Table Of ContentRegular Paper
Isoperformance: Analysis
and Design of Complex
Systems with Desired
Outcomes*
Olivier L. de Weck1, † and Marshall B. Jones2
1Engineering Systems Division, Department of Aeronautics & Astronautics, 77 Massachusetts Avenue, 33-410,
Massachusetts Institute of Technology, Cambridge, MA 02139
2Behavioral Science Department, 500 University Drive, Room C1728, Pennsylvania State University, Hershey, PA 17033
ISOPERFORMANCE: ANALYSIS AND DESIGN OF COMPLEX SYSTEMS WITH DESIRED OUTCOMES
Received 27 October 2004; Accepted 17 October 2005, after one or more revisions
Published online in Wiley InterScience (www.interscience.wiley.com).
DOI 10.1002/sys.20043
ABSTRACT
The design of technical systems such as automobiles and spacecraft has traditionally focused
exclusively on performance maximization. Many organizations now realize that such an
approach must be balanced against competing objectives of cost, risk, and other criteria. If
one is willing to give up some amount of performance relative to the best achievable
performance level, one introduces slack into system design. This slack can be invested in
creating better outcomes overall. One way to achieve this is to balance the requirements
among contributing subsystems such that the number of active constraints is minimized, while
still achieving the desired system performance. This paper introduces a methodology called
“isoperformance” as a means of identifying and evaluating a performance-invariant set of
design solutions, which are efficient in terms of other criteria such as cost, risk, and lifecycle
properties. Isoperformance is an inverse design method that starts from a desired vector of
performance requirements and works backwards to identify acceptable solutions in the
*This work was partially supported by the NASA Goddard Space
Flight Center under Contracts No. NAG5-6079 and No. NAG5-7839
and by the Jet Propulsion Laboratory under Contract No. JPL 961123.
†Author to whom all correspondence should be addressed (e-mail:
deweck@mit.edu).
Systems Engineering, Vol. 9, No. 1, 2006
© 2006 Wiley Periodicals, Inc.
45
46 DE WECK AND JONES
design space. To achieve this, gradient-based contour following is implemented as a multi-
variable search algorithm that manipulates the null set of the Jacobian matrix. Use of the
method is illustrated with two examples from spacecraft design and human performance in
sports. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 45–61, 2006
Key words: system design; isoperformance; multiobjective optimization; constraints; gradi-
ent-based search; inverse design methods; sensitivity analysis
1. INTRODUCTION achieve desired robust functionality at the lowest pos-
sible cost. In that case the desired performance levels
Engineers have been traditionally conditioned to think
become known quantities that can serve as targets for
in particular ways about the challenging task of system
system designers. This is the focus of so-called inverse
design. First, emphasis has been historically placed on
design methods, whose goal it is to find a set of solu-
performance maximization, which was evident in pro-
tions in the design space that satisfy a set of perform-
jects such as Apollo (1962–1973), the development of
ance targets in the objective space, J D x(J). The main
the Concorde (1956–1976) or even current practices in
challenge lies in the nonuniqueness of the problem.
Formula 1 race car design. Second, the dominant way
Since the number of design variables generally far
in which design is taught and practiced is in the mode
exceeds the number of objectives, there might be many,
of “forward analysis.” Choices in the design space such
as material selection, geometry specification or control- often infinitely many design vectors, xi, that satisfy the
ler gain settings are evaluated in terms of their impact vector of performance targets, Jreq. This, of course,
on system response and objectives such as range, speed, assumes that none of the targets are utopian, i.e., that
and more recently fuel consumption. If initial design the targets are feasible based on physics and available
choices are unsatisfactory, engineers typically iterate on technologies (but not necessarily based on available
their designs until such quantities have been maximized resources).
