=Paper=
{{Paper
|id=None
|storemode=property
|title=Constraint and Optimization techniques for supporting Policy Making
|pdfUrl=https://ceur-ws.org/Vol-1068/paper-l13.pdf
|volume=Vol-1068
|dblpUrl=https://dblp.org/rec/conf/cilc/GavanelliRMC13
}}
==Constraint and Optimization techniques for supporting Policy Making==
https://ceur-ws.org/Vol-1068/paper-l13.pdf
Constraint and Optimization techniques for
supporting Policy Making?
Marco Gavanelli1 , Fabrizio Riguzzi1 , Michela Milano2 , and Paolo Cagnoli3
1
University of Ferrara
Via Saragat 1, 44122 Ferrara Italy
2
University of Bologna
V.le Risorgimento 2, 40136, Bologna, Italy
3
ARPA Emilia-Romagna
Bologna, Italy
Abstract. Modeling the policy making process is a very challenging
task. To the best of our knowledge the most widely used technique in
this setting is agent-based simulation. Each agent represents an individ-
ual entity (e.g., citizen, stakeholder, company, public association, public
body). The agent interaction enables emerging behaviours to be observed
and taken into account in the policy making process itself. We claim that
another perspective should be considered in modeling policy issues, that
is the global perspective. Each public body has global objectives, con-
straints and guidelines that have to be combined to take decisions. The
policy making process should be at the same time consistent with con-
straints, optimal with respect to given objectives and assessed to avoid
negative impacts on the environment, economy and society. We propose
in this paper a constraint-based model for the global policy making pro-
cess and we apply the devised model to the regional planning activity.
A case study in the field of energy plan is used to evaluate the proposed
model. Clearly an interaction with agent-based simulation is desirable
and could provide important feedback to the global model. This aspect
is the subject of current research.
1 The problem
Public policy issues are extremely complex, occur in rapidly changing environ-
ments characterized by uncertainty, and involve conflicts among different inter-
ests. Our society is ever more complex due to globalisation, enlargement and the
changing geo-political situation. This means that political activity and interven-
tion become more widespread, and so the effects of its interventions become more
difficult to assess, while at the same time it is becoming ever more important
to ensure that actions are effectively tackling the real challenges that this in-
creasing complexity entails. Thus, those responsible for creating, implementing,
and enforcing policies must be able to reach decisions about ill-defined problem
situations that are not well understood, have no single correct answer, involve
?
An extended version of this paper appeared in [9].
196 Marco Gavanelli, Fabrizio Riguzzi, Michela Milano and Paolo Cagnoli
many competing interests and interact with other policies at multiple levels.
It is therefore increasingly important to ensure coherence across these complex
issues.
In this paper we consider policy issues related to regional planning, the sci-
ence of the efficient placement of activities and infrastructures for the sustainable
growth of a region. Regional plans are classified into types, such as Agriculture,
Forest, Fishing, Energy, Industry, Transport, Waste, Water, Telecommunication,
Tourism, Urban Development and Environment to name a few. Each plan de-
fines activities that should be carried out during the plan implementation. On
the regional plan, the policy maker has to take into account impacts on the
environment, the economy and the society. The procedure aimed to assess the
impacts of a regional plan is called Strategic Environmental Assessment [15] and
relates activities defined in the plan to environmental and economic impacts.
This assessment procedure is now manually implemented by environmental ex-
perts, but it is never applied during the plan/program construction. In addition,
this procedure is applied on a given, already instantiated plan. Taking into ac-
count impacts a posteriori enables only corrective interventions that can at most
reduce the negative effect of wrong planning decisions.
One important aspect to be considered for supporting policy makers with
Computational Intelligence approaches is the definition of formal policy models.
In the literature, the majority of policy models rely on agent based simulation
[11, 14, 18] where agents represent the parties involved in the decision making and
implementation process. The idea is that agent-based modeling and simulation is
suitable for modeling complex systems. In particular, agent-based models permit
carrying out computer experiments to support a better understanding of the
complexity of economic, environmental and social systems, structural changes,
and endogenous adjustment reactions in response to a policy change.
