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Operations research
View on WikipediaThe examples and perspective in this article may not represent a worldwide view of the subject. The specific issue is: US perspective completely neglected, George Dantzig gets a passing mention only (December 2020) |
Operations research (British English: operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialism OR, is a branch of applied mathematics that deals with the development and application of analytical methods to improve management and decision-making.[1][2] The term management science is occasionally used as a synonym.[3]
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.[4]
Overview
[edit]Operations research (OR) encompasses the development and the use of a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queueing theory and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, ordinal priority approach, neural networks, expert systems, decision analysis, and the analytic hierarchy process.[5] Nearly all of these techniques involve the construction of mathematical models that attempt to describe the system. Because of the computational and statistical nature of most of these fields, OR also has strong ties to computer science and analytics. Operational researchers faced with a new problem must determine which of these techniques are most appropriate given the nature of the system, the goals for improvement, and constraints on time and computing power, or develop a new technique specific to the problem at hand (and, afterwards, to that type of problem).
The major sub-disciplines (but not limited to) in modern operational research, as identified by the journal Operations Research[6] and The Journal of the Operational Research Society [7] are:
- Computing and information technologies
- Financial engineering
- Manufacturing, service sciences, and supply chain management
- Policy modeling and public sector work
- Revenue management
- Simulation
- Stochastic models
- Transportation theory
- Game theory for strategies
- Linear programming
- Nonlinear programming
- Integer programming in NP-complete problem specially for 0-1 integer linear programming for binary
- Dynamic programming in Aerospace engineering and Economics
- Information theory used in Cryptography, Quantum computing
- Quadratic programming for solutions of Quadratic equation and Quadratic function
History
[edit]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.[4]
Historical origins
[edit]In the 17th century, mathematicians Blaise Pascal and Christiaan Huygens solved problems involving sometimes complex decisions (problem of points) by using game-theoretic ideas and expected values; others, such as Pierre de Fermat and Jacob Bernoulli, solved these types of problems using combinatorial reasoning instead.[8] Charles Babbage's research into the cost of transportation and sorting of mail led to England's universal "Penny Post" in 1840, and to studies into the dynamical behaviour of railway vehicles in defence of the GWR's broad gauge.[9] Beginning in the 20th century, study of inventory management could be considered[by whom?] the origin of modern operations research with economic order quantity developed by Ford W. Harris in 1913. Operational research may[original research?] have originated in the efforts of military planners during World War I (convoy theory and Lanchester's laws). Percy Bridgman brought operational research to bear on problems in physics in the 1920s and would later attempt to extend these to the social sciences.[10]
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.[11] 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.[12]
Scientists in the United Kingdom (including Patrick Blackett (later Lord Blackett OM PRS), Cecil Gordon, Solly Zuckerman, (later Baron Zuckerman OM, KCB, FRS), C. H. Waddington, Owen Wansbrough-Jones, Frank Yates, Jacob Bronowski and Freeman Dyson), and in the United States (George Dantzig) looked for ways to make better decisions in such areas as logistics and training schedules.
Second World War
[edit]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".[13] Other names for it included operational analysis (UK Ministry of Defence from 1962)[14] and quantitative management.[15]
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.[16]
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.[17]

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.[18] 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.[19][20] 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.[21]
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.[22] 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".[23]

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.[24] This story has been disputed,[25] with a similar damage assessment study completed in the US by the Statistical Research Group at Columbia University,[26] the result of work done by Abraham Wald.[27]
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.[28]
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.[29]
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.[29]
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
[edit]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."[11]
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.[30]
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.[31] 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."[30] 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.[32]
Problems addressed
[edit]- Critical path analysis or project planning: identifying those processes in a multiple-dependency project which affect the overall duration of the project
- Floorplanning: designing the layout of equipment in a factory or components on a computer chip to reduce manufacturing time (therefore reducing cost)
- Network optimization: for instance, setup of telecommunications or power system networks to maintain quality of service during outages
- Resource allocation problems
- Facility location
- Assignment Problems:
- Bayesian search theory: looking for a target
- Optimal search
- 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[33][34]
- 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
- Transportation: managing freight transportation and delivery systems (Examples: LTL shipping, intermodal freight transport, travelling salesman problem, driver scheduling problem)
- Scheduling:
- Personnel staffing
- Manufacturing steps
- Project tasks
- Network data traffic: these are known as queueing models or queueing systems.
- Sports events and their television coverage
- Blending of raw materials in oil refineries
- Determining optimal prices, in many retail and B2B settings, within the disciplines of pricing science
- Cutting stock problem: Cutting small items out of bigger ones.
- 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.
