Development of Multiobjective Genetic Algorithms for Agri-Food Supply Chain Design by Considering Global Climate Change
Decision Support Systems for Agriculture
and Agribusiness
AFITA 2010 International Conference, The Quality Information for Competitive Agricultural Based Production System and Commerce
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I. INTRODUCTION
1
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which impacts randomly and unpredictably on
decision making in every component of the chain.
The work proposed in this paper is aimed to
develop genetic algorithms for designing an agrifood supply chain by considering the effect of global
climate change.
Although, there were many research works on
or related to agri-food supply chains, most of them
used conventional techniques, such as linear
programming (Apaiah and Hendrix, 2004), dynamic
programming (Gigler, et.al 2002), mixed integer
linear programming (Gunnarson et.al, 2004) or
standard single/multi objective genetic algorithms
(Stewart et.al, 2004; Mardle and Pascoe, 2000;
Mayer et.al, 2001; Matthew et.al., 2005) which are
inappropriate for complex systems. A few recent
research works have used more advanced methods
such as agent based model (ABM) and Bayesian
Belief Network (BBN) such as reported by Bryceson
and Smith (2008), van der Vorst et.al (2007) and da
Silva and Filho (2007), but they did not consider
global climate change as an important factor in every
stage of the chains.
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Kenneth De Jong
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Department of Computer Science ‘s Evolutionary
Computation Laboratory
and Krasnow Institute for Advanced Study’s Adaptive
Systems Laboratory
George Mason University, USA
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Yandra Arkeman
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Artificial Intelligence Research Group
Division of Business and Industrial Application,
Department of Agroindustrial Technology (TIN)
Bogor Agricultural University (IPB), Indonesia
Email: yandra@ipb.ac.id
s
m
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.
B
a
c
k
g
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o
u
n
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Global climate change is becoming challenging
for us. It has a severe impact in almost every domain
of our lives, especially in agriculture and agroindustry. The impact of global climate change in
agriculture will affect the food supply in the world.
Thus, there is a need to study agri-food supply chain
with respect to global climate change for today’s and
tomorrow’s agricultural and agro-industrial systems.
Agri-food chains are complex systems involving
multiple multifaceted firms usually working together
within specific agro-industry to satisfy an
increasingly globalized market demand for high
value food products. In so doing, the groupings of
companies involved in an agri-food chain undertake
activities that require multidimensional, interorganizational and cross organizational decision
making in the process of adding value to a raw
commodity product through the production,
manufacturing and distribution stages of the chain.
Additional complexity is added by climate variability
1
.
2
.
O
b
j
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c
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i
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This research aims to develop a multi-objective
genetic algorithm to optimize Agri-food Supply Chain
optimization designs.
II. LITERATURE REVIEW
2
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One of the most recent studies on agri-food
supply chain in global climate change is reported by
Jacxsens et.al (2009). They presented a conceptual
approach to identify the impact of climate change on
microbiological food safety of fresh produce supply
AFITA 2010 International Conference, The Quality Information for Competitive Agricultural Based Production System and Commerce
chain by using simulation modeling and risk
assessment.
Despite the advantage of their
research, their proposed approach needs to be
enhanced so it can deal not only with microbiological
food safety but also with other aspects of agri-food
supply chain.
Another recent study by Sarker and Ray (2009)
reported the development of an improved
evolutionary algorithm for solving crop planning
model. They formulated a crop-planning model as a
multiobjective optimization model and solved it
using -constrained method, NSGA-II and their
proposed algorithm namely MCA (Multiobjective
Constrained Algorithm). The first objective of their
model is to maximize the total gross margin (from
cultivated plus imported crops) and the second
objective is to minimize total working capital
required.
