Embedded Simulation Models in Educational Games
on Environmental Issues for Engineering Students
Amin Torabkhani
Mechanical & Industrial Engineering
Northeastern University
Boston, M\A
amin..coe.neu.edu
Jacqueline A. Isaacs
Mechanical & Industrial Engineering
Northeastern University
Boston, M\A
Jaisaacsgcoe,.jneu.edu
benneyan(gcoe.neu.edu
completing the game to help explore economic and environmental tradeoffs and the longer term implications of their game
style (i.e., beyond allocated class time). The intents are for students to learn how decisions over a longer period of time consistent with "green" or "non-green" management styles will
impact, their company's economic competitiveness and environmental impact and to gain further insight into these dynamics. Currently the model is run in parallel or after the physical
board game itself, although an eventual objective is to test embedding this type of tool within a web-based implementation of
the game currently under development.
Abstract-A Monte Carlo simulation model was developed to
provide tradeoff information about environmental issues and
production costs after students play an educational game managing a simplified automobile supply chain. Two performance
measures are tracked in the model, total production cost and an
environmental green score. Results help students understand the
effects of different management approaches to environmentally
conscious manufacturing decisions. The simulation was run for a
range of management philosophies and results analyzed for potential value to improve learning experiences,
Keywords: Educational Game, Simulation Models, Monte-
Carlo Simulation, Environment, Automobile Supply Chain
SIMULATION MODEL DESRIPTION
A. Possible Events
Twenty-three events can randomly occur during the simulation, adapted from "event cards" dealt during the Shortfall
game and categorized into two domains: regulations and technologies, as shown in Table 1. Ten regulatory events were developed from manufacturing requirements that are relevant to
the U.S. automobile industry, such as the Clean Air Act of
1970, other historical events, and hypothetical possible future
regulations. Thirteen technology events include past breakthroughs or potential new science that impact some aspect of
the manufacturing supply chain (i.e., raw materials, part production, original equipment manufacturer). Examples include
the invention of catalytic converters, alternate fuel vehicles or
hybrid technology, and material technologies that produce improved auto parts or lighter materials (e.g., windshields). Technologies such as automated guided vehicles that can benefit
any stage of the supply chain also are included.
B. Model Inputs
II.
I. INTRODUCTION
This work is based upon development of a board game,
called Shortfall, that simulates a simplified supply chain for
automobile production [1-3]. The goal of the game is to learn
how to maximize profit while minimizing environmental impact over time. Using the auto manufacturing industry, students
make a series of decisions and explore relationships between
design considerations, supply chain management, environmental issues, research and development, and profitability.
Rather than using a conventional lecture format and green
manufacturing case studies, we tested the potential of participative group game play to improve learning outcomes. Although
the supply chain is simplified for practical purposes, over the
course of the game students experience the ramifications of
materials selection and processing decisions through this
unique educational format.
During play, teams of students encounter a series of environmental regulations or green technologies and must make
adoption, investment, or compliance decisions for their company based on cost, environmental impact, beliefs about future
events, and their management philosophy regarding environmental issues. Due to the dynamic nature of game play, inherent randomness in decisions and their ramifications, uncertainty
as to future events, and different responses a player can make,
the game outcomes are not deterministic nor easily predictable.
Moreover, players might only gain a vague understanding of
the dynamics and consequences if time limitations do not allow
many rounds of play.
A separate Monte Carlo simulation model therefore was
developed that provides additional results to students after
1-4244-0861-X/07/$25.O0 2007 IEEE
James C. Benneyan
Mechanical & Industrial Engineering
Northeastern University
Boston, M\A
Table 2 summarizes the various types of information associated with the different types of simulation events, based on
the logic and learning objectives of the Shortfall game. Described in greater detail below, these include adoption costs and
probabilities (differing by management style), environmental
impacts (represented by overall green scores), non-compliance
rates and fines, and the frequency with which any given event
can occur. Specific values for these parameters that produced
the results presented below are summarized in Table 1.