In addition to agent-based simulation models, which provide “individual level
models”, we claim that the policy planning activity needs a global perspective:
in the case of regional planning, we need “a regional perspective” that faces
the problem at a global level while tightly interacting with the individual level
model. Thus rather than proposing an alternative approach with respect to sim-
ulation, we claim that the two approaches should be properly combined as they
represent two different perspectives of the same problem: the individual and
the global perspective. This integration is the subject of our current research
activity. In this setting, regional planning activities can be cast into complex
combinatorial optimization problems. The policy maker has to take decisions
satisfying a set of constraints while at the same time achieving a set of (possibly
conflicting) objectives such as reducing negative impacts and enhancing positive
impacts on the environment, the society and the economy. For this reason, im-
pact assessment should be integrated into the policy model so as to improve the
current procedure performed a posteriori.
In previous work [8], we experimented two different technologies to address
the Strategic Environmental Assessment (SEA) of a regional plan. The tech-
nologies we applied were Constraint Logic Programming (CLP) [13] and Causal
Constraint and Optimization techniques for supporting Policy Making 197
Probabilistic Logic Programming [19]; Logic Programming is common to both
models, so the user could use one or the other from a same environment, and
possibly hybridize them. In [10] we proposed a fuzzy model for the SEA. While
being far more expressive than a traditional CLP approach, it is less usable
within a Regional planning decision support system. We evaluated a previous
regional plan with the two models, and proposed the outputs to an environmen-
tal expert. The expert compared the two outputs and chose the CLP model as
the closest to a human-made assessment.
In this work, we extend the CLP model used for the assessment, and apply it
to the planning problem, i.e., deciding which actions should be taken in a plan.
In the model, decision variables represent political decisions (e.g., the magnitude
of a given activity in the regional plan), potential outcomes are associated with
each decision, constraints limit possible combination of assignments of decision
variables, and objectives (also referred to as criteria) can be either used to evalu-
ate alternative solutions or translated into additional constraints. The model has
been solved with CLP [13] techniques, and tested on the Emilia-Romagna re-
gional energy plan. The results have been validated by experts in policy making
and impact assessment to evaluate the accuracy of the results.
Further constraint based approaches have been proposed for narrower prob-
lems in the field of energy, such as locating biomass power plants in positions
that are both economically affordable [6, 2, 5] and environmentally sustainable
[4]. Other approaches have been applied to wind turbine placement [12]. The
problem faced in this paper is much broader, as the Region should decide which
strategic investments to perform in the next two-three years (with a longer vision
to 2020) in the energy field. All specific details are left to the implementation
of the plan, but are not considered at the Regional Planning stage. To the best
of our knowledge, this is the first time constraint-based reasoning is applied to
such a wide and strategic perspective.
1.1 Regional Planning and Impact assessment
Regional Planning is the result of the main policy making activity of European
regions. Each region has a budget distributed by the Operational Programme
(OP): an OP sets out each region’s priorities for delivering the funds. On the
basis of these funds, the region has to define its priorities: in the field of energy,
one example of priority is increasing the use of renewable energy sources. Then,
a region should decide which activities to insert in the plan. Activities may be
roughly divided into six types: infrastructures and plants; buildings and land use
transformations; resource extraction; modifications of hydraulic regime; indus-
trial transformations; environmental management. Also, a magnitude for each
activity should be decided describing how much of a given activity is performed.
Each activity has an outcome (such as the amount of energy produced or con-
sumed) and a cost. We have two vectors O = (o1 , . . . , oNa ) and C = (c1 , . . . , cNa )
where each element is associated to a specific activity and represents the outcome
and cost per unit of an activity.
198 Marco Gavanelli, Fabrizio Riguzzi, Michela Milano and Paolo Cagnoli
There are constraints linking activities: for instance if a regional plan decides
to build three biomass power plants (primary activities for an energy plan), each
of these plants should be equipped with proper infrastructures (streets, sewage
or possibly a small village nearby, power lines) also called secondary activities.
We thus have a matrix of dependencies between activities. In particular, we have
a Na × Na square matrix D where each element dij represents the magnitude of
activity j per unit of activity i.
Taking as an example the Emilia-Romagna Regional Energy Plan approved in
2007, some objectives of the policy makers are the production of a given amount
of energy (400 additional MW from renewable energy sources), while reducing
the current greenhouse gas emission percentage by 6.5% with respect to 2003. In
addition, the budget constraint limiting the amount of money allocated to the
energy plan by the Regional Operational Programme was 30.5Me in 2007.
The policy maker also takes into account impacts on the environment, the
economy and the society, as defined by a Strategic Environmental Assessment
that relates activities defined in the plan to environmental and economic impacts.