Both objectives subject to some
constraints such as demand, land, capital,
contingent, area and import bound. Before that,
Matthews et.al (2006) used a combination of
deliberative and computer-based methods for multiobjective land-use planning. Two conflicting goals
were stated in their paper: to maximize financial
return and land-use diversity. The metric for
financial goal was the farm gross margin expressed
as a NPV over 60 years. The land-use diversity was
measured using Shannon-Wiener (SW) index that is
maximized when all potential land uses are present
in equal proportions. Despite the success of Sarker
et.al (2009) and Matthews et. al (2006) to use
multiobjective model and apply (new) evolutionary
algorithms, they only dealt with two objectives and
did not consider the global climate change factors.
In addition, Zhang et.al. (2009) developed a
fuzzy multiobjective model on paddy circular
economy system. They used lexicographic method
and genetic algorithm to optimize a three-objective
model for arrangement of paddy planting pattern.
This study tackled three objectives instead of two
and also considered global climate change factor,
but the optimization method used is still
conventional. Instead, a multiobjective evolutionary
algorithm can be used to tackle the problem. Before
that, there were some works that used various
methods such as agent based model (ABM) and
Bayesian Belief Network (BBN) such as reported by
Bryceson and Smith (2008), van der Vorst et.al
(2007) and da Silva and Filho (2007). Although they
have given a significant contribution to the
optimization field, they did not consider global
climate change as an important factor in every stage
of the chains.
2
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2
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Multi-objective optimization has been defined
as finding a vector of decision variables satisfying
constraints to give acceptable values to all objective
functions. In general, it can be mathematically
T
defined as: find the vector X* = [x1*, x2*, …,xn*] to
optimize
T
F(X) = [f1(X), f2(X), …fk(X)]
subject to m inequality constraints
gi(X) ≤ 0, i = 1, …, m
and p equality constraints
hj(X) = 0, j=1, …,p
n
(2.1)
(2.2)
(2.3)
is the vector of decision or design
where X*
k
is the vector of objective
variables, and F(X)
functions, which must each be either minimized or
maximized. A set of solution of a multi-objective
optimization problem (MOOP) is known as Pareto
optimal solutions or Pareto front.
Evolutionary algorithms have been widely used
for multi-objective optimization because of their
natural properties suited for these types of
problems. This is mostly because of their parallel or
population-based search approach. Therefore, most
of the difficulties and deficiencies within the classical
methods in solving multi-objective optimization
problems are eliminated. For example, there is no
need for either several runs to find all individuals of
the Pareto front or quantification of the importance
of each objective using numerical weights. In this
way, the original non-dominated sorting procedure
given by Goldberg (1989) was the catalyst for several
different versions of multi-objective optimization
algorithms. However, it is very important that the
genetic diversity within the population be preserved
sufficiently. This main issue in multi-objective
optimization problems has been addressed by many
related research works.
Consequently, the
premature convergence of MOEAs is prevented and
the solutions are directed and distributed along the
Pareto front if such genetic diversity is well provided.
The Pareto-based approach of NSGA-II (Deb, 2001)
has been used recently in a wide area of engineering
multi-objective problems because of its yet efficient
non-dominance ranking and crowding distance
procedure in yielding different level and diversity of
Pareto frontiers. The algorithm for NSGA-II is as
follow:
231
AFITA 2010 International Conference, The Quality Information for Competitive Agricultural Based Production System and Commerce
Pseudo Code of NSGA-II:
Create a random population Po
Sort the population into different nondominated levels. Each solution is assigned a
fitness equal to its non-domination level (1 is
the best level). Thus, minimization of the fitness
is assumed.
Create an offspring population Qo of size N by
using binary tournament selection (using
ranking and crowding distance as criteria for
winning the tournament), recombination and
mutation.
Combine parent and offspring populations and
create Rt = Pt Qt. Perform a non-dominated
sorting to Rt and identify different fronts: Fi, i =
1,2, …, etc.
Set new population Pt+1 = 0. Set a counter i = 1.
Until |Pt+1|+|Fi| < N, perform Pt+1 = Pt+1 Fi and i
= i + 1.
Perform the Crowding-sort (Fi,
and Agribusiness
AFITA 2010 International Conference, The Quality Information for Competitive Agricultural Based Production System and Commerce
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I. INTRODUCTION
1
.