or minimized, subject to a number of other technical Isoperformance is a method that addresses this prob-
and financial feasibility constraints. This modus oper- lem by first obtaining a performance invariant set of
andi is facilitated by the fact that, with the possible system design solutions and subsequently reducing
exception of buckling and other instability situations, these to an efficient set, when evaluated against other
each vector of choices, x, in the design space can usually
criteria such as cost and risk. Said more simply, it is an
be uniquely mapped to an expected outcome in the
inverse design method that allows system designers to
objective space, x D J(x).
ask: “What is the set of feasible designs, X , that
iso
This paradigm has its roots in an era where system
satisfies all performance targets within some tolerance,
performance was the prime driver of competitiveness
while optimizing other criteria such as cost and risk?”.
and superiority. This is particularly true for aerospace
Let us consider a simple example from rocketry to
engineering, which has only recently come to realize
illustrate this point. The primary performance of a
that performance is not necessarily limited by physical
rocket is measured by the change in velocity, ∆v, that it
phenomena (e.g., transonic drag, atmospheric proper-
can achieve for a specified payload mass m . The rocket
ties in the stratosphere) alone, but that “pushing the p
performance envelope” too hard can have detrimental equation by Tsiolkovsky [1903] states that
consequences for other aspects of the system. We find
m
the following quote by Schrage et al. [1991] to be ∆v = v − v = g ⋅ I ⋅ ln i , (1)
particularly appropriate: “The experience of the 1960’s f i sp mf
has shown that for military aircraft the cost of the final
increment of performance usually is excessive in terms where vi is the initial velocity in m/s, vf is the final
of other characteristics and that the overall system must velocity, g is Earth’s mean gravitational acceleration at
be optimized, not just performance.” the surface, g = 9.81 m/s2, I is the specific impulse of
sp
A more natural mode of thought is not to strive for the propulsion system in seconds, m is the initial mass,
i
the “best achievable” system performance, but accept- and m is the final mass after engine cutoff. The initial
f
able performance that is “good enough,” depending on mass can be written as
contractually specified requirements, the state of com-
petition in various market segments, and the need to m = m + m + m = (1 + α)m + m , (2)
i s f p f p
ISOPERFORMANCE: ANALYSIS AND D E S I G N O F C O M P L E X S YSTEMS WITH DESIRED OUTCOMES 47
where m is the structural (empty) mass of the rocket, which variables in the system the designer can change
s
m is the mass of fuel, and m is the payload mass. For independently, and which ones are constrained by na-
f p
simplicity one can assume that α represents the struc- ture or man to be fixed constants. In the case of rocket
tural mass of the rocket as a fraction of total fuel mass. design we may choose the I (by selecting a particular
sp
Typical values for α are on the order of 0.08–0.12. The oxidizer/propellant combination) and size of the rocket
final mass—assuming all fuel is consumed—is simply (by deciding on the quantity of fuel). Of course, archi-
tectural decisions such as the number of rocket stages
m = m + m = αm + m . (3) must be considered as well and we will return to this
f s p f p
point later on. Let x = [x x ]T be the design vector
1 2
If we were to design a rocket for launching payloads to with x = I and x = m. The vector of fixed parameters
1 sp 2 f
low Earth orbit, a typical performance requirement is p = [p p p ]T with p = g = 9.81 m/s, p = m = 5000
1 2 3 1 2 p
would be J = ∆v = 9500 m/s. This requirement kg, and p = α = 0.1.
req req 3
allows achieving orbital velocity while absorbing drag, Given the fact that we have n = 2 design variables in
gravity, and flight path turning losses during ascent. In this example, we can easily evaluate all possible designs
other words, satisfactory performance is defined as between technologically feasible upper and lower
accelerating a payload mass of say, m = 5000 kg, by bounds:
p
the velocity increment, ∆v . If the actual performance
req
achieved is below this level, a payload would not reach x = 300 ≤ x ≤ x = 500,
1, LB 1 1, UB
or remain in orbit and the system would be useless. On
the other hand, if the system significantly exceeds the x = 5 ⋅ 104 ≤ x ≤ x = 106. (4)
2, LB 2 2, UB
requirement, it will achieve its purpose but be overdes-
igned and, as a consequence, be heavier, more complex, We can discretize this design space into small incre-
and more costly than really needed. So, what is the ments, ∆x, and evaluate all possible combinations of
i
family of feasible rocket designs that satisfies the re- specific impulse and fuel mass. The result is shown in
quired performance level? Figure 1 and depicts the characteristic “wall” caused by
Isoperformance is a method for thinking through and the rocket equation. As I is decreased by a small
sp
solving this problem. After specifying the required amount, it takes more and more propellant to achieve
function and performance level, one must understand the same ∆v to make up for the loss of combustion
Figure 1. Isoperformance contours of v [km/s] for simple rocket example.