The model uses these inputs to simulate a user-specified
number oftime periods ("rounds" in ShortFall) and replica61
TABLE I.
SIMULATION EVENTS AND THEIR ATTRIBUTES
Event Description
Event
Cost
LEGISLATION
The 1970 Clean Air Act mandates 90%
1
reductions il tailpipe emissions.
Noncompliance results in heavy fines or
1 000/car
Bye230,e plans. to.equre utsof0/-
Green
Fine Amount
Fine | Occurr
Value Prob.
1-0.3 1- 100/ca
200 2-0.6
4- 1.0 4-10,000/car
y90 oof3theoxides
harmful
of
nitrogen
and emissions
diesel particulate
from 20061levels.
Legislation requiring recyclability.
M
1OM
tax break to
4Goverment
compa1Mes wit
good recyclimg practices.
RegulafiDllbans the use oflead and
other toxic materials in vehicles.
2-500/car
3- 0.9 3- 5,000/car
vehicle recall.
3
0
-
300
- _______
1- 0.3
1- 50/car
2- 0.6
2- 200/car
3
0
0
M
3- 1.0
3- 500/car
IM
100
60/car
80
3,800/
50
500
0.9.9
00
1- 0.3 1-330/car
2-3
0.6 3-500/car
2- 60/car
|
worker
7
The Energy Policy Act (EPAct) of 1992
requires development of alternative fuel
1,500/car
30
0
0
1
40M
70
0
0
1
8
The Energy Policy Act (EPAct) of 2005
provides abase tax credit for the
nchase of LDV fuel cell.
pur_
purchase-of
cel-.
DOT may raise CAFE from current 27.5
to40MPGin thefuture.
1,500/car
30
0
0
TABLE II.
3
payments
6for worker health care requires
.
vehicles.
style, given
theabove.
variousMultiple
decisions
made andthen
the are
inherent
domness
noted
replications
run toranestimate the expected value and possible range of results for a
given set of decisions.
-
6
Inflation in healthcare
tions, estimating for each replication the environmental impact
and total operating costs (but not revenues nor profits) under
each management policy, normalized for an annual production
volume of 50,000 automobiles and assuming that regulation
compliance, technology adoption, and chance events do not affect net sales revenue i.e., any
advantage
from
,
resulting
market
green" practices is not considered. Each replication represents
a possible realization of the future under a given management
DATA STRUCTURES OF AN EVENT
Details
Event Description
Green
MProb. of Ac-
cee)ance
(a)
Moderate (b)
Non-green (c)
Cost
AllLDVs require dual airbags for
passengers safety.
u,000/car
TECHNOLOGY
Water-Based paint coating reduces
50
0.9
0
1- 0.3
1- 30/car
30/car
300 2- 0.6 2- 300/car
1
150/car
200 0.9
0
1
860/car
100
0.6
2,000/car
1
50/car
0
0
0
M
50/car
0
0
0
M
16 flexible mfg less lead time, and greater
100/car
0
0 0M
Tailor-welded blanks reduce weight by
50/car
50
0
0
1
50/car
0
0
0
1
0
0
11 emission of environmentally harmful
VOCs, but increases costs.
Use of aluminum engines cuts vehicle
12
weig, improves fuel economy.
Advanced catalytic convertors with
13 improved insulation reduce total
emissions by 10%.
14 Automatic Guided Vehicles (AGV)
15 Just-In-Time Manufacturing (JIT)
Automated machining leads to more
_ variety of product.
17
reducimg the part count and assembly.
Plasma Heating makes magnesium
18 production faster, less energy and and
3- 1.0 3- 700/car
Recoverscrapaluminumfrom
19 production that can be sold as a recycled (-)40/car 100
____material.
__
ma-t-erial.
recyclng (scrap segregaion
20 Advanced
technologies).
