Operations research (British English: operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialismOR, is a discipline that deals with the development and application of analytical methods to improve decision-making.[1] The term management science is occasionally used as a synonym.[2]
Employing techniques from other mathematical sciences, such as modeling, statistics, and optimization, operations research arrives at optimal or near-optimal solutions to decision-making problems. Because of its emphasis on practical applications, operations research has overlapped with many other disciplines, notably industrial engineering. Operations research is often concerned with determining the extreme values of some real-world objective: the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost). Originating in military efforts before World War II, its techniques have grown to concern problems in a variety of industries.[3]
The major sub-disciplines (but not limited to) in modern operational research, as identified by the journal Operations Research[5] and The Journal of the Operational Research Society[6] are:
In the decades after the two world wars, the tools of operations research were more widely applied to problems in business, industry, and society. Since that time, operational research has expanded into a field widely used in industries ranging from petrochemicals to airlines, finance, logistics, and government, moving to a focus on the development of mathematical models that can be used to analyse and optimize sometimes complex systems, and has become an area of active academic and industrial research.[3]
Modern operational research originated at the Bawdsey Research Station in the UK in 1937 as the result of an initiative of the station's superintendent, A. P. Rowe and Robert Watson-Watt.[10] Rowe conceived the idea as a means to analyse and improve the working of the UK's early-warning radar system, code-named "Chain Home" (CH). Initially, Rowe analysed the operating of the radar equipment and its communication networks, expanding later to include the operating personnel's behaviour. This revealed unappreciated limitations of the CH network and allowed remedial action to be taken.[11]
The modern field of operational research arose during World War II.[dubious–discuss] In the World War II era, operational research was defined as "a scientific method of providing executive departments with a quantitative basis for decisions regarding the operations under their control".[12] Other names for it included operational analysis (UK Ministry of Defence from 1962)[13] and quantitative management.[14]
During the Second World War close to 1,000 men and women in Britain were engaged in operational research. About 200 operational research scientists worked for the British Army.[15]
Patrick Blackett worked for several different organizations during the war. Early in the war while working for the Royal Aircraft Establishment (RAE) he set up a team known as the "Circus" which helped to reduce the number of anti-aircraft artillery rounds needed to shoot down an enemy aircraft from an average of over 20,000 at the start of the Battle of Britain to 4,000 in 1941.[16]
A Liberator in standard RAF green/dark earth/black night bomber finish as originally used by Coastal Command
In 1941, Blackett moved from the RAE to the Navy, after first working with RAF Coastal Command, in 1941 and then early in 1942 to the Admiralty.[17] Blackett's team at Coastal Command's Operational Research Section (CC-ORS) included two future Nobel prize winners and many other people who went on to be pre-eminent in their fields.[18][19] They undertook a number of crucial analyses that aided the war effort. Britain introduced the convoy system to reduce shipping losses, but while the principle of using warships to accompany merchant ships was generally accepted, it was unclear whether it was better for convoys to be small or large. Convoys travel at the speed of the slowest member, so small convoys can travel faster. It was also argued that small convoys would be harder for German U-boats to detect. On the other hand, large convoys could deploy more warships against an attacker. Blackett's staff showed that the losses suffered by convoys depended largely on the number of escort vessels present, rather than the size of the convoy. Their conclusion was that a few large convoys are more defensible than many small ones.[20]
While performing an analysis of the methods used by RAF Coastal Command to hunt and destroy submarines, one of the analysts asked what colour the aircraft were. As most of them were from Bomber Command they were painted black for night-time operations. At the suggestion of CC-ORS a test was run to see if that was the best colour to camouflage the aircraft for daytime operations in the grey North Atlantic skies. Tests showed that aircraft painted white were on average not spotted until they were 20% closer than those painted black. This change indicated that 30% more submarines would be attacked and sunk for the same number of sightings.[21] As a result of these findings Coastal Command changed their aircraft to using white undersurfaces.
