Presentation on theme: "Introduction to Operations Research A. The Discipline’s Titles Operations Research Decision Science (s) Quantitative Methods Management Science."— Presentation transcript:
Introduction to Operations Research A
The Discipline’s Titles Operations Research Decision Science (s) Quantitative Methods Management Science
Nature of the Work The development and application of mathematical techniques and models, for the purpose of solving problems in a wide array of fields, and producing better quality decisions
Nature of the Work Identifies better methods to coordinate financial, material, equipment, and human resources toward achievement of an organization’s goals, drawing from mathematics, science, and engineering
Nature of the Work The use of techniques such as statistical inference and decision theory, mathematical programming, probabilistic models, network and computer science to solve complex operational and strategic issues.
Operations Researchers Solve problems in different ways Propose alternative solutions to management for consideration Responsible for monitoring the implementation of the selected solution, and working with others to ensure its success
Issues Analyzed High-level Strategy Short-Term Planning Intermediate-Term Planning Forecasting Resource Allocation Performance Measurement Scheduling Supply Chain Management Pricing Design of Facilities and Systems Transportation and Distribution Analysis of Large Data Bases Statistical Quality Control Project Management Waiting Lines Artificial Intelligence Risk Assessment
Organizational Structure Some firms centralize OR in one department Other firms position OR within each division Some firms contract for OR services with an independent consulting firm Analysts may also work closely with senior managers to identify and solve a variety of operational problems In many large corporations, OR reports directly to the CEO
The O.R. Process Managers describe the symptoms of the problem to the operations analyst who then formally defines the problem The analyst breaks the problem down into its major components, and gathers information about each of them from many sources, both internal and external The analyst then selects the most appropriate analytical model or technique
Techniques Available ……….AND MANY MORE! Simulation Linear Programming Queuing Models Dynamic Programming Nonlinear Programming Integer Programming Neural Networks Expert Systems Decision Analysis Markov Processes Econometric Models Game Theory Forecasting Inventory Control Assignment Algorithms Goal Programming Quality Control Models Network Models
The Mathematical Model The Mathematical Model Attempts to describe the system or problem being studied Enables the analyst to assign values to the various components and to clarify the relationships between them Values can be altered in order to examine what may happen to the system or solution under different circumstances
Presentation to Management Analyst presents management with first recommendations based on the model results Additional computer runs of the model may be needed to consider different assumptions The O.R. analyst presents the final recommendation Steps
Post Presentation Once management reaches a selection decision, the O.R. analyst will usually work with others in the organization to ensure the plan’s successful implementation
Employment O.R. analysts held 64,000 jobs in 2012
Employment Telecommunications Aerospace manufacturers Computer systems design firms Engineering Higher education Financial institutions Insurance carriers Airline industry Management service firms State and federal governments Major Employers
Required Credentials OPERATIONS RESEARCH At least a masters degree in OR, computer science, engineering, math, information systems, or business, coupled with a bachelors degree in any of the above, or economics or statistics Dual graduate degrees are especially attractive to employers to employers
Your First Career Position o Routine work under the supervision of experienced analysts o Eventual assignment of more complex tasks and greater autonomy to design models and solve problems o Analysts advance by assuming positions as technical specialists and supervisors
Long-Term Prospects OPERATIONS RESEARCH The skills acquired by O.R. analysts are useful for a variety of higher level management jobs Consequently, experienced analysts can leave the profession to assume non-technical managerial or administrative positions Analysts with significant experience may become professors and consultants
Job Outlook Good, because firms will strive to improve their productivity, effectiveness, and competitiveness, or perish! Good, because firms are sitting on hugh data bases that need to be analyzed by professionals who have the skills to do so. The number of professionals needed to support the growing demand, together with those needed to replace retirees, are expected to exceed the number of graduates for decades to come
Sample Job Titles & Compensation TitleMedian SalaryExperience Analyst – Level I$50, years Analyst – Level III$80, years Analyst – Level V$130, years Manager$143, years Operations Research - Spring 2009
Sample Job Titles & Compensation TitleMedian SalaryExperience Specialist – Level I$32, years Opns Unit Manager$46, years Opns Manager$81, years Opns Director$141, years Top Opns Executive$232, years Operations Management - Spring 2009
Operations Research Applications Operations research has been applied to a wide variety of situations, and has had a dramatic impact on the effectiveness of many organizations A small sampling of the many successful applications include the following….
linear programming Burger King uses linear programming to determine how different cuts of meat should be blended together to pro- duce hamburger patties at minimum cost while still meet- ing certain specifications such as fat content, texture, freshness, and shrinkage. As the cost of different cuts of meat changes, the firm reevaluates its model to determine whether its recipe should be modified.
American Airlines Scheduling aircraft crews is a complex problem involving such factors as the type of aircraft to be flown, the cities of origination and termination for the flight, the intermediary cities visited by the aircraft, and the length of the flight. Federal and union rules govern the placement of personnel on the aircraft. To address these issues, American Airlines has integer linear programming developed an integer linear programming model that allows the company to quickly determine an optimal flight schedule for its personnel.
Sony Advanced Traveler Information System The marriage of the microprocessor with the Global Positioning Satellite System has enabled Sony Cor- poration to develop an onboard navigation system capable of giving directions to a car’s driver. This information is especially valuable during traffic conditions such as rush hour congestion. The software is based on an operations research shortest path network model known as a shortest path network.