21
Froth flotaion plastic recyclig
Optimized glazing systems for cars
22 reduce vehicle vision and side panels
___weight by 30%o.
Decreasing fuel prices and good
23 economy, lead tomarketformore
p__owerful SUVs (20%o of LDV sales).
__ __________________
1-4244-0861-X/07/$25.O0
(-) 30/car 100
0
0
c(-)/Car
50
0
0
400/car
20
00
0
____
2007 IEEE
050
055
Cost associated with event
of Occurrences
Fine incurred if detected
Maximum times event occurs
throughout the simulation
of Acceptance: These
data represent
the
likelihood that a decision maker (DM) proactively Implements
new technologies or complies with regulations that may have
environmental impact. In the below analysis, three styles of
DMs were considered (green, moderate and non-green), differing in their behaviors and decisions
environmental
regarding
issues. Green DMs are more likely to comply with an event
than moderate DMs, and similarly for non-green DMs. Variation in these probabilities for each type of DM (e.g., between
green DM's) is represented via beta probability distributions,
with parameters for each event type specified by a low, average, and high probability in the usual manner [4]. At each event
withi the model, a pseudo-random beta variate is generated
with these parameters and used for the probability that the DM
makes the particular decision.
Cost: An event cost is the amount in dollars that a DM' s
company pays either to invest in some technology or comply
with some regulation. These costs, which depend only on the
event type and not the DM, were estimated from the literature,
comparisons of technologies, and available data historical on
regulations and their compliance costs [5-6]. Each cost is expressed either as a one time fixed cost or a cost per car, based
on what information was available through the above sources.
2)
2
2
1
1
- - _ _
(-) 150 0
0
- - ____
045
0.825
Chance of receiving fine
1) Probability
-
0.9
Fine Probability
Number
1
0.85
0.775
Environmental score gains for
acceptance of event
-
I10
0.8
Green Value
Fin IAmount
I
1
0.625
-3)
Green Value. The green value of an event is a unitless
value ranging from 0 to 500 that represents the overall aggregate environmental impact of investing in or complying with
1
-
62
note that this performance measure calculation (rather than
simply the total of each period's green value) appropriately results in a company with a consistent compliance record outscoring one that is non-compliant until the latter time periods
some event, regulation, or technology, based loosley on the
idea of an environmental Dow Jones type index. Specific
green values for each event are summarized in Table 1 and
were determined as follows. A value of zero is given to any
event with no direct environmental impact, such as advanced
machining technologies, and higher green values are given to
events with larger impacts and regulations passed in response
to environmental challenges. For example, the Clean Air Act
of 1970 has a higher green value than a technology that
produces lighter weight windshield glass. Compliance with a
mandatory regulation has a lower green value than proactively
addressing potential environmental concerns.
but finally implements all the same technologies.
IV. MODEL LOGIC
A. Occurrence ofan Event
The simulation model can be run for any number of time
periods, with the results presented below using 40 rounds. In
each round one event is randomly selected from the candidate
event list as follows. The event type is first determined, currently with 4000 (60%) chance of being a regulation (technology) event, and then the specific event is selected. At present
and for simplicity, specific regulations and technologies are
assumed to have equal occurrence probabilities (within their
type), although this can be easily modified. If a chosen event
has been selected its maximum number of times, it is removed
from the candidate event list.
4) Fine Probabilities and Amounts: If a company chooses
not to comply with a randomly generated requirement event,
fines are levied with these probabilities and amounts. For any
given regulation, these fine probabilities and amounts increase
for the same event in future time periods if a company remains
non-compliant. As above, each fine is expressed either as a
one time fixed cost or cost per car.
B. Company's Response
A company's response to an event is determined randomly
within the code based on the acceptance probabilities in Table
1 (since the simulation does not involve interaction with a human player). The user currently can select one of three types of
DMs: environmentally proactive (green), moderate, or less environmentally unfriendly (non-green). A green DM can still not
comply with a regulatory event, just usually with lower probabilities than a non-green DM; the opposite is true for investing
in a technology event.