Other work by the CC-ORS indicated that on average if the trigger depth of aerial-delivered depth charges were changed from 100 to 25 feet, the kill ratios would go up. The reason was that if a U-boat saw an aircraft only shortly before it arrived over the target then at 100 feet the charges would do no damage (because the U-boat wouldn't have had time to descend as far as 100 feet), and if it saw the aircraft a long way from the target it had time to alter course under water so the chances of it being within the 20-foot kill zone of the charges was small. It was more efficient to attack those submarines close to the surface when the targets' locations were better known than to attempt their destruction at greater depths when their positions could only be guessed. Before the change of settings from 100 to 25 feet, 1% of submerged U-boats were sunk and 14% damaged. After the change, 7% were sunk and 11% damaged; if submarines were caught on the surface but had time to submerge just before being attacked, the numbers rose to 11% sunk and 15% damaged. Blackett observed "there can be few cases where such a great operational gain had been obtained by such a small and simple change of tactics".[22]
Bomber Command's Operational Research Section (BC-ORS), analyzed a report of a survey carried out by RAF Bomber Command.[citation needed] For the survey, Bomber Command inspected all bombers returning from bombing raids over Germany over a particular period. All damage inflicted by German air defenses was noted and the recommendation was given that armor be added in the most heavily damaged areas. This recommendation was not adopted because the fact that the aircraft were able to return with these areas damaged indicated the areas were not vital, and adding armor to non-vital areas where damage is acceptable reduces aircraft performance. Their suggestion to remove some of the crew so that an aircraft loss would result in fewer personnel losses, was also rejected by RAF command. Blackett's team made the logical recommendation that the armor be placed in the areas which were completely untouched by damage in the bombers who returned. They reasoned that the survey was biased, since it only included aircraft that returned to Britain. The areas untouched in returning aircraft were probably vital areas, which, if hit, would result in the loss of the aircraft.[23] This story has been disputed,[24] with a similar damage assessment study completed in the US by the Statistical Research Group at Columbia University,[25] the result of work done by Abraham Wald.[26]
When Germany organized its air defences into the Kammhuber Line, it was realized by the British that if the RAF bombers were to fly in a bomber stream they could overwhelm the night fighters who flew in individual cells directed to their targets by ground controllers. It was then a matter of calculating the statistical loss from collisions against the statistical loss from night fighters to calculate how close the bombers should fly to minimize RAF losses.[27]
The "exchange rate" ratio of output to input was a characteristic feature of operational research. By comparing the number of flying hours put in by Allied aircraft to the number of U-boat sightings in a given area, it was possible to redistribute aircraft to more productive patrol areas. Comparison of exchange rates established "effectiveness ratios" useful in planning. The ratio of 60 mines laid per ship sunk was common to several campaigns: German mines in British ports, British mines on German routes, and United States mines in Japanese routes.[28]
Operational research doubled the on-target bomb rate of B-29s bombing Japan from the Marianas Islands by increasing the training ratio from 4 to 10 percent of flying hours; revealed that wolf-packs of three United States submarines were the most effective number to enable all members of the pack to engage targets discovered on their individual patrol stations; revealed that glossy enamel paint was more effective camouflage for night fighters than conventional dull camouflage paint finish, and a smooth paint finish increased airspeed by reducing skin friction.[28]
On land, the operational research sections of the Army Operational Research Group (AORG) of the Ministry of Supply (MoS) were landed in Normandy in 1944, and they followed British forces in the advance across Europe. They analyzed, among other topics, the effectiveness of artillery, aerial bombing and anti-tank shooting.
After World War II
In 1947, under the auspices of the British Association, a symposium was organized in Dundee. In his opening address, Watson-Watt offered a definition of the aims of OR:
"To examine quantitatively whether the user organization is getting from the operation of its equipment the best attainable contribution to its overall objective."[10]
With expanded techniques and growing awareness of the field at the close of the war, operational research was no longer limited to only operational, but was extended to encompass equipment procurement, training, logistics and infrastructure. Operations research also grew in many areas other than the military once scientists learned to apply its principles to the civilian sector. The development of the simplex algorithm for linear programming was in 1947.[29]
In the 1950s, the term Operations Research was used to describe heterogeneous mathematical methods such as game theory, dynamic programming, linear programming, warehousing, spare parts theory, queue theory, simulation and production control, which were used primarily in civilian industry. Scientific societies and journals on the subject of operations research were founded in the 1950s, such as the Operation Research Society of America (ORSA) in 1952 and the Institute for Management Science (TIMS) in 1953.[30] Philip Morse, the head of the Weapons Systems Evaluation Group of the Pentagon, became the first president of ORSA and attracted the companies of the military-industrial complex to ORSA, which soon had more than 500 members. In the 1960s, ORSA reached 8000 members.[citation needed] Consulting companies also founded OR groups. In 1953, Abraham Charnes and William Cooper published the first textbook on Linear Programming.[citation needed]