48 DE WECK AND JONES
efficiency. The plot shows the isoperformance contour lecting a particular solution in the reduced set must be
of interest (9.5 km/s) and the relationship that must be discussed. These three sequential questions give rise to
maintained between the design variables x and x to the three steps of the isoperformance methodology
1 2
stay on the contour. Thus, there are an infinite number presented in this paper.
of choices between the two extreme designs: A and B.
A uses propulsion technology which is easily achiev-
2. LITERATURE REVIEW AND PROBLEM
able, while B assumes an I of 500 s, which is very
sp FORMULATION
difficult to achieve even with state-of-the-art liquid-hy-
drogen, liquid-oxygen propulsion technology. Said
2.1. Related Literature
more plainly, design A makes the job of the propulsion
subsystem designers easy but requires a very large A system is understood broadly as a complex set of
rocket which creates structural and controls challenges. human and/or artificial components that interact to
Design B, on the other hand, is small and compact, but achieve a desired function. Performance is a quantita-
very challenging from a propulsion technology stand- tive measure of how well this function is executed. Four
point. It is interesting to note where various existing of the main tensions during system or product develop-
rocket stage designs fall in that space. The second stage ment have been identified by Maier and Rechtin [2000]
of the Saturn V launch vehicle (Saturn5.2) falls exactly (see Fig. 2). One of the important tasks of a systems
onto the isoperformance contour and represents a com- engineer or program manager is to identify, quantify,
promise design (close to design C) that meets perform- and resolve these tensions. An increase in system per-
ance requirements but balances the difficulty between formance can generally only be achieved by increasing
structural and propulsion subsystems.1 Other systems cost, stretching project schedules, accepting a higher
such as the Space Shuttle second stage (STS.2), would level of risk, or a combination of these according to
be significantly overdesigned or underdesigned (Atlas- Shishko et al. [1995].
I first stage), respectively. Isoperformance is an operational method that builds
This example, while accurate based on first princi- upon Herbert Simon’s [1996] notion of “satisficing.”
ples, does not capture some of the most important Satisficing is to “accept a choice or judgment as one that
considerations in selecting among designs A, B, or C. is good enough, one that satisfies.” According to Herb
One example of such a secondary criterion is the time Simon, who coined the term, the tendency to satisfice
required to prepare the vehicle for launch. While oper- shows up in many cognitive tasks such as playing
ating along the isoperformance contour (Fig. 1) may be games, designing under time and schedule pressure,
efficient from a propulsion standpoint it implies that and making financial decisions where people typically
cryogenic fluids will be used (LH , LOX) and that a do not or cannot search for the optimal solution.