These probabilities are generated using the values given in
Table 1 (converted to beta parameters a, and a2), which in turn
are used to draw a probability via acceptance-rejection that the
DM complies with this particular event. Thus event-to-event
these probabilities will have a mean of (a+4b+c)16 and variance
((b-a)/6)2. Once determined, these probabilities are used to determine compliance or non-compliance using a Uniform(0,1)
pseudo-random number in the standard manner.
5) Occurrence Times. The maximum number of occurrences for events in the simulation varies based on the event.
Some events can occur multiple times throughout the simulation, indicated by "M" in Table 1, such as machining technologies, healthcare costs, and incentives for recycling, but the
occurrence of most events is limited. Some can only occur one
time (e.g., "using aluminum for the engine blocks") and others
up to, for example, four times (such as the "Clean Air Act")
due to the nature of such regulations. For example, most laws
are passed after a period of lobbying and negotiation and
typicallly will be revised over time based on the EPA's
monitoring of the implementation process. After an event has
occurred its maximum number of times, it is eliminated from
the event candidate list within the simulation.
III. PERFORMANCE MEASURES
One goal of the simulation is to compare the outcome of
three different decision making styles based on two performance measures: total cost and total green score. These measures
then are analyzed statistically to understand and contrast the
impact and variability due to different DM styles.
. Determining Costs and Green Values
If the DM complies with or invests in an event, the company pays the cost assigned to the event while gaining any associated green value from Table 1. Again, technology investments are assumed to not impact sales revenue, with the only
advantage being to improve the green score.
If DM's decide not to comply with a regulatory event,
whether they are penalized is determined from the tabulated
fine probabilities similar to as above. If penalized, the company's total cost is incremented by the associated fine amount
and the corresponding green value is decremented from the total green score following the integration logic described previously. Note that currently the green score is reduced only if a
penalty is enforced, somewhat representing public perception,
although this logic can also be modified.
A. Total Cost
The total cost is the cumulative sum of the compliance and
investment costs, including fines due to non-compliance, incremented over time for the costs incurred within each time pe-
riod.
B. Green Score
Similarly, the overall green score is a time-based running
total of the green values the result from each time period, calculated by integrating over time the total of all green values todate, in essentially the same manner any time-based measure is
computed (e.g., mean queue length) [4]. For purposes here,
1-4244-0861-X/07/$25.O0 2007 IEEE
63
For regulation events that can occur more than once, both
the fine probability and fine amount increase after each occurrence of non-compliance. However, once the company complies with the event, it will not occur again unless it is a multiple occurrence type of event.
For non-acceptance of environmental technology events, no
costs are committed by the company, and no green value is attributed to the event. For certain technologies, however, a green
value is subtracted for non-acceptance. These events (events
11-13) were assigned a fine probability, putting the noninvesting companies at risk of losing the event's green value,
because these technologies have direct impact on the emission
from either the plant (e.g., emission of harmful volatile organic
compounds from paint shops) or automobiles (e.g., catalytic
converter technology that reduces the tail-pope emission). This
technology forcing bias reflects actual requirements in emission reduction by the Clean Air Act.
$250
60
$23552
50
T
~~~~~~$220
$200
$183
~~~~~~~~
40
$150
29
2 30
0
$100
O 20
$50
10
Green
Figure1.
Moderate
$0
Non-Green
Average performance outcomes for three decision makers
Moderate
Green
Non-Green
D. Updating the Variables and Inputs
As the simulation model runs, the total cost and green score
performance measures are updated based on the costs and
green values determined in each round. Table 1 also is updated
with respect to the number of occurrences of each event. An
event that occurs up to its maximum number is eliminated from
the event candidate list; events with no upper bound on the
number of occurr ences are not eliminated.
