Introduction to Operations Research with basic concepts along with Models in Operation Research also addressed.
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The document provides an introduction to operations research. It discusses that operations research is a systematic approach to decision-making and problem-solving that uses techniques like statistics, mathematics, and modeling to arrive at optimal solutions. It also briefly outlines some primary tools used in operations research like statistics, game theory, and probability theory. The document then gives a short history of operations research, noting that it originated in the UK during World War II to analyze problems like radar systems. It concludes with discussing the scope and applications of operations research in fields like management, regulation, and economics.
Operations Research - Meaning, Origin & CharacteristicsSundar B N
Operations research (OR) is a scientific approach to problem solving that uses quantitative analysis. It originated during World War II when the British military used empirical data and basic statistics to develop tactics. OR is characterized by its use of decision making, information technology, quantitative solutions, simulation, optimization, and interdisciplinary team-based work. It aims to uncover new problems and provide the best performance under given circumstances through mathematical modeling.
Operations research (OR) began during World War II when scientists applied analytical methods to solve complex military problems. Since then, OR has expanded to help organizations with strategic decision-making. OR uses interdisciplinary teams and quantitative techniques like linear programming to build mathematical models of systems. These models help optimize resource allocation and identify optimal solutions. OR aims to improve systems through objective, data-driven analysis and continues providing value as new problems emerge over time.
This is a presentation from video on 'Introduction to Operations Research' available at the end of this presentations and directly at https://youtu.be/PSOW3_gX2OU
Topics like Organisations of Operations Research, History of Operations Research Role of Operations Research(OR), Scope of Operations Research(OR), Characteristics of Operations Research(OR), Attributes of Operations Research(OR).
This video also talks about Models of Operations Research
• Degree of abstraction
o Mathematical models
o Language models
o Concrete models
• Function
o Descriptive models
o Predictive models
o Normative models
• Time Horizon
o Static models
o Dynamic models
• Structure
o Iconic or physical models
o Analog or schematic models
o Symbolic or mathematical models
• Nature of environment
o Deterministic models
o Probabilistic models
• Extent of generality
o General model
o Specific models
Operational research is the scientific study of operations aimed at improving decision-making. It originated from military planning in World War II and has since expanded to various industries. In public health, operational research uses analytical methods to identify health program problems, potential solutions, and test solutions to inform evidence-based decisions around programs. It involves interdisciplinary teams that study issues like disease screening, outbreak response, and health behavior programs. Societies like IFORS and journals promote the field. Overall, operational research integrates data analysis into program management to enhance monitoring and evaluation.
This document provides an overview of operations research (OR). It discusses how OR emerged from developments in military operations during World War II and was later applied to industrial problems. OR takes a scientific approach to solving organizational problems by using interdisciplinary teams and systems analysis. It aims to determine optimal solutions and courses of action given limited resources. The document outlines the scope and methodology of OR, including how it can help managerial decision making. It also discusses different types of OR models and techniques.
The document provides an overview of operations research techniques. It discusses:
- Operations research aims to improve decision-making through methods like simulation, optimization, and data analysis.
- Major applications include production scheduling, inventory control, transportation planning, and more.
- The techniques were developed in World War II and are now used widely in business for problems like resource allocation, forecasting, and process improvement.
The document provides an introduction to operations research. It discusses that operations research is a systematic approach to decision-making and problem-solving that uses techniques like statistics, mathematics, and modeling to arrive at optimal solutions. It also briefly outlines some primary tools used in operations research like statistics, game theory, and probability theory. The document then gives a short history of operations research, noting that it originated in the UK during World War II to analyze problems like radar systems. It concludes with discussing the scope and applications of operations research in fields like management, regulation, and economics.
Operations Research - Meaning, Origin & CharacteristicsSundar B N
Operations research (OR) is a scientific approach to problem solving that uses quantitative analysis. It originated during World War II when the British military used empirical data and basic statistics to develop tactics. OR is characterized by its use of decision making, information technology, quantitative solutions, simulation, optimization, and interdisciplinary team-based work. It aims to uncover new problems and provide the best performance under given circumstances through mathematical modeling.
Operations research (OR) began during World War II when scientists applied analytical methods to solve complex military problems. Since then, OR has expanded to help organizations with strategic decision-making. OR uses interdisciplinary teams and quantitative techniques like linear programming to build mathematical models of systems. These models help optimize resource allocation and identify optimal solutions. OR aims to improve systems through objective, data-driven analysis and continues providing value as new problems emerge over time.
This is a presentation from video on 'Introduction to Operations Research' available at the end of this presentations and directly at https://youtu.be/PSOW3_gX2OU
Topics like Organisations of Operations Research, History of Operations Research Role of Operations Research(OR), Scope of Operations Research(OR), Characteristics of Operations Research(OR), Attributes of Operations Research(OR).
This video also talks about Models of Operations Research
• Degree of abstraction
o Mathematical models
o Language models
o Concrete models
• Function
o Descriptive models
o Predictive models
o Normative models
• Time Horizon
o Static models
o Dynamic models
• Structure
o Iconic or physical models
o Analog or schematic models
o Symbolic or mathematical models
• Nature of environment
o Deterministic models
o Probabilistic models
• Extent of generality
o General model
o Specific models
Operational research is the scientific study of operations aimed at improving decision-making. It originated from military planning in World War II and has since expanded to various industries. In public health, operational research uses analytical methods to identify health program problems, potential solutions, and test solutions to inform evidence-based decisions around programs. It involves interdisciplinary teams that study issues like disease screening, outbreak response, and health behavior programs. Societies like IFORS and journals promote the field. Overall, operational research integrates data analysis into program management to enhance monitoring and evaluation.
This document provides an overview of operations research (OR). It discusses how OR emerged from developments in military operations during World War II and was later applied to industrial problems. OR takes a scientific approach to solving organizational problems by using interdisciplinary teams and systems analysis. It aims to determine optimal solutions and courses of action given limited resources. The document outlines the scope and methodology of OR, including how it can help managerial decision making. It also discusses different types of OR models and techniques.
The document provides an overview of operations research techniques. It discusses:
- Operations research aims to improve decision-making through methods like simulation, optimization, and data analysis.
- Major applications include production scheduling, inventory control, transportation planning, and more.
- The techniques were developed in World War II and are now used widely in business for problems like resource allocation, forecasting, and process improvement.