2
rocket cannot be readied for launch with only a few The idea of holding a performance metric or value
minutes notice. This is the main reason why designers of an objective function constant and finding the corre-
of ICBMs chose lower I designs that could be readied sponding “contours” has been previously explored by
sp
for launch in minutes rather than hours. Accepting researchers in other areas. Gilheany [1989], for exam-
lower propulsion performance was acceptable in that ple, presented a methodology for optimally selecting
case because payloads were smaller and orbital veloci- dampers for multi-degree-of-freedom systems. In that
ties did not have to be achieved due to ballistic reentry. particular work the contours of equal values of the
Finding the isoperformance contour in this example objective function are found as a function of the damp-
was trivial because we only considered two design ing coefficients. The term “isoperformance” was origi-
variables and a single objective. What if multiple objec-
tives were to be considered together and the vector of
design variables were significantly larger? In that case
finding the set of isoperformance solutions is not trivial,
and we must find ways to systematically search the
design space for acceptable solutions. And, once such
solutions are obtained, we must find means to reduce
the large set of alternatives to a smaller set that can be
presented to decision-makers. Finally, criteria for se-
1Note that while the position of the existing rockets in Figure 1 is
correct, the actual ∆v and payload masses associated with them are Figure 2. Tensions during systems architecting and design
different from the ones assumed in this simple example. [Maier and Rechtin, 2000: 83].
ISOPERFORMANCE: ANALYSIS AND D E S I G N O F C O M P L E X S YSTEMS WITH DESIRED OUTCOMES 49
nally coined in the area of human factors research. Work roominess [m3] being maximized (LIB). Which of these
has been done by Kennedy, Jones, and coworkers [Ken- objectives are considered as NIB target values and
nedy, Turnage, and Jones, 1990; Jones and Kennedy, which ones can be maximized or minimized depends
1996] on the need within the U.S. Department of De- on the particular circumstances. Typically, however,
fense to improve systems performance through better such objectives are counteracting. Large interior vol-
integration of men and women into military systems ume would tend to increase drag, which in turn de-
(human factors engineering). They present the applica- creases fuel economy. A target vehicle fuel economy
tion of isoperformance analysis in military and aero- can be achieved by trading off fuel capacity, empty
space systems design, by trading off equipment, weight, and engine displacement among other vari-
training variables, and user characteristics. Once the ables. The isoperformance approach assumes that de-
level of operational performance in these systems is sired performance targets Jreq are known, i.e., that the
key performance objectives are captured as NIB and
settled upon (e.g., “pilot will check out all aircraft
that they must be achieved first. Traditional perform-
systems within 30 seconds”), tradeoffs among equip-
ance maximization always assumes (LIB) at the ex-
ment variables, adaptation, training, and individual pre-
pense of the other objectives. Isoperformance, on the
disposing factors can be made. Jones in particular has
other hand, fixes the amount of performance at an
also linked isoperformance to personnel selection
acceptable level (NIB) and trades off the other objec-
[2000].
tives with respect to each other.
Inverse design has recently become a topic in aero-
This is similar in philosophy to Physical Program-
dynamics where one would like to automatically gen-
ming, developed by Messac [1996] with the difference
erate airfoil geometry (usually as a sequence of x/y
that in Physical Programming all objectives, LIB, NIB,
coordinates of control points) for a prescribed pressure
and SIB, are mapped onto a unitless scale of goodness
distribution over a wing (see, for example, Kim, Kim,
and combined together into an overall system utility. It
and Rho [1999]). Level Set Methods in mathematics
is this unitless measure that is then optimized. The result
also have the potential to represent isoperformance
of Physical Programming is a single point design that
surfaces (see Osher and Fedkiw [2002]). So far, how-
will maximize overall utility depending on the break-
ever, these advanced geometrical techniques have been
points that designers select for various levels of desir-
mainly applied to visualization and computer graphics.
ability. For example, for system mass the user might
A number of researchers such as Taguchi, Cook
select the following ranges: Highly Desirable < 250
[1997], and Messac [1996] have recognized that system
(kg), Desirable 250–275, Tolerable 275–300, Undesir-
requirements typically fall into one of three classes:
able 300–325, Highly Undesirable 325–350, Unac-
“smaller-is-better” (SIB), “larger-is-better” (LIB), and
ceptable > 350. While this approach is intuitive and
“nominal-is-better” (NIB) (see Fig. 3). In automotive
ensures that all objectives contribute somewhat to over-
design, for example, a target average fuel economy
all system utility, there is no guarantee that those per-
[mpg] might have to be achieved (NIB) for a vehicle,
formance objectives that are deemed critical will indeed
while at the same time vehicle variable manufacturing
be met. Of course, additional points can be obtained in
cost [$] might have to be minimized (SIB) with interior Physical Programming by rerunning the optimization
for different settings of the desirable-undesirable range
settings and different weightings.
