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Previous year question paper for AOR (MBA 3rd)

Applied operations research

Previous year question paper with solutions for applied operations research from 2008 to 2019.

Our website provides solved previous year question paper for Applied operations research from 2008 to 2019. Doing preparation from the previous year question paper helps you to get good marks in exams. From our AOR question paper bank, students can download solved previous year question paper. The solutions to these previous year question paper are very easy to understand.

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Operations Research

1. Operations Research approach is ______________.

• multi-disciplinary
• collect essential data

2. A feasible solution to a linear programming problem ______________.

• must satisfy all the constraints of the problem simultaneously
• need not satisfy all of the constraints, only some of them
• must be a corner point of the feasible region.
• must optimize the value of the objective function

3. If any value in XB column of final simplex table is negative, then the solution is ______________.

• no solution

4. For any primal problem and its dual______________.

• optimal value of objective function is same
• dual will have an optimal solution iff primal does too
• primal will have an optimal solution iff dual does too
• both primal and dual cannot be infeasible

5. The difference between total float and head event slack is ______________

• independent float
• interference float
• linear float

6. An optimal assignment requires that the maximum number of lines which can be drawn through squares with zero opportunity cost should be equal to the number of ______________.

• rows or columns
• rows and columns.
• rows+columns- 1
• rows-columns.

7. To proceed with the Modified Distribution method algorithm for solving an transportation problem, the number of dummy allocations need to be added are______________.

8. Select the correct statement

• EOQ is that quantity at which price paid by the buyer is minimum
• If annual demand doubles with all other parameters remaining constant, the Economic Order Quantity is doubled
• Total ordering cost equals holding cost
• Stock out cost is never permitted

9. Service mechanism in a queuing system is characterized by ______________.

• customers behavior
• servers behavior
• customers in the system
• server in the system

10. The objective of network analysis is to______________.

• minimize total project duration
• minimize toal project cost
• minimize production delays, interruption and conflicts
• maximize total project duration

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Operations Research - Science topic

• asked a question related to Operations Research
• 24 Jul 2024

• 14 Aug 2024
• Knowledge Creation : Academic research aims to generate new knowledge, theories, or insights in a particular field. It seeks to advance understanding and contribute to the body of knowledge.
• Theoretical Focus : Often focused on theoretical frameworks and models, academic research explores fundamental questions and underlying principles.
• Broad and Deep : This type of research can cover a wide range of topics within a discipline or delve deeply into a specific aspect of a field. It is not always concerned with immediate practical applications.
• Innovation : Often explores new ideas or technologies without immediate practical constraints or considerations.
• Systematic and Rigorous : Follows a structured methodology, including hypothesis formulation, literature review, experimentation, and analysis. The methods are designed to ensure validity and reliability.
• Peer Review : Results are typically published in academic journals and subjected to peer review to validate findings and methodologies.
• Publications : Results are disseminated through academic papers, journals, and conferences.
• Contributions to Theory : Aims to advance theoretical understanding and knowledge in a particular field.
• Basic Science : Research into fundamental principles of physics or mathematics.
• Social Sciences : Theoretical studies on human behaviour or societal structures.
• Practical Problem Solving : Operational research (OR) focuses on solving practical problems and improving decision-making processes in organizations and industries.
• Application-Oriented : Aims to apply analytical methods to real-world problems to enhance efficiency, effectiveness, and performance.
• Specific and Applied : Deals with particular problems within organizations or industries, such as logistics, supply chain management, resource allocation, and optimization.
• Immediate Impact : Emphasizes practical outcomes and solutions that can be implemented to achieve tangible improvements.
• Applied Analytical Methods : Utilizes mathematical modelling, optimization techniques, simulations, and statistical analysis to address operational problems.
• Decision Support : Provides tools and models that help decision-makers make informed choices.
• Recommendations and Solutions : Results in actionable recommendations and solutions that can be implemented in practice.
• Reports and Tools : Often presented as reports, decision support tools, or implemented systems.
• Supply Chain Optimization : Designing efficient supply chain networks to minimize costs and improve service levels.
• Workforce Scheduling : Developing employee schedules to optimize labour costs and meet operational needs.
• Objective: Academic Research : Aims to advance knowledge and theory. Operational Research : Aims to solve practical problems and improve operations.
• Focus: Academic Research : More theoretical and exploratory. Operational Research : More practical and applied.
• Methodology: Academic Research : Structured and organized, focusing on rigour and theoretical contribution. Operational Research : Practical and solution-oriented, using analytical methods to address specific issues.
• Outcomes: Academic Research : Publications and theoretical advancements. Operational Research : Practical solutions and decision support tools.
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• Gurobi and Google OR-Tools:Gurobi and Google OR-Tools are commercial solvers that employ state-of-the-art optimization algorithms, including exact methods like branch and bound, cutting planes, and advanced heuristics. They excel at solving a wide range of optimization problems, including mixed integer programming (MIP) problems with linear and nonlinear objectives and constraints. Gurobi and OR-Tools are known for providing high-quality solutions and are often used in industry and academia when optimality is crucial. These solvers can exploit problem structure, exploit parallel computing, and incorporate various algorithmic enhancements to improve performance. They strive for optimality, but the runtime required for finding the provably optimal solution may increase exponentially as the problem size grows.
• OptaPlanner:OptaPlanner is an open-source constraint satisfaction solver and a metaheuristic optimization framework. It utilizes various metaheuristic algorithms such as simulated annealing, tabu search, and local search to iteratively explore the solution space and improve the quality of solutions. OptaPlanner is particularly well-suited for constraint satisfaction problems (CSPs), where the goal is to find feasible solutions that satisfy a set of constraints. While metaheuristics like OptaPlanner do not guarantee optimal solutions, they can often find high-quality solutions within a reasonable amount of time. OptaPlanner is known for its scalability and efficiency, making it suitable for large-scale, real-world problems.