Each activity has impacts on the environment in terms of positive and negative
pressures. An example of positive pressure is the increased availability of energy,
while an example of a negative pressure is the production of pollutants. Pressures
are further linked to environmental receptors such as the quality of the air or
of surface water. On both pressures and receptors, there are constraints: for
example the maximum amount of greenhouse gas emissions of the overall plan.
One of the instruments used for assessing a regional plan in Emilia-Romagna
are the so called coaxial matrices [3], a development of the network method [17].
One matrix M defines the dependencies between the above mentioned ac-
tivities impacts (also called pressures) on the environment. Each element mij of
the matrix M defines a qualitative dependency between the activity i and the
impact j. The dependency can be high, medium, low or null. Examples of nega-
tive impacts are energy, water and land consumption, variation of water flows,
water and air pollution and so on. Examples of positive impacts are reduction
of water/air pollution, reduction of greenhouse gas emission, reduction of noise,
natural resources saving, creation of new ecosystems etc.
The second matrix N defines the dependencies between the impacts and en-
vironmental receptors. Each element nij of the matrix N defines a qualitative
dependency between the impact i and an environmental receptor j. Again the
dependency can be high, medium, low or null. Examples of environmental recep-
tors are the quality of surface water and groundwater, the quality of landscapes,
energy availability, wildlife wellness and so on.
The matrices used in Emilia-Romagna contain 93 activities, 29 negative im-
pacts, 19 positive impacts and 23 receptors, and assess 11 types of plans.
2 Why constraint based approaches
The regional planning activity is now performed by human experts that build
a single plan, considering strategic regional objectives that follow national and
Constraint and Optimization techniques for supporting Policy Making 199
EU guidelines. After the plan has been devised, the agency for environmental
protection is asked to assess the plan from an environmental point of view. Typ-
ically, there is no feedback: the assessment can state that the devised plan is
environmentally friendly or not, but it cannot change the plan. In rare cases,
it can propose corrective countermeasures, that can only mitigate the negative
impact of wrong planning decisions. Moreover, although regulations state that a
significant environmental assessment should compare two or more options (dif-
ferent plans), this is rarely done in Europe, because the assessment is typically
hand made and requires a long work. Even in the few cases in which two options
are considered, usually one is the plan and the other is the absence of a plan.
Constraint based modeling overcomes the limitation of a hand made process
for a number of reasons. First, it provides a tool that automatically performs
planning decisions, considering both the budget allocated to the plan by the
Regional Operative Plan, and national/EU guidelines.
Second, it takes environmental aspects into consideration during plan con-
struction, avoiding trial-and-error schemes.
Third, constraint reasoning provides a powerful tool in the hand of a policy
maker as the generation of alternative scenarios is extremely easy and their
comparison and evaluation comes for free. Adjustments can be performed on-
the-fly in the case that the results do not satisfy policy makers or environmental
experts. For example, in the field of energy regional plan, by changing the bounds
on the amount of energy each source can provide, we can adjust the plan by
considering market trends and also the potential receptivity of the region.
3 A CLP model
To design a constraint-based model for the regional planning activity, we have
to define variables, constraints and objectives. Variables represent decisions that
have to be taken. Given a vector of activities A = (a1 , . . . , aNa ), we associate to
each activity a variable Gi that defines its magnitude. The magnitude could be
represented either in an absolute way, as the amount of a given activity, or in a
relative way, as a percentage with respect to the existing quantity of the same
activity. We use in this paper the absolute representation.
As stated above, we distinguish primary from secondary activities: let AP be
the set of indexes of primary activities and AS the set of indexes of secondary
activities. The distinction is motivated by the fact that some activities are of
primary importance in a given plan. Secondary activities are those supporting
the primary activities by providing the needed infrastructures. The dependencies
between primary and secondary activities are considered by the constraint:
X
∀j ∈ AS Gj = dij Gi
i∈AP
200 Marco Gavanelli, Fabrizio Riguzzi, Michela Milano and Paolo Cagnoli
Given a budget BP lan available for a given plan, we have a constraint limiting
the overall plan cost as follows
Na
X
Gi ci ≤ BP lan (1)
i=1
Such constraint can be imposed either on the overall plan or on parts of it. For
example, if the budget is partitioned into chapters, we can impose constraint (1)
on activities of a given chapter.
