Hands-On Session on ADA

Subject: ADA(BCS401)

Title: Hands-On Session on ADA

Date: 23 /03/2026 to 21/04/2026

Faculty In-Charge: Dr. Kishore G R

Venue: ISE seminar hall

Introduction

The subject Analysis and Design of Algorithms (ADA) aims to equip students with the ability to solve complex real-world problems using efficient algorithmic techniques. As part of the curriculum, students were engaged in hands-on learning activities focused on designing and implementing algorithms for real-life applications.

This approach enhanced conceptual understanding and strengthened practical problem-solving skills through active learning and application-based tasks.

Algorithms Designed for Real-World Problems

1. Shortest Path for Delivery Optimization

Problem Statement:
To design an efficient route for a delivery agent to minimize travel time and fuel consumption.

Algorithm Used: Dijkstra’s Algorithm

Steps:

1. Initialize the source node with distance 0 and all other nodes with infinity.
2. Use a priority queue to select the node with the minimum distance.
3. Update the distances of adjacent nodes if a shorter path is found.
4. Repeat the process until all nodes are processed.

Output: Optimal delivery route with minimum cost.

2. Job Scheduling for Maximum Profit

Problem Statement:
To schedule tasks with deadlines in order to maximize profit in a freelancing platform.

Algorithm Used: Greedy Job Scheduling

Steps:

1. Sort jobs in descending order of profit.
2. Assign each job to the latest available time slot before its deadline.
3. Skip jobs if no valid slot is available.

Output: Optimal job schedule with maximum profit.

3. Dynamic Pricing for E-Commerce

Problem Statement:
To adjust product prices dynamically based on demand and inventory levels.

Algorithm Used: Dynamic Programming Approach

Steps:

1. Define states based on inventory levels and demand patterns.
2. Formulate a recursive relation to maximize revenue.
3. Apply memoization to store intermediate results and avoid recomputation.

Output: Optimal pricing strategy for maximizing revenue.

Outcomes

1. Improved Problem-Solving Skills: Students gained practical experience in applying algorithmic concepts to real-world scenarios.
2. Understanding of Efficiency: Enhanced understanding of time and space complexity analysis.
3. Collaboration and Innovation: Encouraged teamwork, analytical thinking, and innovative solution development.

Results and Impact

The implementation of hands-on learning activities resulted in the following improvements:

• 20–30% increase in student engagement and class participation.
• Improved average performance in internal assessments and algorithm-based exercises.
• Greater student confidence in designing and applying algorithms to novel problems.