In isoperformance on the other hand we first extract
the subset of solutions that strictly satisfy the NIB
requirements. We carefully analyze and visualize this
set of solutions and try to extract engineering insight
from them. A second step is to evaluate the performance
invariant solutions in terms of their SIB and LIB objec-
tives and to filter the set to only those solutions that are
nondominated according to the SIB and LIB objectives.
The third step is to select one (or more) designs from
this Pareto [1906] set for further consideration.
Unfortunately, most inverse design problems ad-
dressed in the literature are of small scale and don’t
address the curse of dimensionality of complex system
Figure 3. Normalized utility curves ui of the ith system
objective Ji represented by a monotonically decreasing (SIB), design. What is needed is a more general methodology
increasing (LIB), or concave function (NIB). that can deal with system design problems on a larger
50 DE WECK AND JONES
Many systems may be described in this framework;
even if the exact form the transfer functions G, G ,
I sys
G , and K may not always be known. Table I maps the
O
various quantities to the systems of interest in this paper.
This list is not meant to be exhaustive, but rather illus-
trative. As the states of dynamical systems evolve over
time, their behavior can be measured and influenced to
the extent allowed by the system design.
Figure 4. Block diagram of dynamic system with feedback If the system is assumed to be linear and time-invari-
control. ant, it may be described by a set of governing equations
such as
.
scale that have both a large number of design variables, q = A (x, p)q + B (x, p)d,
sys i j sys i j
objectives, constraints and potentially both artificial
and human subsystems and components. The next sec- z = C (x, p)q + D (x, p)d, where i = 1, 2, . . . , n,
sys i j sys i j
tions will present the formulation and solution of the
isoperformance method as one step in this direction. G = C [sI − A ]−1B + D . (5)
sys sys sys sys sys
The method will be contrasted with a more traditional
all-in-one multiobjective optimization formulation and This may not always be a good assumption, but we
two examples will be presented, one from spacecraft will follow this framework for now, because it is allows
design, the other from human performance in sports. us to rigorously define the relationship between design
variables, fixed parameters, and system performance.
2.2. Notation Moreover, the satellite in the first example is modeled
as a dynamic, linear time-invariant system.
Let a general dynamic system be represented by a block
The dynamic states of the system, q, are generally
diagram (Fig. 4) whereby the system is subject to both
internal, and the matrices describing the system prop-
exogenous inputs d, which enter the system as filtered
erties, A, B, C, and D clearly depend on how the system
disturbances w, as well as control inputs u. The system
has been designed. The expression A (x, p) expresses
responds by a measurable system output, y. A system sys i j
that the A-matrix of the system depends on both settings
controller, K, may be present in an attempt to minimize
of the design variables x where i = 1, 2, . . . n, as well
the error signal, e, between the desired system behavior, i
as the fixed parameters p. A performance objective for
r (reference signal) and the measured response. The j
the system may be defined as an average, maximum,
performance of the system, z, may be different from the
minimum, cumulative, root-mean-square, or other sta-
measured output, y. The system objectives are typically
tistical measure of system performance, z(t). For exam-
functions of the performance output signal z(t) such that
ple, if z(t) is a time-varying performance output signal,
J = f(z).
Table I. Examples of Input, Output, and Performance Variables for Various Systems
§§Only liquid fuel rockets can typically be throttled.