This document provides an overview of operations research (OR). It defines OR as the scientific approach to problem solving and decision making through mathematical modeling and analysis. The document outlines the history, terminology, problem solving process, and applications of OR. Key points include that OR uses scientific methods to help organizations make better decisions, solve complex problems, and optimize performance across various industries and applications such as production, marketing, finance, and research.
This document presents 15 quantitative techniques and tools: Linear Programming, Queuing Theory, Inventory Control Method, Net Work Analysis, Replacement Problems, Sequencing, Integer Programming, Assignment Problems, Transportation Problems, Decision Theory and Game Theory, Markov Analysis, Simulation, Dynamic Programming, Goal Programming, and Symbolic Logic. It provides a brief overview of each technique, describing its purpose and typical applications.
Operational research is a systematic approach to decision-making that uses analytical and statistical techniques to arrive at optimal or near-optimal solutions to complex problems. The document provides an overview of operational research, including its history, scope, methodologies, tools and techniques, and applications in various fields such as national planning, defense, and industry. It also discusses some limitations of operational research related to incorporating non-quantifiable factors, computational complexity, and challenges in implementation.
Operation research history and overview application limitationBalaji P
This document provides an overview of operation research (OR). It discusses OR topics like quantitative approaches to decision making, the history and definition of OR, common OR models like linear programming and network flow programming, and applications of OR. It also explains problem solving, decision making, and quantitative analysis approaches. OR aims to apply analytical methods to help make optimal decisions for complex systems and problems.
This document provides an introduction to operational research. It discusses the definition, history, scope, methodologies, tools, and limitations of operational research. Operational research uses analytical and quantitative approaches to help organizations make better decisions and solve complex problems. It originated during World War II to support military planning and operations and has since been applied across various domains including manufacturing, transportation, healthcare, and government.
This document provides an overview of operational research (OR). It discusses the history, definition, scope, phases and applications of OR. OR aims to apply scientific techniques to improve decision making and maximize benefits. It has been used extensively in public health to strengthen programs. Some examples of OR discussed are studies conducted by RNTCP to improve tuberculosis case detection and treatment. OR plays an important role in evaluating and improving health systems but faces challenges like limited funding and trained workforce.
Operation Research (Introduction of Operation Research)Yamini Kahaliya
Operation research is a systematic approach to determine the optimum (best) solution under the restriction of limited resources.
The ppt contains details information about :-
1. Meaning of OR
2. History of OR
3. Objective of OR
4. Characteristics of OR
5. Scope of OR
6. Phases of OR
7. OR models
8. Techniques of OR
9. Methodology of OR
10. Limitation of OR
Operations research is a scientific approach to decision making that was developed during World War II and is now used widely in business and industry. It involves defining problems quantitatively and building mathematical models to represent real-world situations. These models are used to evaluate alternative solutions systematically and predict outcomes in order to optimize decisions. The process involves identifying problems, developing models, obtaining optimal solutions using techniques like linear programming, testing the model solutions, and implementing the best solution. Operations research helps organizations make more informed decisions using data, consider all options, and manage resources effectively.
Operational research (OR) is an analytical method that uses mathematical modeling to help organizations make optimal decisions. It breaks problems down into components and solves them systematically using defined steps. OR aims to help executives obtain the best solution using techniques like modeling interrelationships between subsystems. It applies scientific methods without personal bias to handle complex problems requiring interdisciplinary teamwork and computer modeling. The OR process involves 7 steps: formulating the problem, observing the system, modeling the problem mathematically, verifying the model, selecting alternatives, presenting results, and implementing and evaluating recommendations. OR has wide applications in fields like national planning, defense, industry, research, business, agriculture, education, transportation, and home management.
Solving Degenaracy in Transportation Problemmkmanik
- The document discusses solving degeneracy in transportation problems using the example of a transportation problem with 4 sources and 5 destinations.
- An initial basic feasible solution is found using the least cost method, but it results in a degenerate solution since the number of allocated cells is less than m + n - 1.
- To solve the degeneracy, an unallocated cell is selected and allocated a value to satisfy the condition. Here, an unallocated cell value of 5 is selected and assigned the value ε.
- The solution is then optimized using the U-V method by calculating Uj + Vi = Cij for allocated cells and penalties Pij for unallocated cells until all penalties are less than
Introduction, Meaning and Characteristics of Operations Research Background of Operations Research, Operations Research, Scope of Operations Research, Finance department, Personnel Management, applications of operations research in business, applications of operations research, Hewlett-Packard, CHARACTERISTICS OF OPERATIONS RESEARCH, are addressed.
Subscribe to Vision Academy for Video Assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
applications of operation research in businessraaz kumar
1) Operations research is a quantitative approach to decision making based on the scientific method of problem solving. It involves modeling real-life situations as mathematical problems to arrive at optimal or near-optimal solutions.
2) The key steps in operations research problem solving are defining the problem, determining alternative solutions, evaluating alternatives using criteria, choosing the best alternative, implementing the chosen alternative, and evaluating the results.
3) Common techniques used in operations research include linear programming, transportation modeling, assignment modeling, and simulation methods like PERT/CPM. These techniques help optimize objectives while satisfying constraints.
The document discusses operations research (OR), including its origins during WWII to optimize resource allocation, its goal of applying scientific principles to optimize complex business and organizational problems, and its use of quantitative modeling and analysis. OR aims to find the global optimum solution by analyzing relationships between system components. It uses interdisciplinary teams and scientific methods to develop mathematical and other models of real-world problems, which are then solved using techniques like linear programming. The models represent important variables and constraints. OR has wide applications in areas like the military, production, transportation, and resource allocation.
this ppt is helpful for BBA/B.tech//MBA/M.tech students.
the ppt is on simulation topic...its covers -
Meaning
Advantages & Disadvantages
Uses
Process
Monte Carlo SImulation
Advantages & Disadvantages
Its example
This document discusses linear programming techniques for managerial decision making. Linear programming can determine the optimal allocation of scarce resources among competing demands. It consists of linear objectives and constraints where variables have a proportionate relationship. Essential elements of a linear programming model include limited resources, objectives to maximize or minimize, linear relationships between variables, homogeneity of products/resources, and divisibility of resources/products. The linear programming problem is formulated by defining variables and constraints, with the objective of optimizing a linear function subject to the constraints. It is then solved using graphical or simplex methods through an iterative process to find the optimal solution.
