
Example of
sustainable
solution obtained -
Multiobjective (ωA
= 0.65, ωB = 0.25
and ωC = 0.10)
Pareto Surface

18. Example of an efficient solution
4.Simulation & Results
18
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal
(ωA = 0,65, ωB = 0,25 e ωC = 0,10)
Brand: Philips
Model: LED spotMV
Power: 8 W
Type: LED
Energy Eficiency.: A+
Light
Brand: Samsung
Model: AQV09PSBN
Cooling Power: 12000 Btu/h
Heating Power: 11000 Btu/h
Type: Split
Energy Eficiency.: A++
Air Conditioner
Brand: Philips
Model: LED spotMV
Power: 2400 W
Capacity: 12 sets
Noise: 48 dB
Energy Eficiency.: A++
Dish Washing Machine
Brand: Becken
Model: Bc2016 Ix
Power: 255 kW/ano
Capacity: 251 l
Type: Comb.
Nr. Stars: 4
Energy Eficiency.: A+
Fridge
Brand: Indesit
Model: EWE71252W
Noise: 60 dB
Capacity: 8kg
Energy Eficiency.:A++
Cloth Washing Machine
Electric Oven
Cloth Dryer Machine
CO2
savings per
appliance
Energy Savings
(Hor. Temp.: 10
years): 2112,30 €
Water savings
(Hor. Temp.: 10
years): 745 l
Total Investment
CO2 Emissions savings
(Hor. Temp.: 10 years):
1458,90 kg
Brand: Electrolux
Model: EZC2430AOX
Power: 2700 W
Cleaning Syst: Pirol.
Glass: Triple
Energy Eficiency.: A+
Brand: BOSCH
Model: WTE84107EE
Noise: 65 dB
Capacidade: 8kg
Type: Condensation
Energy Eficiency.: A+

19. • An approach was presented to provide sustainable household appliances from
the market, to the decision-agent (consumer), by considering two objectives,
regarding sustainability; environment and economic well-being.
• The main objective, was to maximize the consumer well-being (environment and
economics) regarding the lifecycle of each appliance, during its usage phase.
• The social wellbeing was also promoted, by suit the obtained solutions to the
consumer needs.
• NSGA-II can also find the Pareto frontier of the solutions, providing therefore
several alternative solutions to the consumer.
• The achievements presented in this work, allows to proceed in a way of getting a
more completed approach that maximizes both dimensions of sustainability
(Economical, Environmental and Social) with the social dimension, being
therefore integrated into the model developed here, in order to better express
the consumer preferences
5.Conclusions
19
Ricardo Santos | João Matias |Antonio Abreu
A decision-making tool to provide sustainable solutions to a consumer
11th Advanced Doctoral Conference on Computing,
Electrical and Industrial Systems
Technological Innovation for Life
Improvement 01-03 July, 2020 | Caparica, Lisbon – Portugal

20. 20
Ricardo Santos 1 Antonio Abreu 3
João Matias 2
01-03 July, 2020 | Caparica, Lisbon – Portugal
1 University of Aveiro, Portugal 2Dept. of Economics, Management, Industrial
Engineering and Tourism (DEGEIT)
University of Aveiro, Portugal
C-MasT – University of Beira Interior
Govcopp - University of Aveiro
3 ISEL- Instituto Superior de Engenharia de Lisboa,
Instituto Politécnico de Lisboa
CTS Uninova, Faculdade de Ciências e Tecnologia,
Universidade Nova de Lisboa, Portugal
ajfa@dem.isel.ipl.pt
Thank you
A decision-making tool to provide sustainable solutions to a consumer
Govcopp - University of Aveiro