Moreover, given an expected outcome oP lan of the plan, we have a constraint
ensuring to reach the outcome:
Na
X
Gi oi ≥ oP lan .
i=1
For example, in an energy plan the outcome can be to have more energy
available in the region, so oP lan could be the increased availability of electrical
power (e.g., in kilo-TOE, Tonnes of Oil Equivalent). In such a case, oi will be
the production in kTOE for each unit of activity ai .
Concerning the impacts, we sum up the contributions of all the activities and
obtain the estimate of the impact on each environmental pressure:
Na
X
∀j ∈ {1, . . . , Np } pj = mij Gi . (2)
i=1
The qualitative values in the matrices have been converted into quantitative
values mij in the [0, 1] range for positive impacts and in the [−1, 0] range for
negative ones. The actual values were suggested by an environmental expert.
In the same way, given the vector of environmental pressures P = (p1 , . . . , pNp ),
one can estimate their influence on the environmental receptor ri by means of
the matrix N , that relates pressures with receptors:
Np
X
∀j ∈ {1, . . . , Nr } rj = nij pi . (3)
i=1
Moreover we can have constraints on receptors and pressures. For example,
“Greenhouse gas emission” (that is a negative pressure) should not exceed a
given threshold.
Concerning objectives, there are a number of possibilities suggested by plan-
ning experts. From an economic perspective, one can decide to minimize the
overall cost of the plan (that is anyway subject to budget constraints). Clearly
in this case, the most economic energy sources are preferred, despite their poten-
tially negative environmental effects (which could be anyway constrained). On
the other hand, one could maintain a fixed budget and maximize the produced
energy. In this case, the most efficient energy sources will be pushed forward.
Constraint and Optimization techniques for supporting Policy Making 201
Or the planner could prefer a green plan and optimize environmental receptors.
For example, one can maximize, say, the air quality, or the quality of the surface
water. In this case, the produced plan decisions are less intuitive and the system
we propose is particularly useful. The link between decisions on primary and sec-
ondary activities and consequences on the environment are extremely complex
to be manually considered. Clearly, more complex objectives can be pursued, by
properly combining the above mentioned aspects.
3.1 The regional energy plan
We can now describe how to cast the general model for regional planning de-
scribed above into the model for designing a regional energy plan. The first step
is to identify primary and secondary activities. In the context of a regional energy
plan, the environmental and planning experts defined the following distinction.
Primary activities are those capable of producing energy, namely renewable and
non-renewable power plants. Secondary activities are those supporting the en-
ergy production, such as activities for energy transportations (e.g., power lines),
and infrastructures supporting the primary activities (e.g., dams, yards).
One important aspect to be taken into account when designing a regional
energy plan is the energy source diversification: this means that funds should
not be directed toward a single energy source, but should cover both renewable
and non renewable energy sources. This requirement comes from fluctuations
of the price and availability of the various resources. For this reason, we have
constraints on the minimal fraction Fi of the total energy produced by each
source i:
∀i ∈ AP Gi oi ≥ Fi T o
where the total outcome T o is simply obtained as
X
To = Gj oj .
j∈AP
In addition, each region has its own geo-physical characteristics. For instance,
some regions are particularly windy, while some others are not. Hydroelectric
power plants can be built with a very careful consideration of environmental
impacts, the most obvious being the flooding of vast areas of land. This poses
constraints on the maximum energy Ui that can be produced by a given energy
source i
∀i ∈ AP Gi oi ≤ Ui .
Finally, the region priorities should be compliant with European guidelines, such
as the 20-20-20 initiative, that aims at achieving three ambitious targets by 2020:
reducing by 20% greenhouse gas emissions, having a 20% share of the final energy
consumption produced by renewable sources, and improving by 20% its energy
efficiency. For this reason, we can impose constraints on the minimum amount
of energy Lren produced by renewable energy sources whose set of activities is
202 Marco Gavanelli, Fabrizio Riguzzi, Michela Milano and Paolo Cagnoli
referred to as AP ren . The constraint that we can impose is
X
Gi oi ≥ Lren .
i∈AP ren
4 The Regional Energy Plan 2011-2013
The constraint-based model described in previous sections has been used in the
planning of the regional energy plan for 2011-2013. The system is implemented
in the Constraint Logic Programming language ECLi PSe [1], and in particular
uses its Eplex library [16], that interfaces ECLi PSe with a (mixed-integer) linear
programming solver. Nowadays, linear solvers are able to solve problems with
millions of variables, while our problem is much smaller (see end of Section 1.1).