Management science
[edit]The field of management science (MS) is known as using operations research models in business.[35] Stafford Beer characterized this in 1967.[36] 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 scientific research-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.[37]
Related fields
[edit]Some of the fields that have considerable overlap with Operations Research and Management Science include:[38]
- Artificial Intelligence
- Business analytics
- Computer science
- Data mining/Data science/Big data
- Decision analysis
- Decision intelligence
- Engineering
- Financial engineering
- Forecasting
- Game theory
- Geography/Geographic information science
- Graph theory
- Industrial engineering
- Inventory control
- Logistics
- Mathematical modeling
- Mathematical optimization
- Probability and statistics
- Project management
- Policy analysis
- Queueing theory
- Simulation
- Social network/Transportation forecasting models
- Stochastic processes
- Supply chain management
- Systems engineering
Applications
[edit]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:[37]
- 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)
- Urban Design
- Computer Network Engineering (packet routing; timing; analysis)
- Telecom & Data Communication Engineering (packet routing; timing; analysis)
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]
- stakeholder based approaches including metagame analysis and drama theory
- morphological analysis and various forms of influence diagrams
- cognitive mapping
- strategic choice
- robustness analysis
Societies and journals
[edit]Societies
[edit]The International Federation of Operational Research Societies (IFORS)[40] is an umbrella organization for operational research societies worldwide, representing approximately 50 national societies including those in the US,[41] UK,[42] France,[43] Germany, Italy,[44] Canada,[45] Australia,[46] New Zealand,[47] Philippines,[48] India,[49] Japan and South Africa.[50] For the institutionalization of Operations Research, the foundation of IFORS in 1960 was of decisive importance, which stimulated the foundation of national OR societies in Austria, Switzerland and Germany. IFORS held important international conferences every three years since 1957.[51] The constituent members of IFORS form regional groups, such as that in Europe, the Association of European Operational Research Societies (EURO).[52] Other important operational research organizations are Simulation Interoperability Standards Organization (SISO)[53] and Interservice/Industry Training, Simulation and Education Conference (I/ITSEC)[54]
In 2004, the US-based organization INFORMS began an initiative to market the OR profession better, including a website entitled The Science of Better[55] 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.[56]
Journals of INFORMS
[edit]The Institute for Operations Research and the Management Sciences (INFORMS) publishes thirteen scholarly journals about operations research, including the top two journals in their class, according to 2005 Journal Citation Reports.[57] They are:
- Decision Analysis[58]
- Information Systems Research[59]
- INFORMS Journal on Computing[60]
- INFORMS Transactions on Education[61] (an open access journal)
- Interfaces[62]
- Management Science
- Manufacturing & Service Operations Management
- Marketing Science
- Mathematics of Operations Research
- Operations Research
- Organization Science[63]
- Service Science[64]
- Transportation Science
Other journals
[edit]These are listed in alphabetical order of their titles.
- 4OR-A Quarterly Journal of Operations Research: jointly published the Belgian, French and Italian Operations Research Societies (Springer);
- Decision Sciences published by Wiley-Blackwell on behalf of the Decision Sciences Institute
- 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.[65]
- Journal of the Operational Research Society (JORS): an official journal of The OR Society; this is the oldest continuously published journal of OR in the world, published by Taylor & Francis;
- Military Operations Research (MOR): published by the Military Operations Research Society;
- Omega - The International Journal of Management Science;
- Operations Research Letters;
- Opsearch: official journal of the Operational Research Society of India;
- OR Insight: a quarterly journal of The OR Society published by Palgrave;[66]
- Pesquisa Operacional, the official journal of the Brazilian Operations Research Society
- Production and Operations Management, the official journal of the Production and Operations Management Society
- TOP: the official journal of the Spanish Statistics and Operations Research Society.[67]
See also
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References
[edit]- ^ "Operations research | Definition, History, Examples, & Facts". Britannica.
- ^ "What is O.R.?". INFORMS.org. Retrieved 7 January 2012.
- ^ Wetherbe, James C. (1979), Systems analysis for computer-based information systems, West series in data processing and information systems, West Pub. Co., ISBN 9780829902280,
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.
- ^ a b "What is OR". HSOR.org. Archived from the original on 18 November 2010. Retrieved 13 November 2011.
- ^ "Operations Research Analysts". Bls.gov. Retrieved 27 January 2012.
- ^ "OR / Pubs / IOL Home". INFORMS.org. 2 January 2009. Archived from the original on 27 May 2009. Retrieved 13 November 2011.
- ^ Petropoulos, Fotios; Laporte, Gilbert; Aktas, Emel; Alumur, Sibel A.; Archetti, Claudia; Ayhan, Hayriye; Battarra, Maria; Bennell, Julia A.; Bourjolly, Jean-Marie; Boylan, John E.; Breton, Michèle; Canca, David; Charlin, Laurent; Chen, Bo; Cicek, Cihan Tugrul (27 December 2023). "Operational Research: methods and applications". Journal of the Operational Research Society. 75 (3): 423–617. arXiv:2303.14217. doi:10.1080/01605682.2023.2253852. ISSN 0160-5682.
