MMPO-001 Solved Assignment 2026-27 in English | MBA Operations Management

MMPO-001 Solved Assignment 2026-27 in English is prepared for students studying Operations Research under MBA Operations Management (MBAOM) and Post Graduate Diploma in Operations Management (PGDIOM).

The uploaded assignment identifies the course as MMPO-001: Operations Research, with assignment code MMPO-001/TMA/JULY/2026 and All Blocks coverage. The assignment has five questions covering Operations Research, Linear Programming, Integer Programming, Monte Carlo Simulation and the Hungarian Assignment Method.

MMPO-001 Course Information

The programme-category records list MMPO-001 Operations Research among the courses of MBAOM and PGDIOM for the 2026–27 session.

MMPO-001 Solved Assignment 2026-27 Overview

The MMPO-001 Operations Research Solved Assignment 2026-27 covers important quantitative and analytical techniques used in managerial decision-making.

The assignment contains five major questions:

1. Operations Research — definition, scope and limitations
2. Linear Programming — graphical investment problem
3. Integer Programming — business applications
4. Monte Carlo Simulation — applications and process
5. Hungarian Assignment Method — optimal worker-job assignment

Question 1 — Operations Research

The first question asks students to define Operations Research, explain its scope and discuss any three major limitations of Operations Research in managerial decision-making.

Meaning of Operations Research

Operations Research is a scientific and systematic approach to managerial decision-making that uses:

• Mathematical models
• Statistical techniques
• Analytical methods
• Optimization techniques
• Quantitative analysis

Its objective is to help managers select effective solutions while considering available resources and constraints.

Operations Research combines concepts from mathematics, economics, engineering, management and computer science.

Scope of Operations Research

Operations Research has applications across many organisational functions.

Production Management

OR can help with:

• Production planning
• Inventory control
• Capacity utilisation
• Scheduling
• Plant location
• Quality improvement

Marketing

Applications include:

• Product selection
• Pricing
• Advertising-budget allocation
• Sales forecasting
• Distribution planning

Finance

OR can support:

• Investment decisions
• Portfolio management
• Capital budgeting
• Cash management
• Risk analysis
• Financial planning

Human Resources

Applications include:

• Manpower planning
• Workforce scheduling
• Recruitment planning
• Job allocation
• Performance evaluation

Logistics and Supply Chain

OR can optimise:

• Transportation routes
• Warehouse operations
• Inventory levels
• Vehicle scheduling
• Distribution networks

Limitations of Operations Research

1. Dependence on Accurate Data

OR models depend heavily on accurate, complete and reliable input data. Incorrect, outdated or incomplete information can produce poor recommendations.

2. Difficulty in Considering Human Behaviour

Human emotions, motivation, leadership, organisational culture and other behavioural factors are difficult to represent mathematically.

Therefore, managerial experience and judgement remain important.

3. High Cost and Complexity

Developing and implementing sophisticated OR models may require:

• Skilled professionals
• Specialised software
• Computing facilities
• Significant data collection
• Time and financial investment

Therefore, OR should be combined with practical knowledge and managerial judgement.

Question 2 — Linear Programming Investment Problem

The second question presents a retired investor with ₹30,000 available for investment in two bonds:

• Bond A: 7% return
• Bond B: 10% return

The investor wants:

• Total investment ≤ ₹30,000
• Bond B ≤ ₹12,000
• Bond A ≥ ₹6,000
• Bond A ≥ Bond B

The objective is to maximise annual return using the graphical method.

Decision Variables

Let:

X = Investment in Bond A

Y = Investment in Bond B

Objective Function

Maximise:

Z = 0.07X + 0.10Y

Constraints

X + Y ≤ 30,000

Y ≤ 12,000

X ≥ 6,000

X ≥ Y

X, Y ≥ 0

Corner Points

The important feasible-region corner points are:

• (6,000, 0)
• (6,000, 6,000)
• (12,000, 12,000)
• (18,000, 12,000)
• (30,000, 0)

Optimal Solution

At (18,000, 12,000):

Z = 0.07(18,000) + 0.10(12,000)

= ₹1,260 + ₹1,200

= ₹2,460

This is the highest return among the feasible corner points.

Broker’s Recommendation

Bond A = ₹18,000

Bond B = ₹12,000

Maximum annual return = ₹2,460

Question 3 — Integer Programming

The third question asks students to explain the importance of Integer Programming in business decisions and provide two real-life examples where it is more appropriate than Linear Programming.

Meaning

Integer Programming is an optimisation technique in which some or all decision variables must have whole-number values.

This is important because many business decisions involve indivisible resources such as:

• Employees
• Machines
• Vehicles
• Warehouses
• Projects

For example, a business cannot hire 4.5 employees or purchase 2.7 machines.

Importance of Integer Programming

Integer Programming helps organisations:

• Allocate scarce resources.
• Minimise costs.
• Maximise profits.
• Plan workforce requirements.
• Select projects.
• Decide facility locations.
• Schedule production.
• Plan transportation.
• Make capital-budgeting decisions.

Real-Life Example 1 — Employee Shift Scheduling

A hospital may need a specific number of nurses for different shifts.

A Linear Programming solution could theoretically produce a fractional number such as 17.5 nurses, which is impossible.

Integer Programming ensures that the number of nurses assigned to each shift remains a whole number.

Real-Life Example 2 — Warehouse Location

A retail company may need to decide which warehouses should be opened.

This is naturally represented through binary variables:

1 = Warehouse opened

0 = Warehouse not opened

Integer Programming therefore provides a realistic solution, unlike a fractional solution such as opening 0.6 of a warehouse.

Question 4 — Monte Carlo Simulation

The fourth question asks when Monte Carlo Simulation is preferred over mathematical optimisation techniques and asks students to explain two important steps in the simulation process.