The Interstate 10 Freeway Following the California earthquake in January 1994, Interstate 10, a main freeway serving the Los Angeles area, needed to be rebuilt quickly. The project prime contractor was given a fairly short 5 month deadline to reopen the roadway. To encourage the work to be done as quickly as possible, the contractor was offered a bonus of $500, for each day by which it was able to beat the deadline. critical Using a project scheduling technique known as the critical path method path method, the contractor was able to schedule work crews so as to be able to complete the repair work a month earlier than the project deadline. As a result, the contractor collected a $15 million dollar bonus.
Cooking at Mrs. Fields Mrs. Fields operates a nationwide chain of cookie shops specializing in fresh-baked chocolate chip cookies. The chain has equipped each shop with a PC-based information system to aid personnel in deciding when additional cookies should be baked and the amounts that should be produced. This system relies on the operations research demand forecastinginventory techniques of demand forecasting and inventory modeling modeling.
Disneyland & Disneyworld Lines form as visitors await their turn to ride or view the most popular attractions. Disney incorporates waiting queuing models line or queuing models into its overall design plans for the park. These models mirror customer behavior and tolerance for waiting in line. As a result, Disney developed an entirely new “industry” of waiting line entertainment to maintain customer satisfaction levels and enhance the value and excitement of the ride or attraction.
New York City Trash NYC handles over 20,000 tons of garbage per day. To dispose of this trash, the city operates 3 incinerators. Refuse is also sent by barge from marine transfer sta- tions to the Fresh Kills Landfill. To determine future operational plans for this landfill, the Department of Sanitation undertook an operations research analysis. The result was development of the BOSS ( barge operation systems simulation ) model. simulation model This simulation model enabled the department to determine the number of additional barges that should be purchased to handle future demands. It also helped plan the dispatching of these barges.
Quality Management at Ford During the 1970s, U.S. automobile manufacturers saw a steady decline in their market share due to competition from Japanese and European manufacturers. In response, Ford Motor Co. embarked on a “Quality Is Job One” campaign. Suppliers were held to tighter standards, and new quality control procedures were developed. quality management As a result of these quality management activities, the firm was able to reverse its decline in market share and profitability.
Primary O.R. Modeling Focus E - commerce Health care services Supply chain management Global warming Telecommunications Banking Flexible manufacturing Neural networks International economics Transportation systems Environmental impact Robotics CURRENTLY
Operations Research Time Line 1890s Frederick Taylor applies the scientific approach to improving operations in production ( industrial engineering ) 1900s Henry Gantt develops control charts for minimizing machine job completion times ( project scheduling ) Andre Markov studies how systems change over time. 1910s Ford Harris develops approaches to determine the optimal inventory quantity to order ( inventory theory ) E.K. Erlang develops a formula for determining the average waiting time for telephone callers ( queuing theory )
Operations Research Time Line 1920s William Shewhart introduces the concept of control charts. Dodge and Romig develop the technique of acceptance sampling ( quality control ) 1930s John von Neuman and Oscar Morgenstern develop strategies for evaluating competitive situations ( game theory ) 1940s World War II provides the impetus for the application of Mathematical modeling for solving military problems. George Dantzig develops the simplex method for solving Problems with a linear objective and constraints ( linear programming )
Operations Research Time Line 1950s Harry Kuhn determines required conditions for optimality for problems with a nonlinear structure ( nonlinear programming ) Ralph Gomory develops a solution procedure for problems in which some variables are required to be integer valued ( integer programming ) PERT and CPM are developed ( project scheduling ) Richard Bellman develops a methodology for solving multistage decision problems ( dynamic programming )
Operations Research Time Line 1960s John Little proves a theoretical relationship between the average length of a waiting line and the average time a customer spends in line ( queuing theory ) Specialized simulation languages such as SIMSCRIPT and GPSS are developed ( simulation ) 1970s The microcomputer is developed 1980s N. Karmarkar develops a new procedure for solving large-scale linear programming problems ( LP ) The personal computer is developed Specialized OR software packages that can run on microcomputers are developed
Operations Research Time Line 1990s Spreadsheet packages begin to play a major role in modeling and solving management science models TIMS and ORSA merge to form the Institute for Operations Research and Management Science ( INFORMS )
Why AACSB Accreditors Strengthened O.R. Guidelines for Business Schools “The explosive growth of applications of information technology itself is being driven by management science-based technologies like decision analysis, scenario generation, simulation, and optimization.*” * OPERATIONS RESEARCH
Why AACSB Accreditors Strengthened O.R. Guidelines for Business Schools “Since management science techniques are driving operations, supply chains, and e-commerce, they constitute a major driver of the economy.”
Why AACSB Accreditors Strengthened O.R. Guidelines for Business Schools “To sustain a career in business for many years, a business graduate will need an understanding of the analytic foundations and tools that are important for managerial decision-making.”
Intellectual Capital “ Intellectual Capital ” In its MBA issue of 10/02/00, Business Week began applying a new standard to judge the top firms based on “ intellectual capital ”. Business Week then chose 12 publications, including the Harvard Business Review, the MIT Sloan Manage- ment Review, Operations Research, and Management Science, to aid in its preparation of its evaluations. The above clearly points outs the value of operations research in their interpretation of “intellectual capital”
Conclusion As the 21 st century begins, there is overwhelming evidence that major organizations are looking for individuals in all disciplines who have strong quantitative, computer, and communications skills. Operations research, with its emphasis on all these areas, as well as its direct application to problems of optimization and efficiency, is recognized as an important element in a well- rounded business education.