- ^ Shafer, G. (2018). Pascal's and Huygens's game-theoretic foundations for probability. [1]
- ^ M.S. Sodhi, "What about the 'O' in O.R.?" OR/MS Today, December, 2007, p. 12, http://www.lionhrtpub.com/orms/orms-12-07/frqed.html Archived 14 July 2009 at the Wayback Machine
- ^ P. W. Bridgman, The Logic of Modern Physics, The MacMillan Company, New York, 1927.
- ^ a b Zuckerman, Solly (1964). "In the Beginning -- And Later". OR. 15 (4): 287–292. doi:10.2307/3007115. ISSN 1473-2858. JSTOR 3007115.
- ^ "operations research (industrial engineering) :: History – Britannica Online Encyclopedia". Britannica.com. Retrieved 13 November 2011.
- ^ "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."
- ^ "Archived copy" (PDF). Archived from the original (PDF) on 12 August 2011. Retrieved 7 October 2009.
{{cite web}}: CS1 maint: archived copy as title (link) - ^ Kirby, p. 117 Archived 27 August 2013 at the Wayback Machine
- ^ Kirby, pp. 91–94 Archived 27 August 2013 at the Wayback Machine
- ^ Kirby, p. 96,109 Archived 2 October 2013 at the Wayback Machine
- ^ Kirby, p. 96 Archived 27 March 2014 at the Wayback Machine
- ^ Freeman Dyson, MIT Technology Review (1 November 2006) "A Failure of Intelligence: Part I"
- ^ ""Numbers are Essential": Victory in the North Atlantic Reconsidered, March–May 1943". Familyheritage.ca. 24 May 1943. Retrieved 13 November 2011.
- ^ Kirby, p. 101
- ^ (Kirby, pp. 102,103)
- ^ James F. Dunnigan (1999). Dirty Little Secrets of the Twentieth Century. Harper Paperbacks. pp. 215–217.
- ^ "Examine your assumptions – LessWrong". 30 March 2012.
- ^ 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. JSTOR 2288257.
- ^ "RAF History – Bomber Command 60th Anniversary". Raf.mod.uk. Archived from the original on 5 November 2011. Retrieved 13 November 2011.
- ^ a b Milkman, Raymond H. (May 1968). "Operations Research in World War II". Proceedings. Vol. 94/5/783. United States Naval Institute.
- ^ a b "Section 1.2: A Historical Perspective". Principles and Applications of Operations Research.
- ^ 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
- ^ Vahrenkamp, Richard (2019). "Nominal Science without Data: The Cold War Content of Game Theory and Operations Research" (PDF). Real World Economics Review. 88: 19–50..
- ^ "Factory Physics for Managers", E. S. Pound, J. H. Bell, and M. L. Spearman, McGraw-Hill, 2014, p 47
- ^ "New Era of Project Delivery – Project as Production System", R. G. Shenoy and T. R. Zabelle, Journal of Project Production Management, Vol 1, pp Nov 2016, pp. 13–24 https://www.researchgate.net/publication/312602707_New_Era_of_Project_Delivery_-_Project_as_Production_System
- ^ What is Management Science? Archived 7 December 2008 at the Wayback Machine The University of Tennessee, 2006. Retrieved 5 June 2008.
- ^ Stafford Beer (1967) Management Science: The Business Use of Operations Research
- ^ a b What is Management Science? Archived 14 September 2008 at the Wayback Machine Lancaster University, 2008. Retrieved 5 June 2008.
- ^ Merigó, José M; Yang, Jian-Bo (2017). "A bibliometric analysis of operations research and management science". Omega - International Journal of Management Science. 73: 37–48. doi:10.1016/j.omega.2016.12.004. ISSN 0305-0483.
- ^ "Blog". Archived from the original on 29 September 2017. Retrieved 28 June 2017.
- ^ "IFORS". IFORS. Retrieved 13 November 2011.
- ^ Leszczynski, Mary (8 November 2011). "Informs". Informs. Retrieved 13 November 2011.
- ^ "The OR Society". Orsoc.org.uk. Archived from the original on 24 April 2006. Retrieved 13 November 2011.
- ^ "Société française de Recherche Opérationnelle et d'Aide à la Décision". ROADEF. Retrieved 13 November 2011.
- ^ airo.org. "AIRO". airo.org. Retrieved 31 March 2018.
- ^ cors.ca. "CORS". Cors.ca. Retrieved 13 November 2011.
- ^ "ASOR". ASOR. 1 January 1972. Retrieved 13 November 2011.
- ^ "ORSNZ". ORSNZ. Retrieved 13 November 2011.
