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, 1 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. 2 Maros 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. 3 Maros 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 4 Maros OR memories 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. 5 Maros OR memories Kiitos 6