Meaning

Monte Carlo Simulation is a quantitative Operations Research technique used to analyse problems involving uncertainty and risk.

It uses repeated random sampling to estimate possible outcomes.

When Monte Carlo Simulation is Preferred

1. High Uncertainty

It is useful when variables such as:

• Customer demand
• Market prices
• Production time
• Machine failures
• Inflation
• Investment returns

are unpredictable.

2. Complex Systems

Monte Carlo Simulation can be useful when mathematical relationships are highly complex or nonlinear and conventional optimisation becomes difficult.

3. Known Probability Distributions

Simulation is particularly useful when uncertain inputs can be represented through probability distributions.

4. Risk Analysis

Simulation can show:

• Range of possible outcomes
• Probabilities
• Average results
• Worst-case scenarios
• Risk levels

Step 1 — Define the Problem and Probability Distributions

The first step is to:

• Clearly define the problem.
• Identify uncertain variables.
• Specify the objective.
• Select appropriate probability distributions.

Historical data, expert opinion or statistical analysis can be used to select distributions.

Step 2 — Random Sampling and Analysis

Random samples are generated from the selected probability distributions.

Thousands of simulation trials may then be performed to represent different possible future scenarios.

The results can be analysed using:

• Average
• Variance
• Standard deviation
• Probability of success
• Confidence intervals
• Risk levels

Question 5 — Hungarian Assignment Method

The fifth question asks students to assign four workers to four jobs — A, B, C and D — so that the total completion time is minimised using the Hungarian Assignment Method (HAM).

Original Time Matrix

Step 1 — Row Reduction

The smallest values of each row are:

• Row 1 = 40
• Row 2 = 42
• Row 3 = 48
• Row 4 = 41

After row reduction:

Step 2 — Column Reduction

The column minimums are:

• Column A = 0
• Column B = 0
• Column C = 0
• Column D = 13

After column reduction:

Optimal Assignment

The independent zeros give:

Minimum Total Time

40 + 55 + 48 + 41 = 184 minutes

Therefore:

Minimum Total Time = 184 Minutes

The optimal allocation is Worker 1 → Job B, Worker 2 → Job D, Worker 3 → Job C and Worker 4 → Job A.

MAJOR TOPICS COVERED IN MMPO-001

Operations Research

• Meaning of Operations Research
• Scope of Operations Research
• OR in Production
• OR in Marketing
• OR in Finance
• OR in Human Resources
• OR in Logistics
• Limitations of OR

Linear Programming

• LPP
• Decision Variables
• Objective Function
• Constraints
• Feasible Region
• Graphical Method
• Maximisation
• Investment Problems

Integer Programming

• Integer Variables
• Whole-number Decisions
• Resource Allocation
• Workforce Scheduling
• Warehouse Location
• Binary Decisions
• Capital Budgeting

Monte Carlo Simulation

• Simulation
• Random Sampling
• Probability Distributions
• Uncertainty
• Risk Analysis
• Simulation Trials
• Statistical Analysis

Assignment Problem

• Assignment Method
• Hungarian Assignment Method
• Cost Matrix
• Row Reduction
• Column Reduction
• Optimal Assignment
• Minimum Total Time

These topics correspond to the five questions in the uploaded MMPO-001 assignment.

KEY FEATURES

• MMPO-001 Operations Research
• Solved Assignment 2026-27
• English Medium
• MBA Operations Management
• Post Graduate Diploma in Operations Management
• Programme Codes: MBAOM / PGDIOM
• Assignment Code: MMPO-001/TMA/JULY/2026
• All Blocks coverage
• Operations Research concepts
• Linear Programming
• Graphical LPP
• Integer Programming
• Monte Carlo Simulation
• Hungarian Assignment Method
• Complete numerical solution
• Digital PDF
• Instant Download
• Mobile-friendly PDF

WHO CAN USE MMPO-001?

MBAOM

MBA Operations Management

PGDIOM

Post Graduate Diploma in Operations Management

The 2026–27 programme records specifically include MMPO-001 Operations Research under both MBAOM and PGDIOM.

ASSIGNMENT SUBMISSION DATES

The assignment states:

July 2026 Semester: 31 October 2026

January 2027 Semester: 30 April 2027

HOW TO GET MMPO-001 SOLVED ASSIGNMENT PDF

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MMPO-001 — Operations Research

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FREQUENTLY ASKED QUESTIONS

1. What is MMPO-001?

MMPO-001 is Operations Research.

2. Which programmes can use MMPO-001?

MBA Operations Management (MBAOM) and PGDIOM — Post Graduate Diploma in Operations Management.

3. What is the assignment code?

MMPO-001/TMA/JULY/2026.

4. What is the medium?

English Medium.

5. What is the coverage?

All Blocks.

6. How many questions are included?

The assignment contains five main questions.

7. Does MMPO-001 include numerical questions?

Yes. The assignment includes a graphical Linear Programming investment problem and a Hungarian Assignment Method numerical problem.

8. What is the optimal investment solution in Question 2?

₹18,000 in Bond A and ₹12,000 in Bond B, producing a maximum annual return of ₹2,460.

9. What is the optimal result of the Hungarian Assignment Problem?

The minimum total time is 184 minutes:

Worker 1 → B, Worker 2 → D, Worker 3 → C, Worker 4 → A.

10. What are the main Operations Research techniques covered?

The assignment covers Linear Programming, Integer Programming, Monte Carlo Simulation and the Hungarian Assignment Method.

DISCLAIMER

Mother Publication independently prepares this material for educational and reference purposes. Students should read, understand and appropriately use the material while preparing their assignments. Mother Publication is not affiliated with, endorsed by, or officially associated with IGNOU.

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