- ^ "ORSP". ORSP. Retrieved 13 November 2011.
- ^ "ORSI". Orsi.in. Retrieved 13 November 2011.
- ^ "ORSSA". ORSSA. 23 September 2011. Retrieved 13 November 2011.
- ^ Richard Vahrenkamp (2023), "Mathematical Management – Operations Research in the United States and Western Europe, 1945 – 1990", Management Revue – Socio-Economic Studies, vol. 34, no. 1, pp. 69–91, doi:10.5771/0935-9915-2023-1-69, S2CID 258937881
- ^ "EURO (EURO)". Euro-online.org. Retrieved 13 November 2011.
- ^ "SISO". Sisostds.org. Retrieved 13 November 2011.
- ^ "I/Itsec". I/Itsec. Retrieved 13 November 2011.
- ^ "The Science of Better". The Science of Better. Retrieved 13 November 2011.
- ^ "Learn about OR". Learn about OR. Archived from the original on 15 November 2011. Retrieved 13 November 2011.
- ^ "INFORMS Journals". Informs.org. Archived from the original on 9 March 2010. Retrieved 13 November 2011.
- ^ "Decision Analysis". Informs.org. Retrieved 19 March 2015.
- ^ "Information Systems Research". Informs.org. Retrieved 19 March 2015.
- ^ "INFORMS Journal on Computing". Informs.org. Retrieved 19 March 2015.
- ^ "INFORMS Transactions on Education". Informs.org. Retrieved 19 March 2015.
- ^ "Interfaces". Informs.org. Retrieved 19 March 2015.
- ^ "Organization Science". Informs.org. Retrieved 19 March 2015.
- ^ "Service Science". Informs.org. Retrieved 19 March 2015.
- ^ The Society for Modeling & Simulation International. "JDMS". Scs.org. Archived from the original on 23 August 2009. Retrieved 13 November 2011.
- ^ The OR Society Archived 24 April 2006 at the Library of Congress Web Archives;
- ^ "TOP". Springer.com. Retrieved 13 November 2011.
Further reading
[edit]Classic books and articles
[edit]- 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
[edit]- 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.
- Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray: Algorithms for Decision Making, MIT Press, 2022
History
[edit]- 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. ISBN 1-86094-366-7, ISBN 978-1-86094-366-9
- 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.
External links
[edit]Operations research
View on GrokipediaIntroduction
Definition and Scope
Operations research (OR) is an interdisciplinary field that applies advanced analytical methods, including mathematical modeling, statistics, and algorithms, to enhance decision-making and optimize the performance of complex systems.[2] It draws from disciplines such as mathematics, engineering, economics, and computer science to address managerial and operational challenges in both public and private sectors.[9] At its core, OR employs a scientific approach to transform data into actionable insights, focusing on quantitative analysis to improve efficiency and effectiveness.[6] The core principles of OR revolve around a structured problem-solving process that emphasizes rigor and validation. This process typically includes problem formulation to clearly define objectives and constraints, model building to represent the system mathematically or statistically, solution derivation using analytical or computational techniques, and validation through testing and sensitivity analysis to ensure robustness.[9] Data collection is integral throughout, providing the empirical foundation for accurate modeling and informed adjustments.[10] This systematic methodology distinguishes OR as a prescriptive discipline, aiming not only to diagnose issues but also to recommend optimal courses of action.[2] The scope of OR encompasses both deterministic problems, where outcomes are predictable under fixed conditions, and stochastic problems, which account for uncertainty and variability. It addresses key areas such as resource allocation to maximize efficiency with limited inputs, scheduling to coordinate activities and minimize delays, inventory control to balance supply and demand, and system design to enhance overall performance.[11] These applications span short-term operational decisions and long-term strategic planning, always prioritizing quantifiable improvements in system outcomes.[6] OR differs from related fields like analytics and optimization in its holistic integration of methods. While analytics broadly encompasses descriptive (what happened) and predictive (what might happen) analyses, OR emphasizes prescriptive analytics to determine the best actions, often through optimization as one of its primary tools but extending to simulation and other techniques for broader system analysis.[2] Optimization, though central to OR, refers specifically to techniques for finding maxima or minima in models, whereas OR represents a comprehensive discipline that incorporates optimization within a full scientific decision-making framework.[6]Importance and Impact
Operations research (OR) has delivered substantial economic benefits across industries by optimizing supply chains and resource allocation, resulting in billions of dollars in savings globally. For instance, finalist projects for the INFORMS Franz Edelman Award, which recognize high-impact OR applications, have collectively generated over $431 billion in quantifiable benefits since the award's inception in 1972 (as of 2025), including efficiencies in logistics and manufacturing that reduce operational costs by streamlining inventory and transportation. In 2025, USA Cycling was awarded for using OR in athlete selection and training strategies that contributed to Olympic gold medals.[12] In the energy sector, OR models implemented by the Midcontinent Independent System Operator (MISO) achieved savings of $2.1 to $3.0 billion from 2007 to 2010 through improved reliability and transmission planning.[13] These optimizations extend to global challenges, where OR supports climate modeling by enhancing renewable energy integration and resource forecasting, and aids pandemic response through supply chain resilience and resource allocation, as demonstrated in over 23 studies during COVID-19 that improved vaccine distribution and healthcare logistics.