Lesson 1. Introduction SA305 Spring 2015 1 What is operations research? 2 What is operations research? “The most influential academic discipline field you’ve never heard of” Boston Globe, 2004 Operations Research (OR) is the discipline of applying advanced mathematical methods to help make better decisions “The Science of Better” INFORMS slogan “A liberal education in a technological world” Thomas Magnanti, former Dean of Engineering at MIT 2 What is operations research? Numerous applications, e.g. logistics manufacturing workforce scheduling finance marketing 3 OR and the military The military uses OR to improve decision making in a variety of ways, e.g. force composition weapon selection search and detection flight operations scheduling training and personnel assignment Assessment Division (OPNAV N81) at the Pentagon The Naval Postgraduate School has one of the oldest and highest ranking OR departments in the US Naval Research Logistics is a prominent academic journal featuring research in OR 4 The traveling nationalatlas.gov salesperson problem C A N A D A H MON TAN A La k e S upe NORTH DAKOTA Bismarck ON M MINNESOTA IA Springfield Topeka KANSAS 100 mi Santa Fe NA ARCTIC RU OC EA SS OCE Oklahoma City PE NN M C A N A D A G SE A GULF 0 OCE AN U.S. Department of the Interior U.S. Geological Survey GEO RGI A MO NT HAM PSH IRE CAR JERS NEC TICU EY MAR T OLI NA Columbia SOUTH A CAR OLIN Montgomery Jackson Tallahassee Baton Rouge IA N RIN NEW er E is Dov DELAWAR Annapol ton D Washing YLAN IA VIR GIN mond Rich NO RTH Atlanta LOUISIANA Austin Trenton Harr DC Raleigh TENNE SSEE ALAB AMA CON NIA SYLVAisburg WE STIA VIR GIN ARKANSAS Little Rock AN ALASKA IC ie Charleston Frankfort Jefferson City MISSISSIPPI BE CIF Columbus Indianapolis MISSOURI OKLAHOMA NEW MEXI CO Phoenix 100 km PA Er OHIO INDIA NA Nashville AR IZO N E A O C 0 0 ke ke NEW d ETT S Concor SAC HUS r MAS O nta ton YO RKAlbany Bos Providence NE W ND d DE ISLA Hartfor RHO io KENT UCKY Honolulu IC La k e Michigan ILLINOIS TEXAS IF La Des Moines Lincoln COLOR ADO HAWAII PA C La Lansing IOWA NEBRASKA Denver N FO RN Cheyenne City UTA H A C I F I P A C CA LI Salt Lake DA VER ron Madison NE VA Carson City Sacram ento IG WISCONS IN NE Augus ier Montpel La Hu St Paul Pierre G M AI ta I H SOUTH DAKOTA WYO MIN KA IC O CEAN ke IDA HO AS A PA I I CIF C Boise AW r i or O C E A N Helena OR EG AL GTON A T L A N T I C Olymp WA SH ia IN Salem AT O LAN CE TIC AN STATES AND CAPITALS TM Where We Are 0 E X I C GU O 0 OF ALA SK A 200 mi 200 km XIC F ME LF O 0 100 100 200 200 O FLO RIDA T 300 mi 300 km H E B A H A M AS Juneau CUBA The National Atlas of the United States of AmericaO R states_capitals2.pdf INTERIOR-GEOLOGICAL SURVEY, RESTON, VIRGINIA-2003 A salesperson located in Annapolis wants to visit clients in each of the 48 state capitals of the continental US and Washington DC What is shortest way of visiting all the capitals and then returning to Annapolis? 5 The traveling salesperson problem Entire books have been written on the TSP 6 The traveling salesperson problem 1962: contest by Proctor and Gamble - best TSP tour through 33 US cities http://www.tsp.gatech.edu 7 The traveling salesperson problem 1998: The Florida Sun-Sentinel’s Science page ponders Santa Claus’s traveling problem http://www.tsp.gatech.edu 8 The traveling salesperson problem One of the most popular problems in operations research Numerous applications in expected and unexpected places Circuit board manufacturing Genome sequencing 9 The traveling salesperson problem Your turn! Try to find the shortest way of visiting all the capitals and then returning to Annapolis 10 The traveling salesperson problem STATES AND CAPITALS TM C A N A D A H M ON TAN Salem Helena A Bismarck M M INNESOTA Madison NE VA C I F I P A C Carso n City Sacra mento FO R DA Cheyenne City UTAH ILLINO IS Springfield COLO RADO Topeka KANSAS 100 mi Santa Fe Oklahoma City NEW M EXIC O ARC T I C RU OC EA SS OCE WE STIA VIRGIN YO RKAlbany rd Hartfo IA SY LVANurg Harrisb DC ARKANSAS Atlanta NE W M A N A D A SE A GU L F 0 OCE AN U.S. Departmentof the Interior U.S. Geological Survey GE ORGIA PSH IRE AN E ISL ECTICU EY M ARY D T A Columbia SO UTH A CA RO LIN J ackson Tallahassee Baton Rouge IA C G A J ERS ver lis Do DEL AW ARE Annapo on Washingt LAN D CA RO LIN HA M Montgomery LOUISIANA Austin N RI N ALA BAM RHOD CO NN n Trento IA VI RGIN mond Rich NO RTH TENN ESSE E Little Rock AN ALASKA IC PE NN ON T NE W SETTS Concord SAC HU M AS n Bosto ence Provid io NE W ie n Charlesto Frankfort M ISSISSIPPI BE CI F Columbus Indianapolis J efferson City OKLAHOM A NA Phoenix 100 km PA Er ta r Raleigh N E A O C 0 0 ke ke O n OH IO INDI ANA Nashville Honolulu IC La M ISSOURI TEXA S IF La KEN TUC KY AR IZO HAWAII PAC VE