$0
$50
$100
$150
$200
$250
$300
$350
$400
Total Cost
SIMULATION RESULTS
As an illustration, the simulation model was run three times
for each of the three types of DM's described above (each as a
40-round run of 50 replication each). The total costs and green
scores were calculated for each replication, and their means and
standard deviations used to construct confidence intervals
Cl's). The averages and Cl's are point and interval estimates of
the expected value of each performance measure, and the standard deviation provides information about the amount of uncertainly and unpredictability of a company's measures after 40
rounds.
V.
Figure 2. Total Cost Distribution
Moderate
Non-Green
N
Green
As shown in Figure 1, the green company accumulated the
highest average green score, but also the highest average cost
-10,000 10,000 30,000 50,000 70,000 90,000
(i.e., averages of the 50 replication results). Not surprisingly,
Total green score
the non-green company produced the lowest average cost by
and
environmental
avoiding green technologies
responsibilities,
Figure 3. Green Score Distribution
so also the lowest average green score.
Because the non-green DM is more likely to fail to comply
Figures 2 and 3 summarize the distributions of total costs
or invest, the greater variability in fine costs and less consisand green scores for each type of DM, with their peaks corretency in investments both result in greater variation in total
sponding to the expectations shown in Figure 1. These distribucosts. For example, although the expected cost for non-green
tions are very well approximated by normal distributions (chicompanies is $183, they may experience an actual cost anysquare fit, p < 0.005), due to the large central limit tendencies
where between roughly $24 and $342 (9500 probability interfrom the many additive effects inherent within the model logic.
As shown, total cost for the non-green DM exhibits higher unval), making if difficult to make effective decision and plan accordingly. This also suggests that merely increasing the fine
certainty (i.e., variability) than the other DM types. This difference also is reflected by the larger confidence intervals in Figprobability or fine costs (although intuitively appealing) may
cause the non-green DM to have the same expected costs as the
ure 1. In contrast, the green scores in Figure 3 appear fairly
homoskedastic for all three DM types. other DMs but also more cost uncertainty.
1-4244-0861-X/07/$25.O0 2007 IEEE
64
performance measure, the implementation and fine cost factors
similarly were excluded. For both measures, 2-level and 3-level
analyses were conducted on the remaining factors, using the
high and low settings summarized in Table 3, acceptance probabilities adjusted similarly, and center points and edge points
for the 3 level designs.
These types of observations help underscore to students the
core concepts that any given company only experiences the
next 40 years once (i.e., one realization) and that management
based on expectations alone can lead to poor company and policy decisions.
A. Short Term vs. Long Term Results
Figure 4 illustrates the expected short versus long term
mean cost per round (total cost divided by number of rounds
played) for the 3 decision making strategies, averaged over 50
replications. The OLS linear regression lines were fit on the
first 20 rounds of data (shown in solid markers) and suggest
that the mean cost per round for the green DM eventually will
decrease beyond that of a non-green DM, such as if the early
acceptance of events saves the company fie amounts.
TABLE III.
Factor
Probability
Acceptanceof
Cost
O
$4-
t
.A,
n , 2i S E S S & E b b t
_
PCFA PCfA
$290
*
PCFa
$2
5
10
15
20
25
30
35
40
45
Green (P)
PCfa
pCFA
50
,
CF pCfA
$10
pCfa
XE $190-PcFAc* *
c
PcFa pcfA pcfa
Round
o
Figure 4. Average Cost Per Round
As shown, the mean cost-per-round for non-green DMs is
much more constant over time, but starting at a lower cost due
to non-investment and in environmental technologies and regulation non-compliance, with the moderate DM exhibiting results somewhere in the middle. The impact of increasing fine
amounts appears negligible.
pcFa
$140-
$90-pcfa
0
4
8
Experiment Setting
pcfA
12
f
16
Figure 5. Average Total Cost Values for Each Experiment
The data for rounds 20-60, however, do not confoirm to
these trends, due to an idiosyncrasy within the model logic. As
time passes, the event candidate list gets smaller, producing increasing probabilities of a null event in any given round (where
no event card is drawn). The costs of each DM type would
cross at roughly round 30, however, given a larger number of
possible events (or alternately not removing one-time events
from the event list, assuming new comparable technologies and
regulations are introduced that replace them).