This document provides an overview of game theory and two-person zero-sum games. It defines key concepts such as players, strategies, payoffs, and classifications of games. It also describes the assumptions and solutions for pure strategy and mixed strategy games. Pure strategy games have a saddle point solution found using minimax and maximin rules. Mixed strategy games do not have a saddle point and require determining the optimal probabilities that players select each strategy.
Operational research (OR) is the scientific approach to problem solving and decision making. It involves modeling complex real-world situations and using analytical methods to evaluate solutions and help decision makers choose optimal alternatives. Some key OR techniques include linear programming, simulation, and data analysis. OR has been successfully applied in many fields like transportation, manufacturing, healthcare, and the airline industry to improve efficiency, maximize profits, and aid strategic planning. The document provides an overview of OR methodology, history, applications, and examples of its use.
Aggregate planning involves developing a preliminary production schedule over the next 6-18 months to satisfy forecasted demand at minimum cost. It considers targeted sales, production levels, inventory levels and backlogs. The objectives are to minimize costs and changes while maximizing profits, customer service and resource utilization. Common strategies are level, which maintains steady output/employment, or chase, which matches demand period to period. Techniques to develop plans include linear programming, linear decision rules and simulation models.
This document discusses simulation as a technique used in operations research to analyze the behavior of systems. It provides examples of how simulation works by initializing a system, generating inputs, observing outputs, and collecting statistics. Some key uses of simulation mentioned include testing policy decisions, conducting experiments without disrupting real systems, and obtaining operating characteristics estimates faster than working with actual systems. The document also outlines some advantages and limitations of the simulation approach. It includes two examples demonstrating how to simulate daily demand for a bakery and daily production for a moped manufacturer using random numbers.
Models of Operations Research is addressedSundar B N
Introduction, Meaning and Characteristics of Operations Research is addressed.
MODELS IN OPERATIONS RESEARCH, Classification of Models, degree of abstraction, Purpose Models, Predictive models, Descriptive models, Prescriptive models, Mathematic / Symbolic models, Models by nature of an environment, Models by the extent of generality, Models by Behaviour, Models by Method of Solution, Models by Method of Solution, Static and dynamic models, Iconic models Iconic models, Analogue models.
Subscribe to Vision Academy for Video Assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The document provides an introduction to operations research (OR). It discusses that OR is a scientific approach to decision making and problem solving that uses techniques from areas like statistics, probability theory, and other applied mathematics. It then discusses the history and origins of OR, noting it emerged from military operations research during World War II. Several key areas where OR is applied are also outlined like national planning, defense, industrial operations, and engineering. The rest of the document defines OR and lists some of its common methodologies, tools, and limitations.
The document provides an overview of the history and applications of operations research (OR). It discusses:
- OR originated in the UK during World War II when scientists were called upon to apply a scientific approach to military operations and allocate scarce resources effectively.
- The success of OR in the military spread its use to other government departments and industries.
- Today, OR uses quantitative techniques like mathematical modeling, computer analysis and simulation to help organizations like the military, businesses, transportation and more make optimal decisions. It breaks problems down and finds the best solutions.
This document provides an overview of operations research (OR). It defines OR as the scientific approach to problem solving and decision making through mathematical modeling and analysis. The document outlines the history, terminology, problem solving process, and applications of OR. Key points include that OR uses scientific methods to help organizations make better decisions, solve complex problems, and optimize performance across various industries and applications such as production, marketing, finance, and research.
This document presents 15 quantitative techniques and tools: Linear Programming, Queuing Theory, Inventory Control Method, Net Work Analysis, Replacement Problems, Sequencing, Integer Programming, Assignment Problems, Transportation Problems, Decision Theory and Game Theory, Markov Analysis, Simulation, Dynamic Programming, Goal Programming, and Symbolic Logic. It provides a brief overview of each technique, describing its purpose and typical applications.
Operational research is a systematic approach to decision-making that uses analytical and statistical techniques to arrive at optimal or near-optimal solutions to complex problems. The document provides an overview of operational research, including its history, scope, methodologies, tools and techniques, and applications in various fields such as national planning, defense, and industry. It also discusses some limitations of operational research related to incorporating non-quantifiable factors, computational complexity, and challenges in implementation.
Operation research history and overview application limitationBalaji P
This document provides an overview of operation research (OR). It discusses OR topics like quantitative approaches to decision making, the history and definition of OR, common OR models like linear programming and network flow programming, and applications of OR. It also explains problem solving, decision making, and quantitative analysis approaches. OR aims to apply analytical methods to help make optimal decisions for complex systems and problems.
This document provides an introduction to operational research. It discusses the definition, history, scope, methodologies, tools, and limitations of operational research. Operational research uses analytical and quantitative approaches to help organizations make better decisions and solve complex problems. It originated during World War II to support military planning and operations and has since been applied across various domains including manufacturing, transportation, healthcare, and government.
This document provides an overview of operational research (OR). It discusses the history, definition, scope, phases and applications of OR. OR aims to apply scientific techniques to improve decision making and maximize benefits. It has been used extensively in public health to strengthen programs. Some examples of OR discussed are studies conducted by RNTCP to improve tuberculosis case detection and treatment. OR plays an important role in evaluating and improving health systems but faces challenges like limited funding and trained workforce.
Operation Research (Introduction of Operation Research)Yamini Kahaliya
Operation research is a systematic approach to determine the optimum (best) solution under the restriction of limited resources.
The ppt contains details information about :-
1. Meaning of OR
2. History of OR
3. Objective of OR
4. Characteristics of OR
5. Scope of OR
6. Phases of OR
7. OR models
8. Techniques of OR
9. Methodology of OR
10. Limitation of OR
Operations research is a scientific approach to decision making that was developed during World War II and is now used widely in business and industry. It involves defining problems quantitatively and building mathematical models to represent real-world situations. These models are used to evaluate alternative solutions systematically and predict outcomes in order to optimize decisions. The process involves identifying problems, developing models, obtaining optimal solutions using techniques like linear programming, testing the model solutions, and implementing the best solution. Operations research helps organizations make more informed decisions using data, consider all options, and manage resources effectively.
Operational research (OR) is an analytical method that uses mathematical modeling to help organizations make optimal decisions. It breaks problems down into components and solves them systematically using defined steps. OR aims to help executives obtain the best solution using techniques like modeling interrelationships between subsystems. It applies scientific methods without personal bias to handle complex problems requiring interdisciplinary teamwork and computer modeling. The OR process involves 7 steps: formulating the problem, observing the system, modeling the problem mathematically, verifying the model, selecting alternatives, presenting results, and implementing and evaluating recommendations. OR has wide applications in fields like national planning, defense, industry, research, business, agriculture, education, transportation, and home management.
