Some personal thoughts on the
history of Operations Research
at the 30th anniversary meeting of the
Finnish Operations Research Society
Finlandia Hall, 2003 November 13
István Maros
Imperial College,
London, U.K.
i.maros@imperial.ac.uk
Founding member of the
Hungarian Operations Research Society,
currently visiting
Systems Analysis Laboratory
Helsinki University of Technology
Maros
OR memories
Personal Background
MSc in Mathematics from Eötvös Loránd University,
Budapest.
PhD in Mathematics (Operations Research) from
the Hungarian Academy of Sciences, Budapest.
Positions: Until 1990, from research assistant to
Head of Division of Applied Mathematics at the
Computer and Automation Research Institute of the
Hungarian Academy of Sciences. 1991–1992 Full
Professor at Rutgers University, New Jersey, USA,
1993–1996 Brunel University, London, 1996– present,
Department of Computing, Imperial College, London.
Research Interest:
Algorithms, computational
techniques and applications of large scale optimization.
Optimization Software: chief architect of 12
linear and network optimization systems for different
platforms.
Recent Book: I. Maros, Computational Techniques
of the Simplex Method, Kluwer Academic Publishers,
2003,
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Maros
OR memories
First Encounters
Optional course in 1962 Linear Programming by András Prékopa. Was quite
mathematical but its “immediate” applicability captured my mind ⇒ enthusiasm.
Still undergraduate: solving a real life cutting stock problem of a large iron mill
company (Hungary, 1963). Success but big lesson: OR is not just mathematics
and computing.
First years in employment: the transportation problem. Another big lesson: it is
all-important how we try to “sell” OR.
Solution of OR problems requires computers. New challenges, like theoretically
convergent algorithms may not converge in practice.
M3 ⇒ Ural ⇒ Elliott 803/B ⇒ Razdan ⇒ ICT/ICL 1900 series ⇒ R-series (IBM
360/370 clones) ⇒ “Unreliable” computing.
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OR memories
Linear Programming
Main solution algorithm: the Simplex Method: (a set of rules for basis changes).
First simplex code I have ever seen: 30 lines in Basic including I/O statements.
Today’s simplex solvers consist of 15,000 – 50,000 lines of code. WHY?
Enormous efforts to make them robust, efficient, portable, maintainable, etc. This
is achieved by using (i) advanced algorithmic procedures, (ii) relevant results of
computer science, (iii) advanced software engineering techniques, and (iv) utilizing
advances in hardware technology.
The result: 2–3 orders of magnitude improvement of this tool.
Similar spectacular improvements have been achieved in many other areas of the
computational tools of OR.
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OR memories
Development of LP Solutions with Simplex
Problem: p19, size m = 285, n = 586, # of nonzeros: 5891, # of upper bounded
variables: 189, # of range constraints: 54.
Year
Computer
1972
1984
1986
1986
1989
1996
2002
ICL 1903A
ICL ME29
IBM PC i8088
IBM PC i8088
IBM PC i80386
IBM PC P/90
Pentium 1GHz
Memory
Sol. time
32K words
unknown
256KB
512KB
1MB
16MB
512MB
48 mins
12 mins
27 mins
11 mins
21 secs
0.77 secs
<0.05 secs
Remark
w/o arithmetic coprocessor
with arithmetic coprocessor
with arithmetic coprocessor
not measurable
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Conditions of Success of OR
Do we know them (all)?
They change over time.
Well established applications. Still many more unutilized areas. Role of education:
spread the OR culture thus raise the demand for it among practitioners.
Nowadays a main obstacle: pressure for immediate results (returns). Persuasion,
high quality references, change of attitude.
The important role of scientific societies: a well represented community has a
better chance to achieve its mission and contribute to the necessary change of
attitude.
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Maros
OR memories
Kiitos
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