[14][15] On the societal front, OR enhances public services by reducing waste and improving efficiency in areas like transportation and healthcare delivery. Applications in urban planning have minimized congestion and emissions, contributing to more equitable access to services, while in humanitarian aid, organizations like the World Food Programme have used OR to distribute 4.2 million metric tons of food and $2.1 billion in cash transfers to 97.1 million beneficiaries in 2019, amplifying impact on food security.[16] Furthermore, OR advances sustainable development goals (SDGs) by integrating environmental, social, and economic dimensions into decision-making, such as optimizing supply chains for circular economies and reducing carbon footprints in manufacturing, thereby supporting global efforts to achieve SDGs like responsible consumption and climate action.[17] The impact of OR has evolved from providing tactical military advantages during World War II, such as radar optimization and convoy routing, to becoming a strategic tool in business and policy, where it drives measurable returns on investment (ROI). Post-war, OR transitioned to civilian applications, with industries adopting techniques for inventory control and production scheduling.[18] In the U.S. Army, OR has generated savings in the hundreds of millions to billions through supply chain forecasting and medical logistics, evolving into big data-driven analytics for broader economic and national security benefits.[19] Despite these gains, challenges in OR adoption persist, including data quality issues that undermine model accuracy, such as inconsistent or incomplete datasets in real-world settings, and resistance to quantitative methods due to organizational inertia and lack of technical expertise.[20] These barriers can limit scalability, particularly in sectors reliant on qualitative factors, though addressing them through better data governance and training enhances overall effectiveness.[8]History
Origins and Early Developments
The origins of operations research can be traced to philosophical and practical efforts in efficiency and management during the late 19th and early 20th centuries, particularly through the lens of scientific management pioneered by Frederick Winslow Taylor. Taylor, an American mechanical engineer, developed his principles in the 1880s and 1890s while working in U.S. manufacturing industries, emphasizing the systematic study of tasks to eliminate inefficiency. His time-motion studies involved breaking down work processes into elemental components, measuring them precisely, and reorganizing them to maximize productivity, as detailed in his 1911 book The Principles of Scientific Management. This approach influenced industrial practices by promoting data-driven decision-making over rule-of-thumb methods, laying a foundational philosophy for analyzing complex systems that later informed operations research. Early mathematical foundations for operations research emerged from economic theory and engineering innovations in the same period. French economist Léon Walras contributed significantly in the 1870s with his general equilibrium theory, which modeled markets as interconnected systems where supply and demand balance across multiple commodities through a set of simultaneous equations. This work, outlined in Éléments d'économie politique pure (1874), introduced mathematical optimization concepts for achieving equilibrium, serving as a precursor to the modeling techniques used in operations research for resource allocation. Complementing this, American industrialist Henry Ford applied efficiency principles in the early 1900s, most notably with the introduction of the moving assembly line at his Highland Park plant in 1913, which reduced Model T production time from over 12 hours to about 90 minutes by standardizing tasks and minimizing worker movement. Ford's methods, inspired by Taylorism, demonstrated practical optimization in mass production, influencing the quantitative analysis of workflows.[21] Key figure A.P. Rowe, an aeronautical engineer and superintendent of the Bawdsey Research Station from 1936, played a pivotal role in institutionalizing these ideas by forming interdisciplinary teams to evaluate system performance quantitatively. Rowe coined the term "operational research" in 1940 to describe these interdisciplinary efforts integrating science into operational decision-making.[22][3] By the late 1930s, these efforts focused on military contexts amid rising geopolitical tensions, particularly in addressing radar deployment and convoy protection challenges. At Bawdsey, Rowe initiated the first operational research studies in 1937, assigning scientists like E.C. Williams and G.A. Roberts to assess radar's effectiveness in air defense, optimizing detection ranges and response times through empirical data collection and modeling. Similar analyses began exploring convoy routing to mitigate submarine threats in the Atlantic, using probabilistic methods to evaluate escort allocations and formation sizes based on simulated scenarios. These efforts formalized operations research as a discipline for integrating science into operational decision-making.[3]World War II Contributions