In fact, the computation time was hardly measurable on a modern computer.
The regional energy plan had the objective of paving the way to reach the
ambitious goal of the 20-20-20 directive, in particular having 20% of energy in
2020 produced by renewable sources. This amount does not consider only electric
power, but the whole energy balance in the region, including thermal energy, and
transports.
Transports can use renewable energy by using renewable fuels, like biogas
(methane produced from the fermentation of vegetable or animal wastes) or oil
produced from various types of crops. Currently, we do not consider this issue.
Thermal energy can be used e.g. for home heating; renewable sources in
this case are thermal solar panels (that produce hot water for domestic use),
geothermal pumps (that are used to heat or to refresh houses), biomass plants,
that produce hot water used to heat neighboring houses during winter.
The considered electric power plants that produce energy from renewable
sources are hydroelectric plants, photovoltaic plants, thermodynamic solar plants,
wind generators and, again, biomass power plants.
For each energy source, the plan should provide: the installed power, in MW;
the total energy produced in a year, in kTOE (TOE stands for Tonne of Oil
Equivalent); the total cost, in Me. The ratio between installed power and total
produced energy is mainly influenced by the availability of the source: while a
biomass plant can (at least in theory) produce energy 24/7, the sun is available
only during the day, and the wind only occasionally. For unreliable sources an
average for the whole year is taken.
The cost of the plant, instead, depends mainly on the installed power: a solar
plant has an installation cost that depends on the square meters of installed
panels, which on their turn can provide some maximum power (peak power).
It is worth noting that the considered cost is the total cost of the plant for
the regional system, which is not the same as the cost for the taxpayers of the
Emilia-Romagna region. In fact, the region can enforce policies in many ways,
convincing private stakeholders to invest in power production. This can be done
with financial leverage, or by giving favorable conditions (either economic or
other) to investors. Some power sources are economically profitable, so there is
Constraint and Optimization techniques for supporting Policy Making 203
no need for the region to give subsidies. For example, currently in Italy biomasses
are economically advantageous for investors, so privates are proposing projects to
build biomasses plants. On the other hand, biomasses also produce pollutants,
they are not always sustainable (see [4] for a discussion) so local committees
are rather likely to spawn a protest against the construction of new plants. For
these reasons, there is a limit on the number of licenses the region gives to private
stakeholders for building biomass-based plants.
Technicians in the region estimated (considering current energy requirements,
growth trends, foreseen energy savings) the total energy requirements for 2020;
out of this, 20% should be provided by renewable sources. They also proposed
for this amount a percentage to be provided during the plan 2011-2013: about
177kTOE of electrical energy and 296kTOE of thermal energy.
Starting from these data, they developed a plan for electrical energy and one
for thermal energy.
We used the model presented in Section 3 considering initially only “ex-
treme” cases, in which only one type of energy source is used. The application
provides the optimal plan, together with its environmental assessment, namely
an evaluation of the environmental receptors used by the environmental protec-
tion agency.
In order to understand the individual contributions of the various energy
forms, we plotted all the plans that use a single type of energy in Figure 1,
together with the plan developed by the region’s experts (we consider here elec-
trical energy sources). On the x-axis, we chose the receptor Air quality because
it is probably the most sensitive receptor in the Emilia-Romagna region. On the
y-axis we plotted the cost of the plan. As explained previously, all plans provide
the same energy in kTOE, while they can require different installation power (in
MW).
First of all, we notice that some of the energy types improve the air qual-
ity (positive values on the x-axis), while others worsen it (negative values). Of
course, no power plant can improve the air quality by itself (as it cannot remove
pollutants from the air). The point is that the plant provides electrical energy
without introducing new pollutants; if such energy would not have been pro-
vided to the electrical network, it would have been imported from neighboring
regions. In such a case, the required energy would be produced with the same
mixture of energy sources as in the national production, including those emitting
pollutants, so the net contribution is positive for the air quality. Note also that
the different energy sources have different impacts on the air quality not only
due to the emissions of the power plants, but also to the impact of the secondary
activities required by the various sources.
Finally, the “extreme” plans are usually not feasible, in the sense that the
constraint on the real availability of the energy source in the region was relaxed.
For example, wind turbines provide a very good air quality at a low cost, but the
amount required in the corresponding extreme plan is not possible in the region
considering the average availability of wind and of land for installing turbines.