ISOPERFORMANCE: ANALYSIS AND D E S I G N O F C O M P L E X S YSTEMS WITH DESIRED OUTCOMES 51
then the objective may be to minimize the root-mean- Recall our rocket design example from Section 1 and
square (RMS) value of that signal. the results in Figure 1. We see that the isoperformance
set is described by the contour labeled as 9.5 km/s. In
J = f (z), this simple case the contour is fully described by the
z
implicit equation
1/2
T
e.g., Jz = ||z||2 = E[zTz]1/2 = T1 ∫0 z(t)2dt RMS.(6) Xiso: 9.81 ⋅ x1 ⋅ ln 01..11xx22 ++ 55000000 = 9500. (12)
An isoperformance requirement can then be formulated We extract from it a subset of three discrete isoperfor-
as mance solutions (A, B, and C), k = 1, 2, 3:
Jz(xiso,i) ≡ Jz,req W i = 1, . . . , n, (7) xi1so = 411016 , xi2so = 3 4⋅ 21905 , xi3so = 7 5⋅ 01004 (.13)
whereby a two-side tolerance band, τ, may be allowed
for practical and numerical reasons: These satisfy the side bounds [Eq. (4)]. Note that feasi-
bility constraints were not present in this simple exam-
τ: Jz(xiso,i) − Jz,req ≤ τ. (8) ple. In cases where n >> 2, there will potentially be
Jz,req many isoperformance solutions, and we can apply sec-
ondary criteria to filter these. We assert that these sec-
Thus, J represents not an ideal but acceptable level ondary criteria should primarily be associated with cost
z,req
of RMS performance of signal z(t). In the first example, and risk (Fig. 2) objectives, rather then required system
z(t) will represent the residual line-of-sight (LOS) jitter performance.
of a space telescope. Attempting to drive this require-
Step 2—Find efficient subset
ment to zero or a number that is significantly below the
resolution of the on-board imaging instruments would
be difficult to achieve and wasteful. Find all xil∗so ∈ Xi∗so ⊆ Xiso ⊆ Rn
The isoperformance problem is succinctly stated in
the following section. such that for a vector of secondary (cost and risk)
objectives J = [J ⋅ ⋅ ⋅ J ]T there exists no other
cr cr,1 cr,r
2.3. Isoperformance Problem Formulation feasible design vector x ∈ X ⊆ Rn such that:
iso
The isoperformance problem is to find a set of design
vectors, xk , that are in the isoperformance set such that
iso J (x) ≤ J (x∗ ) Non-inferiority constraint
the isoperformance constraints, feasibility constraints cr cr iso
and side bounds are all satisfied.
(14)
Step 1—Find performance-invariant set
J (x) ≤ J (x∗ )
cr,j cr,j iso
(15)
Find all xk ⊆ X Component-wise non-inferiority constraint
iso iso
for all j = 1, . . . , r with strict inequality holding for
such that for an objective vector Jz = [Jz,1 ⋅ ⋅ ⋅ Jz,m]T at least one j. Note that the feasibility constraints and
the following constraints are satisfied: side bounds on x∗ are already satisfied by virtue of
iso
step 1.
J(xk , p) − J
z iso z,req ≤ τ
J In our rocket example the risk of a design might relate
z,req (9)
Isoperformance constraints, to technology maturity of the propulsion system,
J = (x − Ig)/Ig, where Ig = 450 is a value readily
cr,1 1 sp sp sp
g(xk , p) ≤ 0, h(xk , p) = 0 Feasibility con- achievable by the current state of the art. The cost, on
iso iso
straints, (10) the other hand, might be captured by the size of the
vehicle, J = log[βx ]2. By setting β = 2 ⋅ 10–4 we
cr,2 2
x ≤ xk ≤ x W i = 1, 2, . . . , n normalize the cost of the smallest rocket to unity. We
i,LB iso,i i,UB
Side bounds. (11) can evaluate the three isoperformance designs, A, B, and
C, in terms of J and obtain
cr
52 DE WECK AND JONES
J (x1 ) = −0.09 , J (x2 ) = −0.05 , J (x3 ) = .11 . 3.2. Isoperformance Method
cr iso 5.3 cr iso 3.2 cr iso 1.3
We can visualize the isoperformance procedure (Fig. 5)
(16) by considering the various sets and domains involved
in problem solving. Figure 5(a) shows the sets involved.