Solving Degenaracy in Transportation Problemmkmanik
- The document discusses solving degeneracy in transportation problems using the example of a transportation problem with 4 sources and 5 destinations.
- An initial basic feasible solution is found using the least cost method, but it results in a degenerate solution since the number of allocated cells is less than m + n - 1.
- To solve the degeneracy, an unallocated cell is selected and allocated a value to satisfy the condition. Here, an unallocated cell value of 5 is selected and assigned the value ε.
- The solution is then optimized using the U-V method by calculating Uj + Vi = Cij for allocated cells and penalties Pij for unallocated cells until all penalties are less than
Introduction, Meaning and Characteristics of Operations Research Background of Operations Research, Operations Research, Scope of Operations Research, Finance department, Personnel Management, applications of operations research in business, applications of operations research, Hewlett-Packard, CHARACTERISTICS OF OPERATIONS RESEARCH, are addressed.
Subscribe to Vision Academy for Video Assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
applications of operation research in businessraaz kumar
1) Operations research is a quantitative approach to decision making based on the scientific method of problem solving. It involves modeling real-life situations as mathematical problems to arrive at optimal or near-optimal solutions.
2) The key steps in operations research problem solving are defining the problem, determining alternative solutions, evaluating alternatives using criteria, choosing the best alternative, implementing the chosen alternative, and evaluating the results.
3) Common techniques used in operations research include linear programming, transportation modeling, assignment modeling, and simulation methods like PERT/CPM. These techniques help optimize objectives while satisfying constraints.
The document discusses operations research (OR), including its origins during WWII to optimize resource allocation, its goal of applying scientific principles to optimize complex business and organizational problems, and its use of quantitative modeling and analysis. OR aims to find the global optimum solution by analyzing relationships between system components. It uses interdisciplinary teams and scientific methods to develop mathematical and other models of real-world problems, which are then solved using techniques like linear programming. The models represent important variables and constraints. OR has wide applications in areas like the military, production, transportation, and resource allocation.
this ppt is helpful for BBA/B.tech//MBA/M.tech students.
the ppt is on simulation topic...its covers -
Meaning
Advantages & Disadvantages
Uses
Process
Monte Carlo SImulation
Advantages & Disadvantages
Its example
This document discusses linear programming techniques for managerial decision making. Linear programming can determine the optimal allocation of scarce resources among competing demands. It consists of linear objectives and constraints where variables have a proportionate relationship. Essential elements of a linear programming model include limited resources, objectives to maximize or minimize, linear relationships between variables, homogeneity of products/resources, and divisibility of resources/products. The linear programming problem is formulated by defining variables and constraints, with the objective of optimizing a linear function subject to the constraints. It is then solved using graphical or simplex methods through an iterative process to find the optimal solution.
This document provides an overview of game theory and two-person zero-sum games. It defines key concepts such as players, strategies, payoffs, and classifications of games. It also describes the assumptions and solutions for pure strategy and mixed strategy games. Pure strategy games have a saddle point solution found using minimax and maximin rules. Mixed strategy games do not have a saddle point and require determining the optimal probabilities that players select each strategy.
Operational research (OR) is the scientific approach to problem solving and decision making. It involves modeling complex real-world situations and using analytical methods to evaluate solutions and help decision makers choose optimal alternatives. Some key OR techniques include linear programming, simulation, and data analysis. OR has been successfully applied in many fields like transportation, manufacturing, healthcare, and the airline industry to improve efficiency, maximize profits, and aid strategic planning. The document provides an overview of OR methodology, history, applications, and examples of its use.
Aggregate planning involves developing a preliminary production schedule over the next 6-18 months to satisfy forecasted demand at minimum cost. It considers targeted sales, production levels, inventory levels and backlogs. The objectives are to minimize costs and changes while maximizing profits, customer service and resource utilization. Common strategies are level, which maintains steady output/employment, or chase, which matches demand period to period. Techniques to develop plans include linear programming, linear decision rules and simulation models.
This document discusses simulation as a technique used in operations research to analyze the behavior of systems. It provides examples of how simulation works by initializing a system, generating inputs, observing outputs, and collecting statistics. Some key uses of simulation mentioned include testing policy decisions, conducting experiments without disrupting real systems, and obtaining operating characteristics estimates faster than working with actual systems. The document also outlines some advantages and limitations of the simulation approach. It includes two examples demonstrating how to simulate daily demand for a bakery and daily production for a moped manufacturer using random numbers.
Models of Operations Research is addressedSundar B N
Introduction, Meaning and Characteristics of Operations Research is addressed.
MODELS IN OPERATIONS RESEARCH, Classification of Models, degree of abstraction, Purpose Models, Predictive models, Descriptive models, Prescriptive models, Mathematic / Symbolic models, Models by nature of an environment, Models by the extent of generality, Models by Behaviour, Models by Method of Solution, Models by Method of Solution, Static and dynamic models, Iconic models Iconic models, Analogue models.
Subscribe to Vision Academy for Video Assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The document provides an introduction to operations research (OR). It discusses that OR is a scientific approach to decision making and problem solving that uses techniques from areas like statistics, probability theory, and other applied mathematics. It then discusses the history and origins of OR, noting it emerged from military operations research during World War II. Several key areas where OR is applied are also outlined like national planning, defense, industrial operations, and engineering. The rest of the document defines OR and lists some of its common methodologies, tools, and limitations.
The document provides an overview of the history and applications of operations research (OR). It discusses:
- OR originated in the UK during World War II when scientists were called upon to apply a scientific approach to military operations and allocate scarce resources effectively.
- The success of OR in the military spread its use to other government departments and industries.
- Today, OR uses quantitative techniques like mathematical modeling, computer analysis and simulation to help organizations like the military, businesses, transportation and more make optimal decisions. It breaks problems down and finds the best solutions.
Operational research is the scientific approach to problem solving and decision making. It involves formulating problems mathematically and using scientific techniques like simulation, optimization, and data analysis to solve complex real-world problems. Some key applications of operational research include supply chain management, transportation and logistics, production scheduling, and resource allocation in industries like airlines, manufacturing, and healthcare. The goal is to help decision makers identify optimal solutions and improve performance.