1
a
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l
m
C
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s
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a
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A
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which impacts randomly and unpredictably on
decision making in every component of the chain.
The work proposed in this paper is aimed to
develop genetic algorithms for designing an agrifood supply chain by considering the effect of global
climate change.
Although, there were many research works on
or related to agri-food supply chains, most of them
used conventional techniques, such as linear
programming (Apaiah and Hendrix, 2004), dynamic
programming (Gigler, et.al 2002), mixed integer
linear programming (Gunnarson et.al, 2004) or
standard single/multi objective genetic algorithms
(Stewart et.al, 2004; Mardle and Pascoe, 2000;
Mayer et.al, 2001; Matthew et.al., 2005) which are
inappropriate for complex systems. A few recent
research works have used more advanced methods
such as agent based model (ABM) and Bayesian
Belief Network (BBN) such as reported by Bryceson
and Smith (2008), van der Vorst et.al (2007) and da
Silva and Filho (2007), but they did not consider
global climate change as an important factor in every
stage of the chains.
a
e
a
e
t
t
y
l
.
h
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m
t
r
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u
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e
Kenneth De Jong
m
o
j
Department of Computer Science ‘s Evolutionary
Computation Laboratory
and Krasnow Institute for Advanced Study’s Adaptive
Systems Laboratory
George Mason University, USA
o
m
b
m
v
c
t
o
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e
f
s
i
Yandra Arkeman
t
l
e
t
Artificial Intelligence Research Group
Division of Business and Industrial Application,
Department of Agroindustrial Technology (TIN)
Bogor Agricultural University (IPB), Indonesia
Email: yandra@ipb.ac.id
s
m
l
.
B
a
c
k
g
r
o
u
n
d
Global climate change is becoming challenging
for us. It has a severe impact in almost every domain
of our lives, especially in agriculture and agroindustry. The impact of global climate change in
agriculture will affect the food supply in the world.
Thus, there is a need to study agri-food supply chain
with respect to global climate change for today’s and
tomorrow’s agricultural and agro-industrial systems.
Agri-food chains are complex systems involving
multiple multifaceted firms usually working together
within specific agro-industry to satisfy an
increasingly globalized market demand for high
value food products. In so doing, the groupings of
companies involved in an agri-food chain undertake
activities that require multidimensional, interorganizational and cross organizational decision
making in the process of adding value to a raw
commodity product through the production,
manufacturing and distribution stages of the chain.
Additional complexity is added by climate variability
1
.
2
.
O
b
j
e
e
c
t
i
v
This research aims to develop a multi-objective
genetic algorithm to optimize Agri-food Supply Chain
optimization designs.
II. LITERATURE REVIEW
2
.
1
.
L
e
i
t
e
r
a
S
t
l
u
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C
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i
-
f
o
h
o
d
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a
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One of the most recent studies on agri-food
supply chain in global climate change is reported by
Jacxsens et.al (2009). They presented a conceptual
approach to identify the impact of climate change on
microbiological food safety of fresh produce supply
AFITA 2010 International Conference, The Quality Information for Competitive Agricultural Based Production System and Commerce
chain by using simulation modeling and risk
assessment.
Despite the advantage of their
research, their proposed approach needs to be
enhanced so it can deal not only with microbiological
food safety but also with other aspects of agri-food
supply chain.
Another recent study by Sarker and Ray (2009)
reported the development of an improved
evolutionary algorithm for solving crop planning
model. They formulated a crop-planning model as a
multiobjective optimization model and solved it
using -constrained method, NSGA-II and their
proposed algorithm namely MCA (Multiobjective
Constrained Algorithm). The first objective of their
model is to maximize the total gross margin (from
cultivated plus imported crops) and the second
objective is to minimize total working capital
required.