In the 1950s and 1960s, chairs of operations research were established in the U.S. and United Kingdom (from 1964 in Lancaster) in the management faculties of universities. Further influences from the U.S. on the development of operations research in Western Europe can be traced here. The authoritative[citation needed] OR textbooks from the U.S. were published in Germany in German language and in France in French (but not in Italian[citation needed]), such as the book by George Dantzig "Linear Programming"(1963) and the book by C. West Churchman et al. "Introduction to Operations Research"(1957). The latter was also published in Spanish in 1973, opening at the same time Latin American readers to Operations Research. NATO gave important impulses for the spread of Operations Research in Western Europe; NATO headquarters (SHAPE) organised four conferences on OR in the 1950s – the one in 1956 with 120 participants – bringing OR to mainland Europe. Within NATO, OR was also known as "Scientific Advisory" (SA) and was grouped together in the Advisory Group of Aeronautical Research and Development (AGARD). SHAPE and AGARD organized an OR conference in April 1957 in Paris. When France withdrew from the NATO military command structure, the transfer of NATO headquarters from France to Belgium led to the institutionalization of OR in Belgium, where Jacques Drèze founded CORE, the Center for Operations Research and Econometrics at the Catholic University of Leuven in 1966.[citation needed]
With the development of computers over the next three decades, Operations Research can now solve problems with hundreds of thousands of variables and constraints. Moreover, the large volumes of data required for such problems can be stored and manipulated very efficiently."[29] Much of operations research (modernly known as 'analytics') relies upon stochastic variables and a therefore access to truly random numbers. Fortunately, the cybernetics field also required the same level of randomness. The development of increasingly better random number generators has been a boon to both disciplines. Modern applications of operations research includes city planning, football strategies, emergency planning, optimizing all facets of industry and economy, and undoubtedly with the likelihood of the inclusion of terrorist attack planning and definitely counterterrorist attack planning. More recently, the research approach of operations research, which dates back to the 1950s, has been criticized for being collections of mathematical models but lacking an empirical basis of data collection for applications. How to collect data is not presented in the textbooks. Because of the lack of data, there are also no computer applications in the textbooks.[31]
Problems addressed
Critical path analysis or project planning: identifying those processes in a multiple-dependency project which affect the overall duration of the project
Routing, such as determining the routes of buses so that as few buses are needed as possible
Supply chain management: managing the flow of raw materials and products based on uncertain demand for the finished products
Project production activities: managing the flow of work activities in a capital project in response to system variability through operations research tools for variability reduction and buffer allocation using a combination of allocation of capacity, inventory and time[32][33]
Efficient messaging and customer response tactics
Automation: automating or integrating robotic systems in human-driven operations processes
Globalization: globalizing operations processes in order to take advantage of cheaper materials, labor, land or other productivity inputs
Finding the optimal parameter (weights) setting of an algorithm that generates the realisation of a figured bass in Baroque compositions (classical music) by using weighted local cost and transition cost rules
Operational research is also used extensively in government where evidence-based policy is used.
The field of management science (MS) is known as using operations research models in business.[34]Stafford Beer characterized this in 1967.[35] Like operational research itself, management science is an interdisciplinary branch of applied mathematics devoted to optimal decision planning, with strong links with economics, business, engineering, and other sciences. It uses various scientificresearch-based principles, strategies, and analytical methods including mathematical modeling, statistics and numerical algorithms to improve an organization's ability to enact rational and meaningful management decisions by arriving at optimal or near-optimal solutions to sometimes complex decision problems. Management scientists help businesses to achieve their goals using the scientific methods of operational research.
The management scientist's mandate is to use rational, systematic, science-based techniques to inform and improve decisions of all kinds. Of course, the techniques of management science are not restricted to business applications but may be applied to military, medical, public administration, charitable groups, political groups or community groups.
Management science is concerned with developing and applying models and concepts that may prove useful in helping to illuminate management issues and solve managerial problems, as well as designing and developing new and better models of organizational excellence.[36]
Related fields
Some of the fields that have considerable overlap with Operations Research and Management Science include:[37]
Applications are abundant such as in airlines, manufacturing companies, service organizations, military branches, and government. The range of problems and issues to which it has contributed insights and solutions is vast. It includes:[36]
Scheduling (of airlines, trains, buses etc.)