This PPT covers Introduction of Operations research, Features, phases,Limitations of OR Travelling salesman problem, Assignment Problems, transportation Problems, Replacement Problems,EOQ,Inventory Control
The document provides an overview of operations research (OR), including its history, methodology, tools and techniques, and applications. It discusses how OR began during World War II to analyze military operations and optimize resource allocation. The seven main steps of the OR methodology are described. Common OR tools include linear programming, game theory, decision theory, queuing theory, inventory models, simulation, and dynamic programming. Finally, the document outlines some example applications of OR in fields like accounting, construction, and facilities planning.
Operational research (OR) is a scientific approach to decision-making that aims to provide rational solutions to complex problems. It involves modeling real-world situations mathematically and using analytical and numerical techniques to determine optimal or near-optimal solutions. OR emerged in the 1940s to help Allied forces in World War II and has since been applied widely in business and industry. Key aspects of OR include quantitative modeling and analysis, interdisciplinary team-based problem solving, and using data and experimentation to evaluate alternative solutions and recommend optimal decisions.
Business Application of Operation ResearchAshim Roy
This document discusses a project on the business applications of operations research. It begins with an acknowledgment section thanking teachers and parents for their support. The main body provides an abstract, introduction and overview of operations research. It discusses the early history and development of OR, and provides examples of its applications in business such as optimizing supply chain management and power grid operations. The document outlines the various techniques, methods, and areas where OR is applied to improve decision making and efficiency.
Operations research (OR) is a science that deals with problem formulation, solutions, and decision-making, especially for allocating scarce resources efficiently. It uses mathematical models and other scientific techniques. OR began after WWII to study problems arising from difficult military situations. It has since been applied to business and other domains to optimize performance. The chapter introduces OR and its phases: observing the problem environment, analyzing and defining the problem, and developing a mathematical model to represent the real-world situation. OR takes an interdisciplinary, systematic approach to provide a rational basis for decision-making.
Operational research (OR) uses analytical techniques to improve decision-making and efficiency. It encompasses problem-solving methods applied to optimize performance. OR analyzes systems through mathematical modeling, simulation, and other techniques. It aims to make the best use of resources by carefully planning and analyzing processes. Examples of OR applications include scheduling, facility planning, forecasting, yield management, and defense logistics. The field originated from military planning in World War II and has since expanded to business, industry, and public policy problems.
Operational research (OR) is defined as a systematic and analytical approach to decision-making and problem-solving. It uses techniques from mathematics, statistics, and other fields to arrive at optimal or near-optimal solutions to complex problems. Some key points made in the document include: OR aims to help executives make better decisions; it follows a scientific approach and uses interdisciplinary teams; it considers the system as a whole and aims to find the best objective solution. OR has wide applications in fields like national planning, defense, industry, R&D, and agriculture. The OR modeling process typically involves 7 phases: problem formulation, system observation, model formulation, model verification, alternative selection, presentation of results, and implementation/evaluation.
The document provides an introduction to operations research (OR), including its origins in solving military problems during World War II, definitions, characteristics, methodology, application areas, and limitations. OR emerged as a field to help allocate scarce resources effectively for military operations. It uses scientific and mathematical modeling approaches to help executives make rational decisions. Some key points made in the document include:
- OR is interdisciplinary and system-focused, drawing on techniques from multiple fields to analyze complex systems.
- The OR methodology typically involves observing the problem environment, defining the problem, developing a mathematical model, inputting appropriate data, testing solutions, and implementing recommendations.
- OR is applied across various domains including defense, industry, transportation,
LITERATURE REVIEW OF OPTIMIZATION TECHNIQUE BUSINESS: BASED ON CASEIAEME Publication
In today’s complex business world, decision making plays a vital role in the
success of any business. The simplex method, an operation research technique is
widely used to finding solutions in many real world problems. This paper is an attempt
to get an insight about the various application of optimization techniques in business.
It is a conceptual research based on various literatures available. This study is based
on different cases applied on selected sectors, viz., industrial, financial, resource
allocation, agriculture, marketing and personnel management area.
The document discusses operations research (OR), which uses analytical methods to help organizations make better decisions. OR involves decomposing problems, developing mathematical models, and using techniques like simulation, optimization, and data analysis to evaluate alternatives and identify optimal solutions. The document provides examples of how OR has been applied in various sectors to improve efficiency and reduce costs. It also outlines the typical phases of an OR project, including problem identification, mathematical modeling, and ensuring available data can support the model.
Operational research (OR) is a discipline that deals with applying advanced analytical methods to help make better decisions. OR uses scientific methods and especially mathematical modeling to study complex problems. It is considered a subfield of applied mathematics. Some key applications of OR include scheduling, facility planning, planning and forecasting, credit scoring, marketing, and defense planning. OR takes a systems approach, uses interdisciplinary teams, and aims to optimize objectives subject to constraints through quantitative modeling and analysis.
This document provides an overview of operations research. It discusses the history and origins of operations research, which began during World War II to help optimize the use of limited resources for the war effort. It developed further after the war to improve industrial efficiency. The document defines operations research as the application of scientific methods to decision making and problem solving. It outlines the significance of operations research, including its use of a scientific approach, interdisciplinary teamwork, systems approach, and computers to analyze complex problems. The document also provides examples of industries that utilize operations research techniques.
Operational research (OR) is the application of advanced analytical techniques to improve decision making. It involves using tools from mathematics like algorithms, statistics, and modeling techniques to find optimal solutions to complex problems. Some common OR techniques include linear programming, network flow programming, integer programming, nonlinear programming, dynamic programming, and stochastic programming. OR has many applications in business for issues like inventory planning, production scheduling, financial management, and risk management. It helps organizations make better decisions around areas like sequencing jobs, production scheduling, and introducing new products/facilities. OR allows for more systematic and analytical decision making with less risk of errors.
The document discusses problem solving approaches and techniques in operations research. It defines operations research as using quantitative methods to assist decision-makers in designing, analyzing, and improving systems to make better decisions. The scientific approach involves studying differences between past and present cases while considering new environmental factors. Some quantitative techniques mentioned include break-even point analysis, financial analysis, and decision theory. The document also provides examples of linear programming models and their components.
