What's so Scientific about MS/OR?
Author(s): Randolph W. Hall
Source: Interfaces, Vol. 15, No. 2 (Mar. - Apr., 1985), pp. 40-45
Published by: INFORMS
Stable URL: https://www.jstor.org/stable/25060669
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What's So Scientific about MS/OR?
RANDOLPH W. HALL Department of Industrial Engineering and
Operations Research
Etcheverry Hall
University of California
Berkeley, California 94720
Basic precepts of research in the natural sciences can be used
to guide research in MS/OR. To prosper as a science,
mathematical elegance is not enough. Attaining an improved
understanding of the real world is essential.
The Management Science/Operations definition has been addressed by Ackoff
[1971], Churchman [1970], Eilon [1975]
and Hicks [1973] (summarized in Zahedi
Research (MS/OR) profession has
often been compared to the natural sci
ences. This tradition is rooted in the ideals
[1984]). Perhaps a more relevant question
of its founders, who sought to introduce is, What can be learned from the natural
the principles of the sciences to manage sciences that can add to the success of the
rial decisions. And, certainly, the word MS/OR profession?
science in the title "Management
I will attempt to show how basic pre
Science" continues to invite such
cepts of research in the natural sciences
can be used to guide research in MS/OR. I
comparisons.
The official definition of operational re believe a greater understanding of these
search by the Operational Research Soci precepts can provide needed focus for the
ety of Britain reflects this orientation:
profession and help resolve the recent de
Operational Research is the application of the bates between "practitioners" and
methods of science to complex problems in the
"theoreticians," which seem to have split
direction and management of large systems of
the profession.
men, machines, materials and money in indus
Science
try, business, government and defence.
The question of whether MS/OR fits this
Webster's dictionary defines science
as "a possession of knowledge as distin
Copyright ? 1985, The Institute of Management Sciences
PROFESSIONAL ? MS/OR IMPLEMENTATION
0092-2102/85/1502/0040$01.25
INTERFACES 15: 2 March-April 1985 (pp. 40-45)
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SCIENTIFIC?
guished from ignorance or misun
derstanding," or "a department of sys
temized knowledge as an object of
study." The persons who coined the term
force; doubling the mass of both quad
ruples the force. Next, it says the force
decreases with the square of the distance,
the second definition in mind. Business
r; the force diminishes quite fast when
the two particles are separated. Finally,
the model demonstrates that force de
schools had been common on university
campuses for more than four decades be
pends on a universal gravitational con
stant, G, which does not depend on the
management science most likely had
fore the inception of MS/OR, and certainly
a considerable body of knowledge regard
ing the practice and theory of manage
ment had evolved. The key to MS/OR is
not the mere possession of knowledge
but the systemized attainment of knowl
edge. And although different practitioners
take different approaches, the three key
steps seem to be
(1) Modeling,
(2) Evaluating, and
(3) Deciding.
In MS/OR, systemized knowledge is re
flected in better decisions.
Modeling
As in the natural sciences, the purpose
behind modeling in MS/OR is to under
stand observable phenomena. Effective
models abstract the most important facets
of a "problem" and present them in a
form that is easy to interpret [Friedman
1935]. They make sense out of the detail
and disorder underlying the real world.
Perhaps one of the most important
models of the natural sciences is New
ton's Law of Universal Gravitation:
We should remember that
MS/OR, unlike mathematics,
is based on real events.
particle composition or anything in the
surrounding environment. The density of
the material, electrical conductivity, ob
jects in the intervening space between the
particles, and a myriad of other factors can
be ignored.
Of course, the model does not account
for all details. For instance, it is only ap
proximate if the particles are objects with
non-spherical shapes and the model ig
nores relativity. But even so, no one
would argue that it is not one of the most
important models in physics.
In the MS/OR profession, there is prob
ably no model which exceeds the impor
tance of the Economic-Order-Quantity
(EOQ) model:
Cost-**+*-&
Q 2
Like Newton's Law in physi
is simple and highlights im
tures of the real world. It ide
Much can be learned from this simple
model. First, it shows that the force be
tween two particles equals the product of
the mass of the two particles. Thus,
doubling the mass of either doubles the
relationships between the t
week and the ordering cost
rate, the order quantity, an
tory carrying charge. It also
single model can be used fo
March-April 1985 41
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HALL
A model should also be verifiable and
orders and that the only information
needed concerns certain attributes of the
based on observable phenomena. It must
order (for example, demand rate). Of capture the essence of a problem faced by
course, the model ignores details of the
practitioners of MS/OR [Dando & Sharp
1978]. And it must include the most im
real world which might be considered im
portant, such as variations in the demand
portant parameters, decision variables,
rate. But like Newton's law, no one would
and objectives, accurately illustrating their
argue that the EOQ model is not one ofrelationships. We should remember that
MS/OR, unlike mathematics, is based on
the most important in MS/OR.