Assignment (assigning crew to flights, trains or buses; employees to projects; commitment and dispatch of power generation facilities)
Facility location (deciding most appropriate location for new facilities such as warehouses; factories or fire station)
Hydraulics & Piping Engineering (managing flow of water from reservoirs)
Health Services (information and supply chain management)
Game Theory (identifying, understanding; developing strategies adopted by companies)
Management is also concerned with so-called soft-operational analysis which concerns methods for strategic planning, strategic decision support, problem structuring methods. In dealing with these sorts of challenges, mathematical modeling and simulation may not be appropriate or may not suffice. Therefore, during the past 30 years[vague], a number of non-quantified modeling methods have been developed. These include:[citation needed]
In 2004, the US-based organization INFORMS began an initiative to market the OR profession better, including a website entitled The Science of Better[54] which provides an introduction to OR and examples of successful applications of OR to industrial problems. This initiative has been adopted by the Operational Research Society in the UK, including a website entitled Learn About OR.[55]
European Journal of Operational Research (EJOR): Founded in 1975 and is presently[when?] by far the largest operational research journal in the world, with its around 9,000 pages of published papers per year. In 2004, its total number of citations was the second largest amongst Operational Research and Management Science journals;
INFOR Journal: published and sponsored by the Canadian Operational Research Society;
Journal of Defense Modeling and Simulation (JDMS): Applications, Methodology, Technology: a quarterly journal devoted to advancing the science of modeling and simulation as it relates to the military and defense.[64]
George Bernard Dantzig was an American mathematical scientist who made contributions to industrial engineering, operations research, computer science, economics, and statistics.
Management science is a wide and interdisciplinary study of solving complex problems and making strategic decisions as it pertains to institutions, corporations, governments and other types of organizational entities. It is closely related to management, economics, business, engineering, management consulting, and other fields. It uses various scientific research-based principles, strategies, and analytical methods including mathematical modeling, statistics and numerical algorithms and aims to improve an organization's ability to enact rational and accurate management decisions by arriving at optimal or near optimal solutions to complex decision problems.
Systems engineering is an interdisciplinary field of engineering and engineering management that focuses on how to design, integrate, and manage complex systems over their life cycles. At its core, systems engineering utilizes systems thinking principles to organize this body of knowledge. The individual outcome of such efforts, an engineered system, can be defined as a combination of components that work in synergy to collectively perform a useful function.
Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making. It is also known as multiple attribute utility theory, multiple attribute value theory, multiple attribute preference theory, and multi-objective decision analysis.
Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the Computer Sciences, which uses advanced computing capabilities to understand and solve complex physical problems. While this discussion typically extenuates into Visual Computation, this research field of study will typically include the following research categorizations.
Ronald Arthur Howard was an emeritus professor in the Department of Engineering-Economic Systems in the School of Engineering at Stanford University.
Management cybernetics is concerned with the application of cybernetics to management and organizations. "Management cybernetics" was first introduced by Stafford Beer in the late 1950s and introduces the various mechanisms of self-regulation applied by and to organizational settings, as seen through a cybernetics perspective. Beer developed the theory through a combination of practical applications and a series of influential books. The practical applications involved steel production, publishing and operations research in a large variety of different industries. Some consider that the full flowering of management cybernetics is represented in Beer's books. However, learning continues.
David Bendel Hertz was an operations research practitioner and academic, known for various contributions to the discipline, and specifically, and more widely, for pioneering the use of Monte Carlo methods in finance. He developed innovative modeling approaches for the solution of complex management issues. His earliest publications added insights to the industrial process of research and development.
Modeling and simulation (M&S) is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making.
Ravindra K. Ahuja is an Indian-born American computer scientist and entrepreneur. He is currently Professor of Industrial and Systems Engineering at the University of Florida in Gainesville, Florida, and CEO of the automation and optimization solutions provider Optym, which he founded in 2000 as Innovative Scheduling, Inc.
Fred Glover is Chief Scientific Officer of Entanglement, Inc., USA, in charge of algorithmic design and strategic planning for applications of combinatorial optimization in quantum computing. He also holds the title of Distinguished University Professor, Emeritus, at the University of Colorado, Boulder, associated with the College of Engineering and Applied Science and the Leeds School of Business. He is known for his innovations in the area of metaheuristics including the computer-based optimization methodology of Tabu search an adaptive memory programming algorithm for mathematical optimization, and the associated evolutionary Scatter Search and Path Relinking algorithms.