RM Lansing Des Moines Lincoln E sta Augu elier Montp IOWA NEBRASK A Denver N IA N W YOM ING Salt Lake A WISCON SIN IG St Paul M A IN IC O C E AN La H SOUTH DAKOTA KA ron Hu ke IDAH O Pierre C A LI I AS A P AI I CI F C Boise AW Lake S uper i or NORTH DAKOTA ON Lake Michigan O R EG AL TO N O C E A N Olym W AS HI pia NG A T L A N T I C nationalatlas.gov Where We Are AT O L AN CE TI C AN The solution: 0 E X IC GU O XI C F ME LF O 0 0 100 100 200 200 O A FLO RID T 300 mi 300 km H E B A H A M AS OF AL A J uneau SK A 200 mi 200 km CUBA The National Atlas of the United States of AmericaO R states_capitals2.pdf INTERIOR-GEOLOGICAL SURVEY, RESTON, VIRGINIA-2003 11 The traveling salesperson problem What about 13,509 cities in the US? 12 The traveling salesperson problem What about 13,509 cities in the US? Sophisticated mathematical techniques are our best bet 12 The OR approach Interpreting Problem statement and data Solution INGTO AL N H M ONTANA Bismarck M M INNESOTA ON C I F I P A C FORN IA Springfield Topeka KANSAS IF IC 100 mi OC EA SS OCE Oklahoma City ARKANSAS Little Rock Austin Atlanta Boston YORKAlbany M C A N A D G A RI N A J ERSEY NEW Dover DELAWARE Annapolis on Washingt VIRGIN M ARYLAND IA Richmond CAROLI HAM RHODE CONNECT Trenton ONT PSHIRE SETTS M ASSACHU ce Providen Hartford DC ISLAND ICUT NA Columbia SOUTH CAROLINA GEORGIA Tallahassee Baton Rouge IA N SE A GU L F 0 OCE ALABAM NEW Concord io NEW Montgomery J ackson LOUISIANA AN ALASKA IC ta r A YLVANI PENNS Harrisburg NORTH TENNESSEE OKLAHOM A NEW M EXICO M ISSISSIPPI BE CI F ke O n ie WEST VIRGINIA Raleigh Santa Fe A Phoenix RU 100 km PA Er Charleston Frankfort J efferson City Nashville N E A O C 0 Columbus Indianapolis M ISSOURI TEXAS 0 INDIANA KENTUCKY ARIZON ARC T I C HAWAII Honolulu PAC M AINE VERM ke OHIO ILLINOIS a August Montpeli La Des Moines Lincoln COLORADO KA La Lansing IOWA NEBRASKA Denver Lake Michigan Madison Cheyenne City UTAH N W YOM ING Salt Lake NEVAD A Carson City Sacrame nto CALI A WISCONSIN IG St Paul Pierre AS er H SOUTH DAKOTA IC O C E AN La ron Hu ke IDAHO A P AI I CI F I C Boise AW Lake S uper i or NORTH DAKOTA Helena OREG AT O L AN CE TI C AN STATES AND CAPITALS C A N A D A Olympia WASH Salem O C E A N What is the shortest way of visiting all the capitals and then returning to Annapolis? TM A T L A N T I C nationalatlas.gov Where We Are AN U.S. Departmentof the Interior U.S. Geological Survey 0 OF AL A J uneau SK A 200 mi 200 km E X IC GU O XI C F ME LF O 0 0 100 100 200 200 O FLORIDA T 300 mi 300 km H E B A H A M AS CUBA The National Atlas of the United States of AmericaO R states_capitals2.pdf INTERIOR-GEOLOGICAL SURVEY, RESTON, VIRGINIA-2003 Modeling Optimization (this course), Stochastic processes P min {i,j}∈E c{i,j} x{i,j} P s.t. {i,k}∈E x{i,k} = 2 ∀i ∈ N P {i,j}∈δ(S) x{i,j} ≥ 2 ∀S ⊆ N x{i,j} ∈ {0, 1} Solving ∀{i, j} ∈ E Mathematical model 13 Goals for this course Modeling Recognize opportunities for mathematical optimization Formulate optimization models – linear programs – that capture the essence of the problem Illustrate applications of real-world problems Solving Algorithms to solve these mathematical models Detailed topic list and schedule is on the syllabus 14 Optimization is everywhere “Minimize” time it takes to get from class to class “Maximize” the company’s profits (Moneyball) “Best” lineup for the Oakland A’s We are always trying to make decisions in a way that meets some objective subject to some constraints Some success stories of optimization helping solve complex real-world decision-making problems ... 15 Package delivery UPS has an air network consisting of 7 hubs, nearly 100 additional airports in the US, 160 aircraft of nine different types Decision: Objective: Constraints: UPS credits optimization-based planning tools with identifying operational changes that have saved over $87 million to date, reduced planning times, peak and non-peak costs, fleet requirements 16 Sports scheduling ACC Basketball earns over $30 million in revenue annually, almost all from TV and radio TV networks need a steady stream of “high quality” games, NCAA rules, school preferences and traditions Decision: Objective: Constraints: Optimization approaches yields reasonable schedules very quickly 17 Radiation therapy High doses of radiation can kill cancer cells and/or prevent them from growing and dividing Can also kill healthy cells! Radiation can be delivered at different angles and intensities Decision: Objective: Constraints: Many successes reported using different types of optimization models 18 Next time... Introduction to optimization models Homework is on the syllabus! Read the course policy statement and be aware of the course website: http://www.usna.edu/www/dphillip/sa305.s13/ 19