Operational Research and Organizational SystemIJRES Journal
Organizational systems, as well as specific integration of social and technical systems are extremely important for the development of human society. The most part, the problems of managing these systems are reduced to operations research - a generic term for activities that define the processes involved in the functions of organizational systems, and hence the term operations research. Field of study operations research as a scientific discipline, the organizational processes and activities that are being carried out and an important determinant of the intention to find the best decisions in managing the operations undertaken to achieve the set goals of the system. The generality of operations research is reflected in the fact that apply to all types of organizational systems - commercial, industrial, agricultural, military, medical, educational, government, and the like. Users of operations research decision makers - managers, whose task is to efficiently and effectively manage organizational systems. In this paper we consider operational research and conceptual foundations that enable its effective use in solving the problem of organizational systems.
Design and Analysis of Runout Measuring Machine using Feaijtsrd
Industrial engineering is a branch of engineering which deals with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of integrated systems of people, money, knowledge, information, equipment, energy, materials, analysis and synthesis, as well as the mathematical, physical and social sciences together with the principles and methods of engineering design to specify, predict, and evaluate the results to be obtained from such systems or processes. While industrial engineering is a traditional and longstanding engineering discipline subject to and eligible for professional engineering licensure in most jurisdictions, its underlying concepts overlap considerably with certain business oriented disciplines such as operations management. Depending on the subspecialties involved, industrial engineering may also be known as, or overlap with, operations management, management science, operations research, systems engineering, management engineering, manufacturing engineering, ergonomics or human factors engineering, safety engineering, or others, depending on the viewpoint or motives of the user. For example, in health care, the engineers known as health management engineers or health systems engineers are, in essence, industrial engineers by another name. Mr. Sandip Subhash Narkhede | Mr. Vijay Liladhar Firke | Mr. Dhruvakumar B. Sharma "Design and Analysis of Runout Measuring Machine using Fea" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28028.pdf Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/28028/design-and-analysis-of-runout-measuring-machine-using-fea/mr-sandip-subhash-narkhede
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2. INTRODUCTION TO
OPERATIONAL RESEARCH
Operational Research is a systematic and analytical
approach to decision making and problem solving.
O.R. as termed in USA, Canada, Africa, Australia
and Operational Research as termed in Europe, is
an Branch of applied mathematics that uses
techniques and statistics to arrive at Optimal
solutions to solve complex problems.
3. INTRODUCTION TO
OPERATIONAL RESEARCH
It is typically concerned with determining the
maximum profit, sale, output, crops yield and
efficiency and minimum losses, risks, cost, and
time of some objective function. It have also
become an important part of INDUSTRIAL
ENGINEERING PROFESSION.
4. Some of the PRIMARY TOOLS used
by operation researchers are-
STATISTICS
GAME THEORY
PROBABILITY THEORY, etc.
5. HISTORY OF
OPERATIONAL RESEARCH
There is no clear history that marks the Birth
if O.R., it is generally accepted that the field
originated in England during the World War II.
Some of them says that Charles Babbage (1791-
1871) is the Father of O.R because his research
into the cost of transportation and sorting of
mail led to England’s University Penny Post in
1840.
6. HISTORY OF
OPERATIONAL RESEARCH
Modern Operations Research originated at the
Bowdsey Research Station in U.K. in 1937 to
analyse and improve the working of the UK’s
Early Warning Rador System.
During the Second World War about 1000 Men
and Women were engaged to work for British
Army.
After World War II, Military Operational
Research in U.K. became Operational Analysis
(OA) within the U.K. Ministry of Defence with
expanded techniques and graving awareness.
7. OPERATIONAL RESEARCH
IN INDIA
The Operational Research Society of India was
founded in 1957 to provide a forum for the
Operational Research Scientists as well as an
avenue to widen their horizon by exchange of
knowledge and application of techniques from
outside the country. The Society is affiliated to
the International Federation of Operational
Research Societies (IFORS).
8. OPERATIONAL RESEARCH
IN INDIA
The Headquarters of the Society is located in
Kolkata at 39, Mahanirvan Road, Kolkata
700029, India. At present the Society has 12
Operating Chapters located
in Agra,Ahmedabad, Ajmer, Bangalore,
Chennai, Delhi, Durgapur,
Jamshedpur, Kolkata, Madurai,
Mumbai and Tirupati.
9. OPERATIONAL RESEARCH
IN INDIA
The Objectives of the Society comprise
advancement of, conducting of research in,
study of, promotion and propagation of
knowledge in Operational Research and Allied
Techniques through exchange of information, as
well as establishment, improvement and
maintenance of professional and academic
standards of work known as Operational
Research.
10. OPERATIONAL RESEARCH
IN INDIA
Contribution to the Society towards attainment
of these objectives is eligible for exemption of
income tax under Section 80(G)(5)(vi) of the
Income Tax Act 1961.
The Society Publishes a quarterly
journal OPSEARCH, which brings out high
quality and state of the art papers in Operational
Research.
11. OPERATIONAL RESEARCH
IN INDIA
In order to provide opportunity to professionals
and students to equip themselves with the
knowledge and usage of the science of
Operational Research, the Society is conducting
an examination on Graduate Diploma in
Operational Research since 1973.
12. DEFINITION OF
OPERATIONAL RESEARCH
It is an Act of winning wars without actually
fighting.
-Aurther Clark
It is a Scientific Approach to problem solving for
executive management.
-H.M. Wagner
It is Art of giving bad answers to problem which
otherwise have worse answers.
-T.L. Saaty
14. DECISION MAKING
Every industrial organisation faces multifacet
problems to identify best possible solution to their
problems.
OR aims to help the executives to obtain optimal
solution with the use of OR techniques.
It also helps the decision maker to improve his
creative and judicious capabilities, analyse and
understand the problem situation leading to
better control, better co-ordination, better
systems and finally better decisions.
15. SCIENTIFIC APPROACH
OR applies scientific methods, techniques and
tools for the purpose of analysis and solution of
the complex problems.
In this approach there is no place for guesswork
and the person bias of the decision maker.
16. INTER-DISCIPLINARY
TEAM APPROACH
Basically the industrial problems are of
complex nature and therefore require a team
effort to handle it.
This team comprises of scientist, mathematician
and technocrats. Who jointly use the OR tools to
obtain a optimal solution of the problem. They
tries to analyse the cause and effect relationship
between various parameters of the problem and
evaluates the outcome of various alternative
strategies.
17. SYSTEM APPROACH
The main aim of the system approach is to
trace out all significant and indirect effects for
each proposal on all sub-system on a system
and to evaluate each action in terms of effects
for the system as a whole.