Anna Nagurney is an American mathematician, economist, educator and writer in the field of Operations Management. Nagurney is the Eugene M. Isenberg Chair in Integrative Studies in the Isenberg School of Management at the University of Massachusetts Amherst in Amherst, Massachusetts. Previously, she held the John F. Smith Memorial Professorship of Operations Management at the Isenberg School of Management from 1998 to 2021.
The Operational Research Society, also known as The OR Society, is an international learned society in the field of operational research.The OR Society is a leading professional body for operational research (OR) practitioners and academics. Based in the United Kingdom, the society promotes the understanding and use of operational research in addressing real-world problems across various sectors, including industry, government, and academia.
William Wager Cooper was an American operations researcher, known as a father of management science and as "Mr. Linear Programming". He was the founding president of The Institute of Management Sciences, founding editor-in-chief of Auditing: A Journal of Practice and Theory, a founding faculty member of the Graduate School of Industrial Administration at the Carnegie Institute of Technology, founding dean of the School of Urban and Public Affairs at CMU, the former Arthur Lowes Dickinson Professor of Accounting at Harvard University, and the Foster Parker Professor Emeritus of Management, Finance and Accounting at the University of Texas at Austin.
Michel Louis Balinski was an American and French applied mathematician, economist, operations research analyst and political scientist. Educated in the United States, from 1980 he lived and worked in France. He was known for his work in optimisation, convex polyhedra, stable matching, and the theory and practice of electoral systems, jury decision, and social choice. He was Directeur de Recherche de classe exceptionnelle (emeritus) of the C.N.R.S. at the École Polytechnique (Paris). He was awarded the John von Neumann Theory Prize by INFORMS in 2013.
The Gesellschaft für Operations Research (GOR) is the professional non-profit society for the scientific field of Operations Research in Germany. The society is a member of the European umbrella organization, the Association of European Operational Research Societies (EURO), and of the International Federation of Operational Research Societies (IFORS).
Valerie Belton, commonly known as Val Belton, is a retired professor of management science at University of Strathclyde. She is a researcher who has worked on the design and application of multi-criteria decision making (MCDM) approaches for over 30 years. She co-authored a book on this field Multicriteria Decision Analysis: An Integrated Approach, that was released in 2002. She has attempted to incorporate multi-criteria decision analysis with problem structuring techniques, system dynamics, and other analytical approaches. She has a number of scholarly articles to her name and served as the editor of the journal Multi-Criteria Decision Analysis.
Sally Brailsford is a British professor of management science within Southampton Business School at the University of Southampton. Her research focuses on operational research and healthcare modelling to evaluate treatments and screening programmes, and to improve health service delivery.
Paolo Toth is an Italian scientist and engineer, and an Emeritus Professor of Operations Research at the University of Bologna. He is known for his research in operations research and mathematical programming. He made significant contributions in the areas of vehicle routing, knapsack and other cutting and packing problems, train scheduling, set covering, vertex coloring and, in general, combinatorial optimization. As of 2023, he published over 170 peer-reviewed articles and was cited more than 10,000 times.
Michel Bierlaire is a Belgian-Swiss applied mathematician specialized in transportation modeling and optimization. He is a professor at EPFL and the head of the Transport and Mobility Laboratory.
↑ Wetherbe, James C. (1979), Systems analysis for computer-based information systems, West series in data processing and information systems, West Pub. Co., ISBN9780829902280, A systems analyst who contributes in the area of DSS must be skilled in such areas as management science (synonymous with decision science and operation research), modeling, simulation, and advanced statistics.
↑ "Operational Research in the British Army 1939–1945", October 1947, Report C67/3/4/48, UK National Archives file WO291/1301 Quoted on the dust-jacket of: Morse, Philip M, and Kimball, George E, Methods of Operation Research, 1st edition revised, MIT Press & J Wiley, 5th printing, 1954.
↑ UK National Archives Catalogue for WO291 lists a War Office organisation called Army Operational Research Group (AORG) that existed from 1946 to 1962. "In January 1962 the name was changed to Army Operational Research Establishment (AORE). Following the creation of a unified Ministry of Defence, a tri-service operational research organisation was established: the Defence Operational Research Establishment (DOAE) which was formed in 1965, and it absorbed the Army Operational Research Establishment based at West Byfleet."
↑ Wallis, W. Allen (1980). "The Statistical Research Group, 1942–1945". Journal of the American Statistical Association. 75 (370): 320–330. doi:10.1080/01621459.1980.10477469.