During World War II, operations research emerged as a critical discipline through the efforts of interdisciplinary teams applying scientific methods to military challenges, particularly in the Battle of the Atlantic. In March 1941, British physicist Patrick Blackett formed a small group of scientists at RAF Coastal Command, informally known as "Blackett's Circus," to analyze anti-submarine warfare and optimize resource use against German U-boats.[23] This team, consisting of physicists, physiologists, and engineers, pioneered systematic data collection and analysis to evaluate operational effectiveness, setting a model for future OR units across the Allies.[24] The group addressed key problems such as optimizing convoy routes and sizes, radar deployment for aircraft patrols, and bombing strategies for anti-submarine attacks. Analysis revealed that the rate of merchant ship sinkings per U-boat attack was independent of convoy size, leading to recommendations for larger convoys that reduced the number of required escorts and overall shipping losses; by 1943, this shift contributed to greatly decreased losses in the Atlantic convoys.[25] For radar and search operations, Blackett's team optimized patrol patterns and recommended painting aircraft white to reduce visibility, which cut the average detection distance by 20% and doubled U-boat sightings during patrols.[26] In bombing tactics, their studies showed that firing on approaching aircraft during level bombing runs reduced the probability of ships being sunk from 25% to 10%, influencing defensive procedures.[27] Methodologically, Blackett's Circus introduced rigorous data-driven modeling and empirical testing, emphasizing probabilistic analysis over intuition to quantify uncertainties in search and engagement scenarios; this approach transformed ad hoc decision-making into evidence-based strategies.[23] Blackett's leadership in these efforts earned him the U.S. Medal for Merit in 1946 for contributions to the U-boat campaign, complementing his 1948 Nobel Prize in Physics for unrelated cloud chamber work.[24] The success of British OR prompted its adoption by the United States, where the Anti-Submarine Warfare Operations Research Group (ASWORG) was established in April 1942 under Philip Morse to support the U.S. Navy's Tenth Fleet.[28] ASWORG collaborated with British teams on logistics and search theory, developing optimal escort screening plans and convoy configurations that further minimized vulnerabilities to U-boat attacks.[28] OR practices spread to the U.S. Army, where dedicated groups analyzed supply chain efficiencies and troop movements, enhancing overall Allied logistical capabilities.Post-War Expansion
Following World War II, operations research transitioned from military applications to civilian sectors as demobilized personnel and institutions adapted wartime methodologies to industrial and business problems in the late 1940s and 1950s. Practitioners who had honed analytical techniques during the war returned to academia, government, and private industry, applying quantitative methods to optimize production, logistics, and resource allocation in growing economies. The RAND Corporation, established as an independent nonprofit in 1948 from the earlier Project RAND, played a pivotal role in this transfer, initially focusing on defense-related research while facilitating the dissemination of operations research tools to commercial contexts, such as supply chain management and economic planning.[29][30] Key institutional milestones marked the formalization of operations research during this period. In 1952, the Operations Research Society of America (ORSA) was founded to promote the discipline among professionals in the United States, providing a platform for collaboration between military, academic, and industrial experts. That same year, the first dedicated journal, Operations Research (initially the Journal of the Operations Research Society of America), began publication, enabling the sharing of seminal advancements and case studies. These developments solidified operations research as a distinct field, bridging wartime innovations with peacetime applications.[31][32] The discipline expanded globally in the post-war era, with societies forming across Europe and Asia to adapt operations research to local industrial needs. In the United Kingdom, the Operational Research Society was established in 1948, building on wartime efforts to support nationalized industries like transportation and energy. In Japan, the Operations Research Society of Japan was founded in 1957, aiding post-war reconstruction through applications in manufacturing and efficiency improvements amid rapid economic recovery. The 1950s also saw the widespread adoption of linear programming, pioneered by George Dantzig in the late 1940s, which became a cornerstone for solving complex optimization problems in industry worldwide, influencing sectors from oil refining to agriculture.[22][33][34] Cold War tensions sustained military funding for operations research, particularly through institutions like RAND, which drove innovations in game theory and systems analysis during the 1950s and 1960s. Substantial U.S. defense investments supported theoretical work on strategic decision-making, such as John von Neumann's contributions to game theory for conflict modeling, while systems analysis integrated interdisciplinary approaches to evaluate policy options in nuclear deterrence and resource allocation. This era's advancements not only bolstered national security but also enriched civilian methodologies by refining tools for uncertainty and multi-objective optimization.[35][36]Methodologies and Techniques
In quantitative analysis, particularly in operations research and management science, problem-solving approaches include trial and error (informal guessing and testing), complete enumeration (brute-force checking of all possibilities), using an algorithm (systematic, guaranteed solution procedure), and trying various approaches (heuristic methods exploring multiple strategies for good-enough solutions in complex problems).Optimization Methods