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• Maximize the continuity of assignments or minimize gaps between assignments
• Minimize reassignments of current licenses (optional objective)
• The continuity index (CI): a quantitative measurement of the continuity of a spectrum subset, ranging from (0) for perfect continuity and (1) for maximum imperfect continuity. The continuity index works by penalizing two features, gap number in a defined subset and gap sizes.
• Work (W total ) to measure reassignments calculated as W total = w 1 + w 2 + w n , where n is the number of licenses in a given band and where W n = license n bandwidth * distance moved in Mhz

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• Particle Swarm Optimization (PSO): Particle Swarm Optimization (PSO) is a population-based optimization algorithm that is inspired by the social behavior of birds and fish. PSO has been applied to multi-objective optimization problems, and it has been shown to be effective in finding Pareto-optimal solutions.
• Multi-objective Artificial Bee Colony (MOABC): MOABC is a multi-objective optimization algorithm inspired by the foraging behavior of honeybees. MOABC has been applied to various multi-objective optimization problems and has been found to be efficient in finding the Pareto-optimal solutions
• Decomposition-based Multi-objective Optimization Algorithms (MOEA/D): Decomposition-based multi-objective optimization algorithms (MOEA/D) decompose the multi-objective problem into a set of scalar subproblems, then solve them by using a scalar optimization algorithm. MOEA/D has been found to be effective in solving multi-objective problems with high dimensionality and/or large numbers of objectives.
• Deep reinforcement learning (DRL) : DRL is a category of machine learning algorithm that allows the agent to learn by interacting with the environment and using the rewards as feedback. This approach has been used to optimize the decision-making process in multi-objective problems.
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• For every worker at least one task must be assigned;
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• Tasks must be grouped (e.g. by location), and for every group the number of workers is fixed. Every worker must be in exactly one group.
• Without 4. and 5. this problem can be stated as the “Assignment Problem”, for which there are algorithms with polynomial complexity;
• Without 4. this problem can be addressed as “Generalized Assignment Problem” which is NP-hard;
• Without 4. and if M = 1 this problem can be addressed as “0-1 Knapsack Problem”.

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Tuesday, March 2, 2021

Anna university ba 5201-applied operations research november/december 2019 question paper.

 Anna University Previous Years Old Question Papers

Question Paper Code: 70163

M.B.A.  DEGREE EXAMINATION, NOVEMBER/DECEMBER 2019

Second Semester

BA 5201-APPLIED OPERATIONS RESEARCH 

(Regulation 2017)

Time: Three hours

Maximum: 100 marks

Attachment Type: PDF and Images

PDF Download:  Click Here to Anna University  BA 5201-APPLIED OPERATIONS RESEARCH   NOVEMBER/DECEMBER 2019 Question Paper 

Question Paper Scanned Images Download  

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Operations Research Analyst Interview Questions

The most important interview questions for Operations Research Analysts, and how to answer them

Getting Started as a Operations Research Analyst

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• How to Become
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Interviewing as a Operations Research Analyst

Types of questions to expect in a operations research analyst interview, behavioral questions, technical and analytical questions, case study and scenario-based questions, quantitative and mathematical questions, software and tool proficiency questions, preparing for a operations research analyst interview, how to do interview prep as an operations research analyst.

• Understand the Company and Its Industry: Research the company's operations, industry trends, and competitive landscape. This knowledge shows your ability to apply analytical techniques to real-world business problems.
• Review Key OR Techniques and Methodologies: Be well-versed in popular operations research techniques such as linear programming, simulation, optimization, and statistical analysis. Understanding these methodologies is crucial for discussing how you can solve complex problems.
• Practice Problem-Solving Questions: Prepare for technical questions by practicing problem-solving scenarios that require you to apply OR techniques. This demonstrates your analytical thinking and ability to handle real-world challenges.
• Brush Up on Relevant Software Skills: Ensure your proficiency with software tools commonly used in operations research, such as MATLAB, R, Python, and optimization software like CPLEX or Gurobi. Familiarity with these tools can set you apart.
• Prepare Your Own Questions: Develop thoughtful questions to ask the interviewer about the company's challenges, team structure, and expectations. This shows your eagerness to understand the role and contribute effectively.
• Mock Interviews: Conduct mock interviews with a mentor or peer to get feedback and improve your interview skills. Focus on both technical and behavioral aspects to ensure a well-rounded preparation.

Stay Organized with Interview Tracking

Operations Research Analyst Interview Questions and Answers

"can you describe a complex problem you solved using operations research techniques", how to answer it, example answer, "how do you approach data collection and analysis for a new project", "describe a time when you had to present complex findings to a non-technical audience. how did you ensure they understood", "how do you stay updated with the latest trends and advancements in operations research", "what software tools are you proficient in, and how have you used them in your projects", "can you explain a time when you had to make a decision with incomplete data how did you handle it", "how do you ensure the accuracy and reliability of your models", "describe a project where you collaborated with a cross-functional team. how did you ensure successful collaboration", which questions should you ask in a operations research analyst interview, good questions to ask the interviewer, "can you describe the types of projects the operations research team typically works on", "what are the biggest operational challenges the company is currently facing", "how does the company support the professional development of its operations research analysts", "can you provide an example of a successful project led by the operations research team and what contributed to its success", what does a good operations research analyst candidate look like, quantitative analysis skills, problem-solving abilities, data interpretation and insight generation, effective communication, collaboration and teamwork, attention to detail, adaptability and continuous learning, interview faqs for operations research analysts, what is the most common interview question for operations research analysts, what's the best way to discuss past failures or challenges in a operations research analyst interview, how can i effectively showcase problem-solving skills in a operations research analyst interview.

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