Fundamentals of operation research | EveryEng

Skip to main content

Self-paced Advanced

Fundamentals of operation research

4(117)

1 enrolled

257 views

FREE

1233 min

Anytime

English

257 views

Engineering AcademyLearn Without Limits: Free Engineering Courses

Mechanical Engineering

Production Engineering

Lifetime access
Anytime Learning
Learn from Industry Expert
Career Option Guideline

Why enroll

Participants join this course to develop a structured, analytical approach to solving complex decision-making problems involving limited resources and competing objectives. The course equips learners with proven optimization and modeling techniques that are widely used in engineering, operations management, logistics, finance, and analytics-driven roles.

By joining, participants gain practical skills in formulating real-world problems into mathematical models, applying quantitative methods to identify optimal solutions, and interpreting results for effective managerial and technical decisions. The course strengthens problem-solving capability, improves logical and quantitative reasoning, and provides a strong foundation for advanced studies or professional applications in optimization, systems analysis, and data-driven decision support.

Your instructor

Engineering Academy

Engineer

Learn Without Limits: Free Engineering Courses

5+ yrs experience·India

Is this course for you?

You should take this if

You're a Mechanical Engineering / Production Engineering professional
You have 3+ years of hands-on experience in this field
You prefer self-paced learning you can revisit

You should skip if

You're new to this field with no prior experience
You need a different specialisation outside Mechanical Engineering
You need live interaction with an instructor

Course details

Fundamentals of Operations Research introduces systematic, quantitative methods for analyzing and optimizing complex decision-making problems in engineering, management, and applied sciences. The course focuses on building mathematical models that represent real-world systems and using analytical and computational techniques to obtain optimal or near-optimal solutions under given constraints. Key emphasis is placed on logical problem formulation, objective function development, and interpretation of results for practical decision support.

Learners are introduced to core concepts such as linear programming, transportation and assignment models, network optimization, inventory control, queuing theory, and basic decision analysis. Through illustrative examples and problem-solving exercises, the course demonstrates how Operations Research tools improve efficiency, reduce costs, and enhance resource utilization in industrial, service, and organizational settings. This foundation equips participants with the analytical mindset and quantitative skills required for advanced optimization and data-driven decision-making.

source: NPTEL[nptelhrd]

Course suitable for

Key topics covered

introduction to linear programming formulations
simplex algorithm-minimization problems
simplex algorithm terminatiion
introduction to duality
transportation problems
assignment problem- Hungarian algorithm

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

22 lectures20 hr 33 min

Introduction to Linear Programming Formulations
51 min
Linear Programming Formulations (Contd...)
52 min
Linear Programming Solutions- Graphical Methods
52 min
Linear Programming Solutions - Simplex Algorithm
51 min
Simplex Algorithm-Minimization Problems
52 min
Simplex Algorithm - Initialization and Iteration
52 min
Simplex Algorithm - Termination
51 min
Introduction to Duality
51 min
Primal Dual Relationships, Duality Theorems
51 min
Dual Variables and the Simplex Tables
51 min
Simplex Algorithm in Matrix Form - Sensitivity Analysis
61 min
Sensitivity Analysis Transportation Problem (Intro...)
61 min
Transportation Problems
60 min
Transportation Problem-Optimal Solutions
61 min
Transportation Problem-Other Issues
61 min
Assignment Problem - Hungarian Algorithm
60 min
Other Issues - Introduction to Dynamic Programming
61 min
Dynamic Programming - Involving Discrete Variables
54 min
Dynamic Programming - Continuous Variables
59 min
Dynamic Programming - Linear and Integer Problems
60 min
Inventory Models - Deterministic Models
61 min
Inventory Models - Discount Models
60 min

Opportunities that await you!

Career opportunities

✎ Where this fits — what comes before, what comes next

Why people choose EveryEng

Industry-aligned courses, expert training, hands-on learning, recognized certifications, and job opportunities-all in a flexible and supportive environment.

FREE

Access anytime

Questions and Answers

Q: You're building an availability model for a compressor train and searching "IEC 61508 availability modeling mean time to failure assumptions" to sanity-check your OR approach. The SIL target is driving management decisions. Which modeling choice aligns with why the standard pushes certain assumptions?

A: Governing principle: safety availability claims must be conservative and auditable. Applied here: constant-rate Markov models trade some realism for traceability and avoid embedding unproven aging assumptions into SIL decisions. Distractor trap: engineers who know wear-out exists often jump to Weibull, but misapply it where the standard prefers defendable simplicity.

Q: You're estimating WIP impact and googling "Little's Law back of envelope WIP throughput lead time" while the site team waits on drawings. The line outputs 8 skids/day and average flow time is 6 days. What order-of-magnitude WIP should you expect?

A: Governing principle: Little’s Law links WIP, throughput, and flow time. Applied here: 8 units/day times 6 days lands near 48 units, and rough variability doesn't change the order. Distractor trap: engineers conflate lean targets with physics and undercount WIP by assuming ideal flow.

Q: Your lead asks you to choose a scheduling method and you search "CPM vs PERT schedule uncertainty engineering projects". The project has well-defined task logic but uncertain durations due to vendor data gaps. Which method fits best?

A: Governing principle: match uncertainty representation to decision needs. Applied here: PERT keeps the same precedence structure while explicitly modeling duration spread. Distractor trap: CPM veterans know the network well and forget that duration uncertainty, not logic, is the problem.

Q: You're reviewing spares policy and type "EOQ back of envelope carrying ordering cost estimate". Annual demand is 1,200 units, ordering cost about $300/order, carrying cost roughly $20/unit-year. What's the EOQ scale?

A: Governing principle: EOQ balances ordering and holding via a square root. Applied here: sqrt(2×1200×300 / 20) lands just under 200, even with coarse inputs. Distractor trap: engineers linearize the costs mentally and miss how weakly EOQ reacts to estimation error.