To be useful, an MS/OR model must real events. It is not our role to develop
possess the same qualities as models in and judge models which properly belong
the natural sciences, the most important in other disciplines.
being that it is
Finally, as a criterion for publication,
? Understandable,
the phenomena underlying the model
? Verifiable, and
must be reproducible. The model must be
? Reproducible.
sufficiently general that it applies to con
The extent to which a model can be un
ditions which occur outside the author's
derstood depends on the tools available"laboratory." If the situation is unique,
for evaluation. However, there is inherent
the model has no broad appeal. Why tell
the world about it?
value in models which can be interpreted
on inspection. In the 1960s, considerable
Evaluating
The role of evaluating is to extract in
formation from the model. Sometimes,
The decision maker probably
even simple models can be difficult to
does not want a single soluunderstand. Understanding more compli
tion but information on sevcated models, with many equations,
eral alternatives.
parameters, and decision variables is even
more difficult. For this reason, much of
effort was expended by many in the the research in MS/OR has been devoted
MS/OR profession to model the behavior
to developing tools for interpreting
of automobile traffic. The most successful
models through evaluation. These range
models examined traffic from the macro
from drawing graphs, to simulation, to
scopic point of view and illustrated impor
advanced optimization techniques. Their
tant similarities between the flow of traffic
common aim is to make sense out of the
and the flow of fluids. Less successful model.
models examined the behavior of indi Evaluating typically entails two, often
simultaneous, activities:
vidual drivers with complicated queueing
alternatives, and
expressions. One might argue that the? Identifying
lat
ter models are more accurate, yet they
? Calculating objectives.
The most familiar technique for identify
clearly have less value. They are too dif
ing alternatives is optimization. This
ficult to interpret.
INTERFACES 15:2 42
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SCIENTIFIC?
process generally yields a single solution
which maximizes or minimizes a single
objective function. Solving simultaneous
equations and many heuristics are similar
in aiming to identify a single "solution" to
a well-defined mathematical problem.
The most commonly used technique for
identifying multiple alternatives is sen
sitivity analysis. This process can show
how the optimum changes when model
parameters change or can provide alterna
tive solutions in the vicinity of the
optimum.
The predominance of optimization is
disturbing, not just because models are
only abstractions of the real world [ Ackoff
1979], but because optimization does not
provide adequate information for making
a decision. Its aim is to find a single solu
tion. The decision maker probably does
not want a single solution but information
on several alternatives. Although sensitiv
ity analysis greatly increases the effective
ness of optimization, it too is deficient. It
only yields alternative solutions in the
vicinity of the "optimum." What the deci
sion maker generally needs are truly
unique solutions, which offer distinct
alternatives.
Very little MS/OR research has been
devoted to identifying multiple alterna
tives. One place to begin may be in the
solution process itself. In optimizing the
objective, linear program algorithms iden
tify many feasible solutions. Each solution
is a viable alternative, which the decision
maker might prefer to the "optimum."
Going one step further, new algorithms
might be designed to identify distinct al
ternatives. Descent algorithms could be
replaced with algorithms designed to
jump around to different parts of the feas
ible region, thus identifying distinct op
tions. There is much to be learned from
the optimization process, which might
eventually yield better decisions.
The second step of evaluating involves
calculating quantifiable objectives for each
alternative. The purpose is to provide the
decision maker with a measure, or meas
ures, for comparing the alternatives. Like
modeling, it leads to an improved under
standing of the underlying problem.
Therefore it is crucial that this information
Very little MS/OR research
has been devoted to identify
ing multiple alternatives.
be presented clearly and concisely. Too
much information is overwhelming, but
too little diminishes the model's credibil
ity. For instance, management would not
be satisfied with a presentation which
gave only the profits expected from alter
native marketing strategies. Neither
would they like to see information on ir
relevant factors, such as employee com
pensation plans and utility rates. Mana
gers prefer to see clear and concise infor
mation on the most important criteria,
such as cost, availability, and sales.
Deciding
The aim of MS/OR is to make better de
cisions. Therefore, the final step in the
process, deciding, is the most crucial. Yet
it is also the most elusive. There is hardly
an MS/OR analyst alive who has not been
dismayed when management failed to
implement the "optimal" strategy, or con
fused as to why it failed to choose the op
March-April 1985 43
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HALL
owner would benefit from models that
tion its own model determined to be best.
offer assistance in cutting the carpet.
The key to good decisions is knowledge
Combining the knowledge obtained from
and judgment. Modeling and evaluating
modeling with the judgment of the store
form a systemized approach for gaining
owner would likely provide the best
knowledge; judgment is acquired through
result.
experience.