For total cost (Figure 5), the natural groupings of four setting combinations each lead to several conclusions. The acceptance probability P and cost factor C appear to have the largest
impact, with a probable PxC interaction suggested in the righthand 2 groups. The combination of P and C settings changes at
every fourth experiment, resulting in the observed clusters and
trends. Changes in the other factors have a greater impact on
under low compliance probability (corresponding to a nongreen DM in experiments 9-16), which makes intuitive sense
since higher non-compliance probability implies higher probabilities to receive fines whereas the fine amount in the first 8
experiments is not that important since they are rarely levied.
B. DOE/Regression Analysis
To better understand which variables have the largest impact on each performance measure (total cost, green score), 2k
and 3k factorial design analyses were conducted on simulation
results, using the variables and settings shown in Table 3. Each
The green scores in Figure 6 reveal similar patters and interpretations. The acceptance probabilities and green values
appear the
to have
a slight
interaction
betwo the
and largest
a smallimpact,
effecthwith
dueecn
to
the size
of the fine
~~~~~~~~tween
prbbiiy
etnsta
h
moepoone.o
combination.of modl inut at thei high _An low vaue wa
replicated 50 times, and the results used to determine signifi' .
. equations.
.
cant factors and develop predictive
For the cost performance measure, the green value was excluded from the experiments since it has no impact on cost. For the green score
1-4244-0861-X/07/$25.O0 2007 IEEE
125% Cost (C)
*
$240-
$0
0
75% Cost (c)
Figures 5 and 6 summarize the cost and green score results
for the 2k experiments, respectively, assuming no factor interactions. The coded labels near each data point correspond to
the experimental settings, with lowercase (uppercase) letters
indicating the low (high) factor settings in Table 3.
$8
0
High Value
75% Fine Probability (f) 125% Fine Probability (F)
50% Fine Amount (a)
150% Fine Amount (A)
Fine Amount
= $12
$10C
Low Value
Non-Green (p)
Fine Probability
$14
*
FIVE FACTORS STUDIED VIAEXPERIMENTALDESIGN
fis.Ta's h lp o ahPGcmiaini rae o
65
term and appears in other non-linear interactions, although the
fine probability (F) now also has significant (of smaller magnitudes) quadratic and non-linear effects.
lower values of F, the fine probability. As the fine probability
increases, non-compliers are more likely to receive environmental fines and corresponding reductions in green scores.
65,000
VI.
PGF * PGf
P
-
The simulation model was developed to further explore environmental versus economic impacts after playing the Shortfall educational board game. In particular, different decision
making styles can be quickly investigated for their impact on a
company's production costs and environmental profile. This
model can be used either as a real-time teaching tool after
55,000-
45,000
co 45X000 -
Pgf
PgF
:> 35,000 -
,
25,000
15,000 -
0
1
2
3
4
pGF
5
* pGf
Shortfall game play, to investigate alternate management or
policy decisions, or to conduct more in-depth analysis as to the
underlying dynamics.
Key lessons for students include the importance of considering uncertainty and short-versus-long term impacts in environmental decision making. In the above example, green management philosophies in the long run result in both lower cost
and better environmental impact, whereas non-green DMs have
greater economic uncertainty, even if compliance fines and enforcement are increased. The experimental design results help
develop better understandings of what factors have the most
and least impact.
Further analysis may include additional investigation of the
distributions of cost and green values for different DM styles,
adding variability to implementation costs and green values,
and examining the total cost slope and cross-over time. Future
development work might include expansion of the candidate
event list, implementation of more complex logic to capture
,pg
pgF
6
7
8
Experiment Setting
Figure 6. Experimental Design Responses for Average Green Score
Regression results shown in Table 4 for the 2-level and 3
level runs confirm these general conclusions and calculate the
specific effects for each factor; only the significant factors coefficients (p < .05) are shown for brevity. Note that all main effects (factors) are significant, although some much less so than
others. Both performance measures also have significant nonlinear terms in the 3k models.