Both objectives subject to some
constraints such as demand, land, capital,
contingent, area and import bound. Before that,
Matthews et.al (2006) used a combination of
deliberative and computer-based methods for multiobjective land-use planning. Two conflicting goals
were stated in their paper: to maximize financial
return and land-use diversity. The metric for
financial goal was the farm gross margin expressed
as a NPV over 60 years. The land-use diversity was
measured using Shannon-Wiener (SW) index that is
maximized when all potential land uses are present
in equal proportions. Despite the success of Sarker
et.al (2009) and Matthews et. al (2006) to use
multiobjective model and apply (new) evolutionary
algorithms, they only dealt with two objectives and
did not consider the global climate change factors.
In addition, Zhang et.al. (2009) developed a
fuzzy multiobjective model on paddy circular
economy system. They used lexicographic method
and genetic algorithm to optimize a three-objective
model for arrangement of paddy planting pattern.
This study tackled three objectives instead of two
and also considered global climate change factor,
but the optimization method used is still
conventional. Instead, a multiobjective evolutionary
algorithm can be used to tackle the problem. Before
that, there were some works that used various
methods such as agent based model (ABM) and
Bayesian Belief Network (BBN) such as reported by
Bryceson and Smith (2008), van der Vorst et.al
(2007) and da Silva and Filho (2007). Although they
have given a significant contribution to the
optimization field, they did not consider global
climate change as an important factor in every stage
of the chains.
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Multi-objective optimization has been defined
as finding a vector of decision variables satisfying
constraints to give acceptable values to all objective
functions. In general, it can be mathematically
T
defined as: find the vector X* = [x1*, x2*, …,xn*] to
optimize
T
F(X) = [f1(X), f2(X), …fk(X)]
subject to m inequality constraints
gi(X) ≤ 0, i = 1, …, m
and p equality constraints
hj(X) = 0, j=1, …,p
n
(2.1)
(2.2)
(2.3)
is the vector of decision or design
where X*
k
is the vector of objective
variables, and F(X)
functions, which must each be either minimized or
maximized. A set of solution of a multi-objective
optimization problem (MOOP) is known as Pareto
optimal solutions or Pareto front.
Evolutionary algorithms have been widely used
for multi-objective optimization because of their
natural properties suited for these types of
problems. This is mostly because of their parallel or
population-based search approach. Therefore, most
of the difficulties and deficiencies within the classical
methods in solving multi-objective optimization
problems are eliminated. For example, there is no
need for either several runs to find all individuals of
the Pareto front or quantification of the importance
of each objective using numerical weights. In this
way, the original non-dominated sorting procedure
given by Goldberg (1989) was the catalyst for several
different versions of multi-objective optimization
algorithms. However, it is very important that the
genetic diversity within the population be preserved
sufficiently. This main issue in multi-objective
optimization problems has been addressed by many
related research works.
Consequently, the
premature convergence of MOEAs is prevented and
the solutions are directed and distributed along the
Pareto front if such genetic diversity is well provided.
The Pareto-based approach of NSGA-II (Deb, 2001)
has been used recently in a wide area of engineering
multi-objective problems because of its yet efficient
non-dominance ranking and crowding distance
procedure in yielding different level and diversity of
Pareto frontiers. The algorithm for NSGA-II is as
follow:
231
AFITA 2010 International Conference, The Quality Information for Competitive Agricultural Based Production System and Commerce
Pseudo Code of NSGA-II:
Create a random population Po
Sort the population into different nondominated levels. Each solution is assigned a
fitness equal to its non-domination level (1 is
the best level). Thus, minimization of the fitness
is assumed.
Create an offspring population Qo of size N by
using binary tournament selection (using
ranking and crowding distance as criteria for
winning the tournament), recombination and
mutation.
Combine parent and offspring populations and
create Rt = Pt Qt. Perform a non-dominated
sorting to Rt and identify different fronts: Fi, i =
1,2, …, etc.
Set new population Pt+1 = 0. Set a counter i = 1.
Until |Pt+1|+|Fi| < N, perform Pt+1 = Pt+1 Fi and i
= i + 1.
Perform the Crowding-sort (Fi,