204 Marco Gavanelli, Fabrizio Riguzzi, Michela Milano and Paolo Cagnoli
Fig. 1. Plot of the extreme plans using only one energy source, compared with the plan
by the region’s experts.
The plan proposed by the region’s experts is more balanced: it considers the
real availability of the energy source in the region, and provides a mixture of
all the different renewable types of energy. This is very important in particular
for renewable sources, that are often discontinuous: wind power is only available
when the wind is blowing at a sufficient speed, solar power is only available
during sunny days, etc., so having a mixture of different sources can provide an
energy availability more continuous during the day.
Beside assessing the plan proposed by the experts, we also provided new,
alternative plans. In particular, we searched for optimal plans, both with respect
to the cost, and to the air quality. Since we have two objective functions, we
plotted the Pareto-optimal frontier: each point of the frontier is a point such
that one cannot improve one of the objectives without sacrificing the other.
In our case, the air quality cannot be improved without raising the cost, and,
vice-versa, it is impossible to reduce the cost without sacrificing the air quality.
The Pareto frontier is shown in Figure 2, together with the experts’ plan. The
objective function is a weighted sum of single criteria so our formulation of the
problem is linear and we can compute the Pareto frontier by changing coefficients
in the weighted sum.
The picture shows that, although the plan devised by the experts is close to
the frontier, it can be improved. In particular, we identified on the frontier two
solutions that dominate the experts’ plan: one has the same cost, but better air
quality, while the other has same air quality, but a lower cost.
Table 1 contains the plan developed by the region’s experts, while Table 2
shows the plan on the Pareto curve that has the same air quality as the plan
of the experts. The energy produced by wind generators is almost doubled (as
they provide a very convenient ratio (air quality)/cost, see Figure 1), we have a
Constraint and Optimization techniques for supporting Policy Making 205
Fig. 2. Pareto frontier of the air quality against cost.
Power 2010 Power 2013 Energy 2013 Investments
Electrical power plants (MW) (MW) (kTOE) (Me)
Hydroelectric 300 310 69.3 84
Photovoltaic 230 850 87.7 2170
Thermodyn. solar 0 10 1 45
Wind generators 20 80 10.3 120
Biomasses 430 600 361.2 595
Total 980 1850 529.5 3014
Table 1. Energy plan developed by the region’s experts
slight increase in the cheap biomass energy, while the other energy sources reduce
accordingly. Extracting plans from the solution of the CLP model is trivial: we
simply extract the values assigned to decision variables by the linear solver.
Concerning the environmental assessment, we plot in Figure 3 the value of
the receptors in significant points of the Pareto front. Each bar represents a
single environmental receptor for a specific plan on the Pareto frontier of Fig-
ure 2. In this way it is easy to compare how receptors are impacted by different
plans. In the Figure, the white bar is associated to the plan, on the frontier,
that has the highest air quality, while bars with dark colors are associated to
plans that have a low cost (and, thus, a low quality of the air). Notice that the
receptors have different trends: some of them improve as we move in the fron-
tier towards higher air quality (like climate quality, mankind wellness, value of
material goods), while others improve when moving to less expensive solutions
(like quality of sensitive landscapes, wellness of wildlife, soil quality). This is due
to several reasons, depending both on the type of power plants installed and on
the secondary activities.
206 Marco Gavanelli, Fabrizio Riguzzi, Michela Milano and Paolo Cagnoli
Power 2010 Power 2013 Energy 2013 Investments
Electrical power plants (MW) (MW) (kTOE) (Me)
Hydroelectric 300 303 67.74 25.2
Photovoltaic 230 782.14 80.7 1932.51
Thermodyn. solar 0 5 0.5 22.5
Wind generators 20 140 18.03 240
Biomasses 430 602.23 362.54 602.8
Total 980 1832.37 529.5 2823
Table 2. Energy plan that dominates the experts’ plan, retaining same air quality but
with lower cost
5 Added value of CLP
The application (including both the assessment and the planning) was developed
in few person-months by a CLP expert. It does not have a graphical user inter-
face yet, and it is currently usable only by CLP experts; however it produces
spreadsheet files with tables having the same structure as those used for years by
the region’s experts, so the output is easily understandable by the end user. We
are currently developing a web-based application, to let users input the relevant
data, and try themselves producing plans on-the-fly.