REGRESSION COEFFICIENTS FOR COST AND GREEN SCORE
TABLE IV.
Cost Function Results
Factor
Intercept
P
C
LP*C
F
Parameter
Parameter
Estimate
Estimate
207.35
220.61
27.94
25.87
4|49.92
1 8 54
5.58
P*F
P*C*F
A
P*A
-3.70
-0 . 60
3 .83
p2
P2*C
N/A
P2*F
P2*A
rP*:F2
P*C 2*F*A
Green Score Results
2-level J 3-level
||
N/A
N/A
N/A
N/A
N/A
P
Parameter
Estimate
Estimate
38, 742
15,045
43, 118
15,452
9,685
3, 76 1
-1,645
-3.70
-0.60
P*F
G*F
P*G*F
850
850
-411
213
-250
2I 1
p2
N/A
P*G
F
- 4 .37 P2*G
P2*F
2 .43
F2
2 .79
2 06 I P*F2
- 0.65
N/Al-065
PbC2F*A
G*F2
G*
P2*G*F
P*G*F2
||
N/A
N/A
N/A
N/A
various dynamics, and assessment of improved learning.
AcKNOWLEDGMENTS
The authors would liketo acknowledgethe support ofNSF
grant number DMI-0537056 and are grateful to D.M. Qualters,
T.P. Cullinane, , and J.L. Isaacs for insightful comments.
10,778
3, 863
-1,000
G
1.48
3.31
-11.42
1
Intercept
3-level
Parameter
54.49
8 . 34
3.15
-3. 1
2-level
Factor
REFERENCES
[1] J. Corriere, "Short Fall: An Educational Game on Environmental Issues
in Supply Chain Management" , M.S. thesis, Mechanical and Industrial
- 5,063
-1,264
- 644
675
|
-40 7
N/A
171
N/A
N/A
-161
[2]
[3]
-102
Forecasting and Social Change, vol. 72, pp. 76 1-778, 2005.
and is involved in all non-linear interactions. For green value,
[6] J. Michalek, P. Papalambros, S. Skerlos, "A study of fuel efficiency and
emission policy impact on optimal vehicle design decisions", Journal of
the most significant factor now is compliance probability, followed by green value and the interaction between these two.
th.otsgiicn
1-4244-0861-X/07/$25.O0 2007 IEEE
J. Isaacs, T. Cullinane, D. Qualters, A. McDonald, J. Laird, "Games as
Learning Tools to Promote Environmentally Benign Systems",
Proceedings of LCE CIRP 2006, International Conference on Life Cycle
Engineering, May 31 - June 2, 2006, Leuven, Belgium. CD-ROM.
D. Qualters, J. Isaacs, T. Cullinane, J. Laird, A. McDonald, "Assessment
of Shortfall: A Board Game on Environmental Decisionmaking",
Proceedings of ASEE 2006 Annual Conference and Exposition, June 18
- 22, 2006, Chicago IL, American Society for Engineering Education
(ASEE), Washington, DC, CD-ROM.
automotive emissions controls in the United States", Technological
the compliance probability iS only significant quadratic effect
~.
Engineering, Northeastern University, Boston, MA 2003.
[4] A. Law, D. Kelton, Simulation Modeling and Analysis, McGraw-Hill,
1995.
[5] D.
Gerard, L. Lave, "Implementing technology-forcing policies: The
1970 Clean Air Act Amendments and the introduction of advanced
For total cost, the most significant factor is compliance and
implementation costs, followed by compliance/implementation
probability and the interaction between these two. However,
Again,
copiac prbblt is
SUMMARY AND FUTURE WORK
Mechanical
udai
66
Design, Trans. of the ASME, vol. 126, pp. 1062-1070, 2004.