Analysis and Design of Algorithms

Course: Analysis and Design of Algorithms

Course Code: BCS401

Academic Year: 2023 – 24

Introduction
The subject Analysis and Design of Algorithms focuses on equipping students with the ability to solve complex real-world problems using efficient algorithms. As part of the curriculum, students were introduced to various
problem-solving techniques and encouraged to design algorithms addressing real-world challenges. This approach not only enhanced their theoretical understanding but also developed their practical problem-solving skills.

Algorithms Designed for Real-World Problems
1. Shortest Path for Delivery Optimization Problem: Designing an efficient route for a delivery agent to minimize
travel time and fuel consumption. Algorithm: Dijkstra’s Algorithm

o Input: Graph representation of delivery locations, distances between locations.
o Steps:
1. Initialize the source node with a distance of 0 and all others with infinity.
2. Use a priority queue to explore the shortest distance to each node.
3. Update the distance of neighboring nodes if a shorter path is found.
4. Repeat until all nodes are visited.
o Output: Optimal route with minimal cost.

2. Job Scheduling for Maximum Profit Problem: Scheduling tasks with deadlines to maximize profit for a
freelancing platform. Algorithm: Greedy Job Scheduling
o Input: Array of jobs with deadlines and profits.
o Steps:
1. Sort jobs in descending order of profit.
2. Iterate through the jobs, assigning each to the latest available time slot before its deadline.
3. Skip jobs if no time slot is available.
o Output: Maximum profit and the schedule of jobs.
3. Dynamic Pricing for E-commerce
Problem: Adjusting the price of products dynamically based on demand and inventory levels. Algorithm: Dynamic Programming for Price Adjustment
o Input: Historical demand, inventory levels, and competitor pricing.
o Steps:
1. Define a state as the remaining inventory and current demand.
2. Formulate a recursive relation to maximize revenue by adjusting prices based on predicted demand.
3. Use memoization to store intermediate results.
o Output: Optimal price for each product at a given time.

Outcomes
1. Practical Problem-Solving Skills: Students gained hands-on experience in applying algorithm design techniques to real-world scenarios.
2. Understanding of Algorithmic Efficiency: Improved understanding of time and space complexity and their impact on real-world applications.
3. Collaboration and Innovation: Encouraged teamwork and creative thinking while solving complex challenges.