The inter-relationship and interaction of
each sub-system can be handled with the help
of mathematical/analytical models of OR to
obtain acceptable solution.
18. USE OF COMPUTERS
The models of OR need lot of
computation and therefore, the use of
computers becomes necessary.
With the use of computers it is
possible to handle complex problems
requiring large amount of calculations.
The objective of the operations
research models is to attempt and to
locate Best or Optimal Solution.
19. OBJECTIVES
Operational Research always try to find
the best and optimal solution to the
problem.
For this purpose objectives of the
organisation are defined and analysed.
These objectives are then used as the
basis to compare the alternative courses
of action.
20. HUMAN FACTORS
In deriving, Quantitative Solutions we do
not consider human factors, which
doubtlessly play a great role in the
problems.
So, study of O.R. is incomplete without
study of human factors.
21. SCOPE OF
OPERATIONAL RESEARCH
The scope of OR is not only confined to any specific
agency like defence services but today it is widely used in
all industrial organisations.
It can be used to find the best solution to any problem be
it simple or complex. It is useful in every field of human
activities. Thus, it attempts to resolve the conflicts of
interest among the components of organization in a way
that is best for the organisation as a whole.
22. The main fields where OR is
extensively used are
National Planning and Budgeting
Defence Services
Industrial Establishment and Private
Sector Units
R & D and Engineering
23. NATIONAL PLANNING AND
BUDGETING
OR is used for the Preparation of-
Five Year Plans
Annual Budgets
Forecasting of Income and Expenditure
Scheduling of Major Projects of National Importance
Estimation of GNP
GDP
Population
Employment and Generation of Agriculture Yields,
etc.
24. DEFENCE SERVICES
Basically formulation of OR started from USA Army, so it
has wide application in the areas such as:
Development of New Technology
Optimization of Cost and Time
Tender Evaluation
Setting and Layouts of Defence Projects
Assessment of “Threat Analysis”
Strategy of Battle
Effective Maintenance and Replacement of Equipment
Inventory Control,
Transportation
Supply Depots, etc.
25. INDUSTRIAL ESTABLISHMENTS
AND PRIVATE SECTOR UNITS
OR can be effectively used in-
Plant Location and Setting Finance Planning
Product and Process Planning
Facility Planning and Construction
Production Planning and Control
Purchasing
Maintenance Management
Personnel Management, etc.
26. R&D AND ENGINEERING
Research and development being the heart of
technological growth, OR has wide scope and
can be applied in-
Technology Forecasting and Evaluation,
Technology and Project Management,
Preparation of Tender and Negotiation,
Value Engineering,
Work/Method Study and so on.
27. METHODOLOGIES/APPROACHES
OF OPERATIONAL RESEARCH
1. FORMULATE THE PROBLEM
2. OBSERVE THE SYSTEM/COLLECTION OF DATA
3. FORMULATE A MATHEMATICAL MODEL OF THE
PROBELM
4. VERIFY THE MODEL
5. SOLUTION
6. ANALYSES AND PRESENT THE RESULT
7. IMPLEMENTATION AND EVALUTE RECOMMENDATIONS
28. TECHNIQUES/TOOLS OF
OPERATIONAL RESEARCH
Linear Programming
Queuing Theory
Transportation Problems
Integer Problems
Assignment Problems
Decision Theory and Games Theory
Replacement Problems
Symbolic Logic
29. LINEAR PROGRAMMING
This technique is used to find a solution for optimising a
given objective. Objective may be maximizing profits or
minimizing costs.
Objective function and Boundary conditions are linear
in nature.
LPP techniques solve Product-Mix and Distribution
problems of enterprise.
Its also used to allocate Scarce Resources in optimum
manner in problems of scheduling, product mix, etc.
30. QUEUING THEORY
This theory deals with the situations in which queue is
formed, e.g. customers waiting for services, machines
waiting for repairmen, and aircrafts waiting for landing
strips, etc.
If the Queue will be long the cost will be high due to long
waiting hour.
This technique is used to analyse the feasibility of adding
facilities and to access the amount and cost of waiting time.
This calculations can then be used to determine the
desirable number of service facilities.
31. TRANSPORTATION PROBLEMS
Transportation problems deals with
transportation of a product
From a number of sources
With limited supplies
To number of destinations
With specified demands
At the total transportation cost.
The main objective of transportation is to
Schedule Shipment from sources to destinations
in such a way so as to Minimize the Total
Transportation Cost.
32. INTEGER PROGRAMMING
Integer means complete or whole number. By
using the Integer Programming Algorithm a series
of continuous linear programming problem are
solved in such a way that the solution containing
un-acceptable non-integer value are ruled out and
the best higher programming solution is obtained.
33. ASSIGNMENT PROBLEMS
It is a special type of linear programming problem. It deals
in allocating the various resources or items to various
activities in a one to one basis in such a way that the time
or cost involved in minimised and the sale or profit is
maximized.
E.g. Manager may like to know which job should be assigned
to which person so that all jobs can be completed in the
shortest possible time.
34. DECISION THEORY AND
GAME THEORY
Decision Theory is primarily considered with decision
making under the conditions of:
Risk
Uncertainity
Game Theory is concerned with:
Decision Making under Conflict
Hence, both Decision Theory and Game Theory assist the
Decision-Maker in Analysing Problems with numerous
alternative course of action and consequences.
35. REPLACEMENT PROBLEMS
This Theory is concerned with situations that arise when
some items such as machines, men, etc. require
replacement due to their decreasing efficiency, failure or
break-down.
Sooner or later all the equipments are required to be
replaced because of:
Obsolescence
Discovery of New Technology
Better Design of Equipment
In Replacement Decisions we consider:
Cost of Equipment to be Installed
Cost of Equipment Replaced, etc.
Hence, this theory helps to solve all Replacement
Problems.
36. SYMBOLIC LOGIC
Symbols are more meaningful and accurate. Everything is
Symbolic in this world.
Words, classes of things, functional systems and rules are
substituted with symbols.
The whole problem is converted into algebraic equations
and propositions. Business Problems are not commonly
converted into symbols but calculations are done on
computers, that is why symbols have extensive
applications.
37. OPERATIONAL RESEARCH AND MANAGEMENT
DECISION-MAKING
Operation Research increases the creative capabilities
of a decision maker.
It increases the effectiveness of mgt. decisions.