Much of the MS/OR literature is di Perhaps the most important questions
management are not so well
rected towards problems which do not facing
re
defined as either the shortest path or cut
quire judgment. These are the ones which
can be formulated with well-defined ob
ting stock problem. Although there might
be a related problem which lends itself to
jective functions and solved automatically
automation, the objective function never
seems to capture all of the important fea
In the meantime, the carpet
store owner would benefit tures of the decision. One example is the
assignment problem. A great body of lit
from models that offer assiserature exists on assigning employees to
tance in cutting the carpet.
tasks so that cost is minimized or return is
maximized, and there exist efficient al
gorithms for solving these problems. Yet
with efficient algorithms. Perhaps the best
example is the shortest path problem. these
In algorithms totally ignore the human
choosing a travel route, most people element.
The first time I had to assign employees
would agree that the objective is to
to jobs was when I served as workshift
minimize the sum of the costs correspond
ing to the arcs of the path. Therefore, manager in a student housing coopera
tive. My most important consideration
companies are willing to rely on computer
software (based on shortest path al was not maximizing a linear objective
gorithms) to automatically select their function, but insuring that people as
routes.
signed to the same job were compatible
and that no one felt cheated by his or her
In a slightly different category, there are
assignment. I am still baffled at the
problems which are easy to formulate, but
thought of writing an objective function to
difficult to solve. Although in principle
capture these factors. Yet I feel I did a bet
these problems can be automated, there is
ter job because I knew how to write an
often benefit in allowing a human being
assignment matrix and apply the stepping
to pick the solution. A carpet store owner
stone method to resolve bottlenecks.
would not argue with the objective of the
The most difficult questions are neither
cutting stock problem but may be dissatis
well
defined, nor is there a related well
fied with the solutions provided by avail
able software. Whether or not this prob
lem can be solved optimally in a reason
able computation time is an open ques
tion. In the meantime, the carpet store
defined problem which can be optimized.
One example is the facilities layout prob
lem. Although various heuristics exist for
arriving at feasible layouts, none are op
INTERFACES 15:2 44
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SCIENTIFIC?
timal in the classic sense. And even the
heuristics do not come close to capturing
all of the relevant parameters, decision
variables, or objective functions. Even so,
the models provide valuable information
which helps the decision maker identify
better layouts.
Of the four types of problems men
tioned here, only the first lends itself to
natural sciences. Further attention by
MS/OR theoreticians to the principles of
science should not only improve the qual
ity of research, but also make MS/OR
more effective at addressing the complex
problems facing practitioners today.
References
Ackoff, R. L. 1971, "Frontiers of management
science/' The Bulletin (TIMS), Vol. 1, No. 2,
pp. 19-24.
automation. All others depend on both
the judgment of the decision maker and
the knowledge gained from modeling and
evaluating. Optimization may be a poor
Ackoff, R. L. 1979, "The future of operational
research is past," Journal of the Operational Re
search Society, Vol. 30, No. 2, pp. 93-104.
tool for approaching these more difficult
No. 2, pp. B37-B53.
Dando, M. R. and R. G . Sharp 1978, "Opera
tional research in the UK in 1977: The cause
problems. It discourages the analyst from
fully interpreting the model and tends to
mask important trade-offs and principles.
In making complex decisions, understand
ing trade-offs and principles is far more
crucial than knowing the model's
optimum.
Churchman, C. W. 1970, "Operations research
as a profession," Management Science, Vol. 17,
and consequences of a myth?" Journal of the
Operational Research Society, Vol. 29, No. 10,
pp. 939-950.
Eilon, S. 1975, "How scientific is OR?" Omega,
Vol. 3, No. 1, pp. 1-8.
Friedman, M., editor, 1935, "The methodology
of positive economics," in Essays in Positive
Economics, The University of Chicago Press,
Conclusions
Chicago.
Perhaps the most important similarity
between MS/OR and the natural sciences
Hicks, D. 1973, "Education for operational re
is the goal of systematically attaining an
Zahedi, F. 1984, "A survey of issues in the
improved understanding of the real
world. Mathematical elegance is not
enough. To prosper as a science, under
standing is essential.
Optimization has predominated re
search in MS/OR for far too long. It is but
one technique for addressing one part of
the MS/OR process. Moreover, it is defi
search," Omega, Vol. 1, No. 1, pp. 107-116.
MS/OR field," Interfaces, Vol. 14, No. 2,
(March-April) pp. 57-68.
cient in that it does not provide sufficient
information for making crucial decisions.
The most complex decisions require in
formation on many alternatives, as well as
understanding of basic trade-offs and
principles. Optimization alone does not
provide this information.
MS/OR has much in common with the
March-April 1985 45
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