All three solutions are noninferior according to Eq. The outer square represents the n-dimensional Eu-
(14), whereby the first solution is the low risk, high cost clidian plane, Rn. Designs that we could implemented
design, the second is a compromise between manufac- are bounded by set B [side constraints, Eq. (11)] and its
turing cost and development risk. and the third is the subset F [feasibility constraints, Eq. (10)]. Further-
high risk, low cost solution. Ultimately, large techno- more, in step 1 we look for the subset that satisfies the
logical risk might translate into high development costs isoperformance constraints, I, according to Eq. (9).
as well. Among this subset we look for intersection with the
Pareto-set P [see Eq. (14)], which contains the nonin-
Step 3—Select final design
ferior solutions from a cost/risk perspective (step 2).
The final design x∗∗ is said to be from among the
Select x∗∗ ∈ X∗ ⊆ X ⊆ Rn, iso
iso iso iso efficient set E.
Figures 5(b–d) show the various domains that are
according to non-quantified objectives/criteria and
involved. First, the vector of system objectives is speci-
stakeholder consensus. (17)
fied in the performance objective space J as a target
z,req
[Fig. 5(c)]. For a new system about which little is know
3. ISOPERFORMANCE METHOD AND it is conceivable that the initial target vector is set
ALGORITHM outside the feasible domain. In that case the best possi-
ble outcome is to minimize the Euclidian distance be-
tween the target vector and the closest achievable point
3.1. Advantages over One-Step
in the feasible space (goal programming). If the target
Multiobjective Optimization
vector is set inside the feasible domain, the solution will
It may be possible to arrive at a similar or even the same generally be non-unique when the number of design
solution by collapsing steps 1 and 2 into a single mul- variables exceeds the number of performance objec-
tiobjective optimization problem with two-sided in- tives, i.e., n > m. In step 1 search algorithms are
equality constraints on performance: launched from the objective space to identify feasible
members of the isoperformance set X in the design
iso
min J (x, p) = [J ⋅ ⋅ ⋅ J ]T, space [Fig. 5(b)].2 Once these have been found, they are
cr cr,1 cr,r
evaluated against secondary cost and risk criteria [Fig.
J(x, p) − J 5(d)] and the noninferior set E is retained. The final
s.t. z z,req ≤ τ, selection of x∗∗ is done based on nonquantifiable cri-
J iso
z,req teria and stakeholder consensus.
g(x, p) ≤ 0, h(x, p) = 0,
3.3. Gradient-Based Contour Following
x ≤ x ≤ x W i = 1, 2, . . . , n. (18)
i,LB i i,UB
There are a number of algorithms that have been
For simple problems the results of the two ap- proposed to identify the isoperformance set I. In de
proaches should be equivalent. For complex system Weck and Miller [2002], the following three algorithms
design problems, however, we claim two advantages of have been developed:
the isoperformance method [Eqs. (9)–(16)] over the
all-in-one optimization [Eq. (18)]: – Branch-and-Bound Design Space Explora-
tion
1. Engineering insight is obtained by solving the
– Gradient-based Contour Following
problem in subsequent steps and inspecting and
– Progressive Vector Spline Approximation
visualizing intermediate results, rather than in an
all-in-one optimization.
The interested reader is referred to de Weck and Miller
2. As the tolerance is decreased, τ → 0, the isoper-
[2002] for details on these algorithms. Nevertheless, the
formance constraint becomes an inequality con-
second algorithm, gradient-based contour following,
straint which has previously been shown by Kim
and de Weck [2005] and others to be difficult to
2One such algorithm, gradient-based contour following is discussed
solve using standard multiobjective techniques. below.
ISOPERFORMANCE: ANALYSIS AND D E S I G N O F C O M P L E X S YSTEMS WITH DESIRED OUTCOMES 53
Figure 5. (a) Venn diagram, (b–d) domains mapped in the isoperformance method.
will be briefly discussed here, since it illuminates an satisfies Eqs. (9)–(11). To find additional isoperfor-
interesting mathematical property of the isoperfor- mance points, the condition
mance approach.