↑ Mangel, Marc; Samaniego, Francisco J (1984). "Abraham Wald's Work on Aircraft Survivability". Journal of the American Statistical Association. 79 (386): 259. doi:10.2307/2288257. JSTOR2288257.
↑ Richard Vahrenkamp: Mathematical Management – Operations Research in the United States and Western Europe, 1945 – 1990, in: Management Revue – Socio-Economic Studies, vol. 34 (2023), issue 1, pp. 69–91
↑ "TOP". Springer.com. Retrieved 13 November 2011.
Further reading
Classic books and articles
R. E. Bellman, Dynamic Programming, Princeton University Press, Princeton, 1957
Abraham Charnes, William W. Cooper, Management Models and Industrial Applications of Linear Programming, Volumes I and II, New York, John Wiley & Sons, 1961
Abraham Charnes, William W. Cooper, A. Henderson, An Introduction to Linear Programming, New York, John Wiley & Sons, 1953
C. West Churchman, Russell L. Ackoff & E. L. Arnoff, Introduction to Operations Research, New York: J. Wiley and Sons, 1957
George B. Dantzig, Linear Programming and Extensions, Princeton, Princeton University Press, 1963
Lester K. Ford, Jr., D. Ray Fulkerson, Flows in Networks, Princeton, Princeton University Press, 1962
Jay W. Forrester, Industrial Dynamics, Cambridge, MIT Press, 1961
L. V. Kantorovich, "Mathematical Methods of Organizing and Planning Production" Management Science, 4, 1960, 266–422
Ralph Keeney, Howard Raiffa, Decisions with Multiple Objectives: Preferences and Value Tradeoffs, New York, John Wiley & Sons, 1976
H. W. Kuhn, "The Hungarian Method for the Assignment Problem," Naval Research Logistics Quarterly, 1–2, 1955, 83–97
H. W. Kuhn, A. W. Tucker, "Nonlinear Programming," pp.481–492 in Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability
B. O. Koopman, Search and Screening: General Principles and Historical Applications, New York, Pergamon Press, 1980
Tjalling C. Koopmans, editor, Activity Analysis of Production and Allocation, New York, John Wiley & Sons, 1951
Charles C. Holt, Franco Modigliani, John F. Muth, Herbert A. Simon, Planning Production, Inventories, and Work Force, Englewood Cliffs, NJ, Prentice-Hall, 1960
Philip M. Morse, George E. Kimball, Methods of Operations Research, New York, MIT Press and John Wiley & Sons, 1951
Robert O. Schlaifer, Howard Raiffa, Applied Statistical Decision Theory, Cambridge, Division of Research, Harvard Business School, 1961
Classic textbooks
Taha, Hamdy A., "Operations Research: An Introduction", Pearson, 10th Edition, 2016
Frederick S. Hillier & Gerald J. Lieberman, Introduction to Operations Research, McGraw-Hill: Boston MA; 10th Edition, 2014
Robert J. Thierauf & Richard A. Grosse, "Decision Making Through Operations Research", John Wiley & Sons, INC, 1970
Harvey M. Wagner, Principles of Operations Research, Englewood Cliffs, Prentice-Hall, 1969
Wentzel (Ventsel), E. S.Introduction to Operations Research, Moscow: Soviet Radio Publishing House, 1964.
History
Saul I. Gass, Arjang A. Assad, An Annotated Timeline of Operations Research: An Informal History. New York, Kluwer Academic Publishers, 2005.
Saul I. Gass (Editor), Arjang A. Assad (Editor), Profiles in Operations Research: Pioneers and Innovators. Springer, 2011
Maurice W. Kirby (Operational Research Society (Great Britain)). Operational Research in War and Peace: The British Experience from the 1930s to 1970, Imperial College Press, 2003. ISBN1-86094-366-7, ISBN978-1-86094-366-9
J. K. Lenstra, A. H. G. Rinnooy Kan, A. Schrijver (editors) History of Mathematical Programming: A Collection of Personal Reminiscences, North-Holland, 1991
Charles W. McArthur, Operations Analysis in the U.S. Army Eighth Air Force in World War II, History of Mathematics, Vol. 4, Providence, American Mathematical Society, 1990
C. H. Waddington, O. R. in World War 2: Operational Research Against the U-boat, London, Elek Science, 1973.
Richard Vahrenkamp: Mathematical Management – Operations Research in the United States and Western Europe, 1945 – 1990, in: Management Revue – Socio-Economic Studies, vol. 34 (2023), issue 1, pp.69–91.
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