The assessment module [8] was first tested on a previously developed plan,
then used during the planning of the 2011-2013 regional energy plan. The various
alternatives have been submitted to the regional council, that could choose one
of them, instead of accepting/rejecting the only proposal, as in previous years.
One of the results is the ability to generate easily alternative plans with their
assessment; this is required by the EU regulations, but it is widely disregarded.
Another result is the possibility to provide plans that are optimal; the op-
timization criteria can include the cost, or one of the various environmental
receptors. The user can select two objectives, and in this case the application
generates a Pareto front. This helps the experts or the regional council in doing
choices that are more grounded.
We still do not know which plan the regional council will choose, neither
we know if and how the directives given in the regional plan will be indeed
implemented. More refined plans (at the province or municipality level) should
follow the guidelines in the regional plan, but it is also possible to introduce
modifications during the plan execution. However, in a perfect world, in which
everything is implemented as expected, the added value of CLP in monetary
terms could be the difference of the investment columns in the plans in Tables 1
and 2: 191Me saved (by the various actors, public and private, in the whole
region) in three years.
Finally, the choice of Constraint Programming greatly enables model flexi-
bility. Discussing with experts, it is often the case that they change their mind
on some model constraints or on objectives. Therefore, the flexibility in deal-
ing with side constraints and in dealing with non linear constraints facilitates
Constraint and Optimization techniques for supporting Policy Making 207
Fig. 3. Value of the receptors on the Pareto front
knowledge acquisition making Constraint Programming the technique of choice
for the problem and its future extensions.
6 Conclusion and Future Open Issues
Global public policy making is a complex process that is influenced by many
factors. We believe that the use of constraint reasoning techniques could greatly
increase the effectiveness of the process, by enabling the policy maker to analyze
various aspects and to play with parameters so as to obtain alternative solu-
tions along with their environmental assessment. Given the amount of financial,
human and environmental resources that are involved in regional plans, even a
small improvement can have a huge effect.
Important features of the system are: its efficiency, as a plan is returned in
few milliseconds, its wide applicability to many regional plans, to provincial and
urban plans and also to private and public projects. The system was used for
the environmental assessment of the regional energy plan of the Emilia-Romagna
region of Italy. Beside performing automatically the assessment (that was per-
formed by hand in previous years), the assessment for the first time includes the
evaluation of alternative plans: this is a requirement of EU regulations that is
largely disregarded in practice. Moreover, the alternative plans were produced
208 Marco Gavanelli, Fabrizio Riguzzi, Michela Milano and Paolo Cagnoli
by optimizing the quality of the environmental receptors, together with the cost
for the community of the plan itself.
This work is a first step towards a system that fully supports the decision
maker in designing public policies. To achieve this objective, the method must be
extended to take into account the individual level, by investigating the effect of
a policy over the parties affected by it. This can be achieved by integrating con-
straint reasoning with simulation models that reproduce the interactions among
the parties. In our current research, we are studying how the region can choose
the form of incentives and calibrate them in order to push the energy market to
invest in the directions foreseen by the Regional Energy plan [7].
In turn these models can be enriched by adopting e-Participation tools that
allow citizens and stakeholders to voice their concerns regarding policy decisions.
To fully leverage e-Participation tools, the system must also be able to extract in-
formation from all the available data, including natural language. Thus, opinion
mining techniques will be useful in this context.
At the moment the system can only be used by IT expert people. In order to
turn it into a practical tool that is routinely used by decision makers, we must
equip it with a user-friendly interface. In particular, we are in the process of
developing a web interface to the constraint solver, in order to make it easy to
use and widely accessible.
Finally, economic indicators will be used to assess the economic aspect of
the plan. Up to now, only budget and few economic pressures and receptors are
considered. We believe that a comprehensive system should fully incorporate
this aspect. We will integrate a well established approach (UN and Eurostat
Guidelines) and robust data from official statistics into the system to combine
economic accounts (measured in monetary terms) and environmental accounts
(measured in physical units) into a single framework useful for the evaluation
of the integrated economic, environmental-social performance of regions. If the
relations with economic indicators are linear, then the solution time should not
increase significantly.
7 Acknowledgements
This work was partially supported by EU project ePolicy, FP7-ICT-2011-7, grant
agreement 288147. Possible inaccuracies of information are under the responsi-
bility of the project team. The text reflects solely the views of its authors. The
European Commission is not liable for any use that may be made of the infor-
mation contained in this paper.