Management is most of the time making decisions. It is
thus a decision science which helps mgt. to take better
decisions.
Nowadays, business problems have become so
complex that it is almost impossible for a human being to
comprehend all important factors, OR Techniques can be
helpful in such situations.
It also helps in ascertaining best locations for factories
and warehouses, project scheduling as well as most
economic means of transportation.
OR study approach in business decisions leads to better
control, better co-ordination, better system and at the
end better decision.
38. MODELS & MODELING
A model in OR is a simplified representation of
an operation, or is a process in which only the
basic aspects or the most important features of a
typical problem under investigation are
considered. The objective of a model is to
identify significant factors and interrelationships.
The reliability of the solution obtained from a
model depends on the validity of the model
representing the real system.
39. A good model must possess the following
characteristics:
It should be capable of taking into account, new
formulation without having any changes in its
frame.
Assumptions made in the model should be as
small as possible.
Variables used in the model must be less in
number ensuring that it is simple and coherent.
It should be open to parametric type of
treatment.
It should not take much time in its construction
for any problem.
40. Classification of Models
Based on structure:
A. Iconic or physical models: They are pictorial
representations of real systems and have the
appearance of the real thing.
An iconic model is said to be scaled down or scaled up
according to the dimensions of the model which may be
smaller or greater than that of the real item,
e.g., city maps, houses blueprints, globe, and so on.
These models are easy to observe and describe, but are
difficult to manipulate and are not very useful for the
purpose of prediction
41. . B. Analog models: These are more abstract than the
iconic ones for there is no look alike correspondence
between these models and real life items. The models in
which one set of properties is used to represent another
set of properties are called analog models.
C. Mathematic / Symbolic models: They are most
abstract in nature. They employ a set of mathematical
symbols to represent the components of the real system.
These variables are related together by means of
mathematical equations to describe the behavior of the
system. The solution of the problem is then obtained by
applying well developed mathematical techniques to the
model.
42. Based on function or purpose
A. Descriptive models: A descriptive model simply describes
some aspects of a situation based on observations, survey.
Questionnaire results or other available data. The result of an
opinion poll represents a descriptive model.
B. Predictive models: Such models can answer ‘what if’ type of
questions, i.e. they can make predictions regarding certain events.
For example, based on the survey results, television networks such
models attempt to explain and predict the election results before
all the votes are actually counted.
C. Prescriptive models: Finally, when a predictive model has
been repeatedly successful, it can be used to prescribe a source of
action. For example, linear programming is a prescriptive (or
normative) model because it prescribes what the managers ought
to do.
43. Based on certainty
A. Deterministic models: They are those in which
all parameters and functional relationships are
assumed to be known with certainty when the
decision is to be made. Linear programming and
break-even models are the examples of
deterministic models.
B. Probabilistic / Stochastic models: These models
are those in which at least one parameter or
decision variable is a random variable. These
models reflect to some extent the complexity
of the real world and the uncertainty
surrounding it.
44. Based on time reference
A. Static models: These models do not consider
the impact of changes that takes place during
the planning horizon, i.e. they are independent
of time. Also, in a static model only one
decision is needed for the duration of a given
time period.
B. Dynamic models: In these models, time is
considered as one of the important variables
and admits the impact of changes generated by
time. Also, in dynamic models, not only one but
a series of interdependent’ decisions is required
during the planning horizon.
45. Based on method of solution
A. Analytical models: These models have a specific
mathematical structure-and thus can be solved
by known analytical or mathematical techniques.
For example, a general linear programing model
as well as the specially structured transportation
and assignment models are analytical models. .
B. Simulation models: A simulation model is
essentially computer-assisted experimentation on
a mathematical structure of a real time structure
in order to study the system under a variety of
assumptions.
46. APPLICATIONS OF OPERATIONS
RESEARCH IN BUSINESS
Accounting:
-Assigning audit teams effectively
- Credit policy analysis
-Cash flow planning
- Developing standard costs
- Establishing costs for byproducts
- Planning of delinquent account strategy
Construction:
-Project scheduling, monitoring and control
- Determination of proper work force
- Deployment of work force
- Allocation of resources to projects
47. Facilities Planning:
- Factory location and size decision
-Estimation of number of facilities required
-Hospital planning
- International logistic system design
- Transportation loading and unloading
-Warehouse location decision
Finance:
-Building cash management models
- Allocating capital among various alternatives
- Building financial planning models
- Investment analysis
-Portfolio analysis
-Dividend policy making
48. Manufacturing:
-Inventory control
-Marketing balance projection
- Production scheduling
- Production smoothing
Marketing:
-Advertising budget allocation
-Product introduction timing
-Selection of Product mix
-Deciding most effective packaging alternative
Purchasing:
-Optimal buying
-Optimal reordering
-Materials transfer
49. Organizational Behavior / Human Resources:
-Personnel planning
-Recruitment of employees
-Skill balancing
-Training program scheduling
-Designing organizational structure more effectively
Research and Development:
- R & D Projects control
- R & D Budget allocation
-Planning of Product introduction
51. MAGNITUDE OF COMPUTATION
Operations research models try to find out optimal
solution taking into account all the factors. But,
these factors are enormous
and,
expressing them in quantity,
and,
establishing relationships among these, Require
voluminous calculations which can be handled only
by computers.
52. NON-QUANTIFIABLE FACTORS
OR provides solution only when all
elements related to a problem can be
quantified.
All relevant variables do not lend themselves
to quantification. Factors which cannot be
quantified, find no place in OR study. Models
in OR do not take into account qualititative
factors or emotional factors which may be
quite important.
53. DISTANCE BETWEEN USER
AND ANALYST
OR being specialist’s job requires a
mathematician or statistician, who
might not be aware of the business
problems.
Similarly, a manager fails to understand
the complex working of OR. Thus there
is a gap between the two. Management
itself may offer a lot of resistance due to
conventional thinking.
54. TIME AND MONEY COST
When basic data are subjected to frequent
changes, incorporating them into the OR models
is a costly proposition.
Moreover, a fairly good solution at present may
be,
More desirable than a perfect OR solution
available after sometime. The computational
time increases depending upon the size of the
problem and accuracy of results desired.
55. IMPLEMENTATION
Implementation of any decision is a
delicate task. It must take into account the
complexities of human relations and
behaviour. Sometimes, resistance is offered
due to psychological factors which may not
have any bearing on the problem as well as
its solution.