Optimization methods constitute a cornerstone of operations research, focusing on deterministic techniques to identify optimal solutions for decision-making problems involving resource allocation, production planning, and scheduling. These methods assume known parameters and seek exact solutions within defined constraints, contrasting with probabilistic approaches. Central to this domain are formulations that model objectives and restrictions mathematically, enabling systematic solution procedures. Key techniques include linear, integer, nonlinear, dynamic, and multi-objective programming, each addressing specific problem structures prevalent in operational contexts. Linear programming (LP) models problems where both the objective function and constraints are linear, providing a framework for optimizing outcomes such as profit maximization or cost minimization subject to limited resources. The standard form of an LP problem is formulated as: where represents the decision variables, the objective coefficients, the constraint matrix, and the resource bounds.[37] This formulation assumes non-negativity for simplicity, though extensions handle equalities and free variables via slack or surplus variables. LP problems are geometrically interpreted as finding the optimal vertex of a convex polyhedron defined by the constraints, ensuring a unique optimum exists under feasibility and boundedness.[37] The simplex algorithm, devised by George Dantzig, efficiently solves LP problems by moving along the edges of the feasible region from one basic feasible solution to an adjacent, improved one. The process begins by initializing a basic feasible solution, often using artificial variables for Phase I to find feasibility. In the main Phase II, an entering variable is selected as the non-basic variable with the most negative reduced cost (indicating potential improvement), while the leaving variable is chosen via the minimum ratio test on the updated constraints to maintain feasibility. Pivoting updates the basis by swapping variables, recomputing the tableau until all reduced costs are non-negative, signaling optimality. To prevent cycling—revisiting bases indefinitely—pivot rules like Bland's rule select the lowest-index eligible variable for entering or leaving.[38][39] The algorithm's polynomial-time variants, such as those using interior-point methods, complement the simplex for large-scale problems, but the tableau-based approach remains foundational for its intuitive edge-following.[39] Integer programming extends LP by requiring some or all variables to take integer values, addressing discrete decisions like selecting whole units in inventory or facility location. The branch-and-bound method, pioneered by Land and Doig, solves these by relaxing integrality to obtain LP bounds and systematically partitioning the solution space. It constructs a tree where each node represents a subproblem with added constraints (e.g., fixing a fractional variable to floor or ceiling values); upper and lower bounds from LP relaxations and incumbent integer solutions prune infeasible or suboptimal branches, ensuring enumeration terminates at the global optimum.[40] For pure integer linear programs, strong formulations like Gomory cuts enhance bounding. A classic example is the 0-1 knapsack problem, which maximizes value subject to and , modeling cargo loading or project selection; branch-and-bound efficiently explores subsets while bounding via LP relaxation discards unpromising paths.[40] Nonlinear programming (NLP) handles cases where the objective or constraints involve nonlinear functions, common in modeling economies of scale or engineering designs. Gradient descent serves as a foundational first-order method for unconstrained or constrained NLPs, iteratively updating the solution as , where is a step size chosen via exact or approximate line search to ensure descent, and is the gradient. For constrained problems, projected gradient or barrier methods adapt this by incorporating feasibility. Convergence relies on convexity for global optima, though local minima suffice for many applications; second-order methods like Newton's accelerate but require Hessian computation.[41] In operations research, NLPs optimize nonlinear costs in supply chains or routing, often solved via sequential quadratic programming that linearizes constraints around iterates.[41] Dynamic programming (DP) addresses sequential decision problems by decomposing them into overlapping subproblems, leveraging recursion for efficiency. Bellman's principle of optimality states that an optimal policy has the property that, regardless of the initial state and decision, the remaining decisions form an optimal subpolicy for the resulting state. This enables backward or forward recursion, typically via the value function representing the maximum value from stage in state . The recursive formulation for finite-horizon problems is: where is the immediate reward for action in state , the next state transition, and a discount factor (or for undiscounted). Initialization sets , and policies derive from argmax choices.[42] DP excels in staging problems like inventory control or resource allocation over time, avoiding exponential enumeration through memoization. In sequencing applications, such as job shop scheduling, DP minimizes total completion time by optimally ordering jobs on machines, computing costs stage-by-stage from the final machine backward.[42] Multi-objective optimization arises when multiple, often conflicting, criteria must be balanced, such as cost versus environmental impact in supply chain design. Solutions lie on the Pareto front, the set of non-dominated points where no objective improves without worsening another; Vilfredo Pareto introduced this concept in economic efficiency analysis. Generating the front involves solving scalarized problems, like the weighted sum method, which combines objectives as with , , varying weights to trace efficient solutions. This linear scalarization suffices for convex problems but requires nonlinear variants like -constraint for non-convexity, ensuring comprehensive coverage of trade-offs.[43] In operations research, these methods support decision analysis in logistics, prioritizing solutions via post-optimization tools like trade-off curves.[43]Stochastic and Simulation Techniques