References
1. Krzysztof R. Apt and Mark Wallace. Constraint logic programming using Eclipse.
Cambridge University Press, 2007.
2. Maurizio Bruglieri and Leo Liberti. Optimal running and planning of a biomass-
based energy production process. Energy Policy, 36(7):2430–2438, July 2008.
Constraint and Optimization techniques for supporting Policy Making 209
3. Paolo Cagnoli. VAS valutazione ambientale strategica. Dario Flaccovio, 2010.
4. Massimiliano Cattafi, Marco Gavanelli, Michela Milano, and Paolo Cagnoli. Sus-
tainable biomass power plant location in the Italian Emilia-Romagna region. ACM
Transactions on Intelligent Systems and Technology, 2(4), July 2011.
5. Damiana Chinese and Antonella Meneghetti. Design of forest biofuel supply chains.
International Journal of Logistics Systems and Management, 5(5):525–550, 2009.
6. Davide Freppaz, Riccardo Minciardi, Michela Robba, Mauro Rovatti, Roberto
Sacile, and Angela Taramasso. Optimizing forest biomass exploitation for energy
supply at regional level. Biomass and Bioenergy, 26:15–24, 2003.
7. Marco Gavanelli, Michela Milano, Alan Holland, and Barry O’Sullivan. What-if
analysis through simulation-optimization hybrids. In K. G. Troitzsch, M. Möhring,
and U. Lotzmann, editors, Proceedings 26th European Conference on Modelling and
Simulation ECMS 2012, pages 624–630. Digitaldruck Pirrot GmbH, 2012.
8. Marco Gavanelli, Fabrizio Riguzzi, Michela Milano, and Paolo Cagnoli. Logic-
Based Decision Support for Strategic Environmental Assessment. Theory and
Practice of Logic Programming, 10(4-6):643–658, July 2010.
9. Marco Gavanelli, Fabrizio Riguzzi, Michela Milano, and Paolo Cagnoli. Constraint
and optimization techniques for supporting policy making. In Ting Yu, Nitesh
Chawla, and Simeon Simoff, editors, Computational Intelligent Data Analysis for
Sustainable Development, Data Mining and Knowledge Discovery Series, chap-
ter 12. Chapman & Hall/CRC, 2013.
10. Marco Gavanelli, Fabrizio Riguzzi, Michela Milano, Davide Sottara, Alessandro
Cangini, and Paolo Cagnoli. An application of fuzzy logic to strategic environ-
mental assessment. In R. Pirrone and F. Sorbello, editors, XIIth International
Conference of the Italian Association for Artificial Intelligence, volume 6934 of
Lecture Notes in Computer Science, pages 324–335. Springer, 2011.
11. Nigel Gilbert. Computational Social Science. SAGE, 2010.
12. S. A. Grady, M. Y. Hussaini, and Makola M. Abdullah. Placement of wind turbines
using genetic algorithms. Renewable Energy, 30(2):259 – 270, 2005.
13. Joxan Jaffar and Michael J. Maher. Constraint logic programming: A survey.
Journal of Logic Programming, 19/20:503–581, 1994.
14. Robin Matthews, Nigel Gilbert, Alan Roach, Gary Polhill, and Nick Gotts. Agent-
based land-use models: a review of applications. Landscape Ecology, 22(10), 2007.
15. B. Sadler, R. Aschemann, J. Dusik, T. Fischer, M. Partidario, and R. Verheem,
editors. Handbook of Strategic Environmental Assessment. Earthscan, 2010.
16. Kish Shen and Joachim Schimpf. Eplex: Harnessing mathematical programming
solvers for constraint logic programming. In P. van Beek, editor, CP 2005, volume
3709 of LNCS. Springer-Verlag, 2005.
17. Jens C. Sorensen and Mitchell L. Moss. Procedures and programs to assist in the
impact statement process. Technical report, Univ. of California, Berkely, 1973.
18. Klaus G. Troitzsch, Ulrich Mueller, Nigel Gilbert, and Jim Doran. Social science
microsimulation. J. Artificial Societies and Social Simulation, 2(1), 1999.
19. Joost Vennekens, Sofie Verbaeten, and Maurice Bruynooghe. Logic programs with
annotated disjunctions. In B. Demoen and V. Lifschitz, editors, International
Conference on Logic Programming, volume 3131 of LNCS. Springer, 2004.