Assume that we start searching for isoperformance ∇JzT,jxk ∆x ≡ 0 (21)
solutions from within the feasible domain F with a
starting point xO. Furthermore, let must be satisfied. In other words, we want to find
tangential directions that are performance invariant.
Since this must be true for all performance objectives,
j = 1, 2, . . . , m at once we define
(19)
be the Taylor decomposition of the jth objective func-
tion evaluated at xk, where (22)
∂J ∂J T (20)
∇J = z,j ⋅ ⋅ ⋅ z,j
z,j ∂x1 ∂xn
to be the system Jacobian. This is the matrix of partial
is the gradient vector of the jth objective function with
derivatives of the m performance objectives with re-
respect to the design variable vector. Initially, we follow
spect to the n design variables. Performance invariant
the steepest gradient vector to ensure that we move from
directions can be found by singular decomposition of
the initial point xO to a point xk that satisfies the
iso
the Jacobian:
isoperformance constraint. Once such a point has been
found, the algorithm switches to “contour following”
U Σ VT = ∇J T (23)
mode. Let xk = xk be an isoperformance point that z
iso
54 DE WECK AND JONES
The Σ matrix contains the singular values. Here we are frequency, isochronal or isoelectric curves or contours.
interested in the zero singular values. The correspond- These terms all share the prefix iso-, which means “the
ing columns of the V matrix span the nullspace of the same.” Graphically showing isoperformance results for
Jacobian. n > 3 is challenging.
3.4. Physics-Based versus Empirical
Models
From the discussion thus far it is clear that isoperfor-
mance requires some mathematical model that relates
(24)
the design vector x to both performance as well as
cost/risk objectives, J and J , respectively. The model
z cr
can be derived from first principles [as was done in Eq.
(1)], or obtained from empirical experimental or field
observation. In the first case the model is developed and
Any linear combination of these n - m null vectors implemented directly (deterministic case). In the sec-
points in a performance invariant direction, ond case an empirical model (with embedded statistical
__
uncertainty) must be developed first. This requires a
∆x = α ⋅ (β v + . . . + β v ) = αV β , (25) data set that relates experimental factors (x) to observed
1 m+1 n−m n o
__ outcomes (J). A variety of meta-modeling techniques
where α is an arbitrary, but small, step size and β is a such as response surfaces, kriging, or neural networks
vector of linear combination parameters. Hence, isop- are available for this purpose, and there is an abundant
erformance solutions can be obtained from the null- literature on this subject. Figure 7 illustrates these two
space of the system Jacobian matrix. Figure 6 shows for cases. Note that the isoperformance algorithms used to
a sample problem (de Weck and Miller, [2002]) in three extract the performance-invariant set Xiso, are the same
in both cases.
dimensions how the algorithm finds isoperformance
The following two sections give examples of isoper-
surfaces by following performance-invariant directions
formance applications for both the deterministic case
in the nullspace.
(spacecraft design) as well as the stochastic case (team
In this paper we are particularly interested in con-
sports).
tours and isoperformance surfaces that arise, when the
vector J represents the performance of a complex
z
technical or human system. Thus, Jz could represent the 4. EXAMPLE 1: SPACECRAFT DESIGN
pointing performance of a space telescope, average fuel
economy of a car, total output of an electrical power The design of precision optomechanical systems for
grid, or the aptitude of humans as measured by some remote sensing in space is challenging since it combines
objective criterion. In economics, relationships of this tightly coupled disciplines such as structures, optics and
type are usually called indifference curves. In sensory controls. When applied to space telescopes such as the
psychology and physiology, they are often called iso- “Nexus” spacecraft concept shown in Figure 8, we need
Figure 6. Gradient-based Contour-Following algorithm (n = 3, m = 1).