Stochastic and simulation techniques form a cornerstone of operations research for modeling systems under uncertainty, where randomness in inputs, processes, or outcomes precludes deterministic analysis. These approaches employ probability theory to quantify risks, predict behaviors, and support decision-making in dynamic environments like supply chains, telecommunications, and service operations. By incorporating stochastic elements such as random arrivals or variable processing times, they enable the evaluation of long-term performance metrics, including expected costs, delays, and resource utilization, often through analytical models or computational approximations. Queueing theory provides analytical frameworks for systems where entities arrive randomly and await service, capturing congestion and efficiency under probabilistic demands. A standardized description uses Kendall's notation A/B/s, where A specifies the interarrival time distribution, B the service time distribution, and s the number of parallel servers; this notation, proposed by D.G. Kendall in 1953, facilitates concise model specification and comparison across diverse applications.[44] The M/M/1 queue exemplifies a basic yet influential model, assuming Poisson-distributed arrivals at rate λ and exponentially distributed service times at rate μ with one server. For stability, the traffic intensity ρ = λ/μ must be less than 1, yielding the steady-state probability of n customers asfrom which average queue length and waiting time follow via the birth-death process. This formulation originated in A.K. Erlang's 1909 analysis of telephone traffic congestion, laying groundwork for modern teletraffic engineering.[45] Little's law complements these models by relating system-wide averages: the long-run average number of items L equals the arrival rate λ times the average time per item W, or L = λW, holding for stable systems with general arrival and service processes under mild conditions. Proven rigorously by John D.C. Little in 1961, this theorem underpins performance evaluation across queueing networks without requiring detailed distributional assumptions.[46] Markov decision processes (MDPs) extend stochastic modeling to optimal control problems, framing decisions in environments with probabilistic state transitions and rewards. An MDP is defined by a state space S, action space A, transition probabilities P(s'|s, a) governing the probability of moving to state s' from s under action a, and reward function R(s, a); the Markov property ensures future states depend only on the current state and action. Introduced by Richard Bellman in 1957, MDPs formalize dynamic programming for uncertain sequential choices, such as inventory management or routing under variable demands.[47] The value iteration algorithm solves discounted infinite-horizon MDPs by iteratively computing the value function V(s), which represents the maximum expected discounted reward starting from state s. The update rule is
where γ ∈ (0,1) is the discount factor; under contraction mapping properties, it converges to the optimal V^*(s) = max_π E[∑_{t=0}^∞ γ^t r_t | s_0 = s, π], enabling policy extraction via argmax actions. This method, inherent to Bellman's dynamic programming framework, balances exploration of state-action spaces efficiently for moderate-sized problems.[47] Monte Carlo simulation approximates solutions to intractable stochastic systems by generating numerous random realizations of the process and averaging outcomes, offering flexibility for complex, non-Markovian scenarios. Pioneered by Nicholas Metropolis and Stanislaw Ulam in 1949, the technique leverages random sampling to estimate integrals or expectations, such as system throughput, by simulating paths from probabilistic models and computing empirical means; its efficacy stems from the law of large numbers, ensuring convergence to true values as sample size grows. In operations research, it assesses designs like production lines or networks where analytical tractability fails, often integrated with optimization for parameter tuning.[48] To enhance efficiency, variance reduction methods like antithetic variates mitigate the high computational cost of naive sampling by introducing controlled correlations. Developed by J.M. Hammersley and K.W. Morton in 1956, the approach generates pairs of simulations from oppositely transformed random variables—e.g., uniform draws U and 1-U—to induce negative dependence, yielding an unbiased estimator with lower variance: if θ̂_1 and θ̂_2 are paired estimates, then (θ̂_1 + θ̂_2)/2 has Var[(θ̂_1 + θ̂_2)/2] = [Var(θ̂_1) + Var(θ̂_2) - 2Cov(θ̂_1, θ̂_2)] / 4, reduced when Cov < 0. This technique proves particularly effective for smooth functions in financial risk assessment or queueing simulations. Reliability analysis quantifies the dependability of systems against failures, focusing on survival probabilities and operational uptime amid stochastic breakdowns. The exponential distribution models constant failure rates λ, with reliability function R(t) = e^{-λt} implying memoryless property—conditional failure risk remains λ regardless of age—suitable for non-aging components like certain electronics or software faults. Seminal treatments in Richard E. Barlow and Frank Proschan's 1965 monograph establish probabilistic foundations for coherent systems, where component failures propagate via structure functions. For repairable systems with exponential failure and repair times (rates λ and μ, respectively), steady-state availability—the long-run proportion of time the system operates—is given by
where MTTF = 1/λ and MTTR = 1/μ; this formula, derived from alternating renewal theory, guides redundancy and maintenance policies in critical infrastructure like power grids. Queueing models from these techniques further inform healthcare applications, such as predicting patient wait times in emergency departments to improve resource allocation.
