IIM Lucknow

Overview

IIM Lucknow faced a challenge in creating efficient timetables after the finalization of course registrations. With students enrolling in multiple overlapping courses and limited faculty availability, managing clashes became increasingly difficult. The existing manual scheduling process led to inefficiencies, including scheduling conflicts between popular courses and dissatisfaction among students.

To solve these issues, the IIM Lucknow implemented an automated scheduling system that leveraged final course registration data and utilized advanced optimization techniques builtin the Registro Timetable Scheduling platform. Registro empowered administrators and students to view potential clashes, minimized conflicts by shuffling students between course sections, and optimized scheduling to reduce clashes overall.

This case study describes the problem, solution approach, implementation process, and the outcomes achieved with the new automated timetable scheduling system.

The Problem

As the student body grew and course offerings expanded, the school encountered several scheduling challenges:

1. Course Enrollment Conflicts:
   With multiple courses being offered, students often registered for overlapping classes, leading to clashes.
2. Lack of Visibility on Clashes:
   Administrators and students had limited tools to identify conflicts early, and addressing these conflicts manually became cumbersome.
3. Section Management Difficulties:
   Some courses were offered across multiple sections. However, students were not easily shuffled between sections to reduce conflicts, leading to overbooking in certain sections and under-utilization in others.
4. Inefficient Timetable Allocation:
   The manual scheduling process struggled to account for all constraints—such as minimizing student clashes while balancing classroom utilization and faculty preferences—leading to frustration among students and faculty.

Objectives

The primary goals of the new system were:

• Clash Visibility: Empower students and administrators to view potential clashes in advance.
• Conflict Reduction: Minimize student course clashes by shuffling students between sections.
• Advanced Optimization: Use optimization techniques to balance constraints such as faculty schedules, classroom availability, and course enrollments.
• Improved Student Experience: Ensure students could enroll in desired courses with minimal disruptions to their schedules.

Solution: Automated Timetable Scheduling System

The business school implemented an automated timetable scheduling solution that utilized final course registration data and integrated advanced optimization methods to reduce overall clashes. The system had the following key components:

1. Integration with Final Registration Data

The system was integrated with the school’s course registration platform to import real-time student enrollment data. This provided administrators with an accurate view of which students were enrolled in which courses, enabling dynamic conflict detection.

2. Clash Detection and Viewing

Students and administrators could view potential clashes using a visual dashboard.

• Color-coded conflict indicators highlighted overlapping courses.
• The dashboard allowed students to review their schedules and request changes.
• Administrators could see clash patterns across the entire student body, helping them make informed decisions.

3. Shuffling Students Between Sections

The system automatically analyzed enrollments for courses with multiple sections and suggested shuffling students to reduce conflicts.

• Example: If a student registered for two clashing courses but one course had multiple sections, the system could automatically reassign the student to a different section to avoid the conflict.
• The reassignments considered classroom capacities and faculty preferences to ensure smooth operations.

4. Advanced Optimization Engine

The system utilized constraint-based optimization algorithms to generate the best possible timetable, balancing several factors:

• Minimizing Student Clashes: The primary objective was to minimize overlaps between students' registered courses.
• Optimal Use of Classrooms: Assigning courses to rooms based on size and availability.
• Faculty Preferences: Respecting instructors’ preferred teaching schedules.
• Balancing Course Loads Across Time Slots: Avoiding congestion by evenly distributing courses across the day.

The optimization engine used techniques such as integer programming and greedy algorithms to explore multiple scheduling scenarios and suggest the most efficient timetable.

Implementation Process

Phase 1: Data Collection and System Setup

• The team integrated the new scheduling software with the school’s student registration platform.
• Administrators imported final course registration data to create a baseline schedule.

Phase 2: Testing and Clash Detection

• Test schedules were generated to identify common clashes, and the results were reviewed by faculty and administrators.
• The system’s shuffling mechanism was validated to ensure students were reassigned without causing overloading issues in any section.

Phase 3: Training and User Adoption

• Administrators were trained on how to use the dashboard to identify and resolve clashes.
• Students were given access to the system’s visual interface to view their schedules and identify any conflicts.

Phase 4: Rollout and Fine-tuning

• The new scheduling system was launched at the start of the academic term.
• As students finalized their registrations, the system automatically adjusted the timetable to accommodate changes, with minimal disruptions.

Results

The automated scheduling system brought several tangible benefits:

1. Significant Reduction in Student Clashes

• Over 90% of clashes were resolved automatically through the shuffling of students between course sections.
• The number of students reporting conflicts decreased by 60%, improving the overall student experience.

2. Improved Scheduling Efficiency

• The time required to finalize the timetable was reduced by 50%, as the system handled most of the conflict resolution and section assignments.

3. Better Use of Classrooms and Resources

• Courses were assigned to classrooms more efficiently, with classroom utilization improving by 20%.

4. Higher Faculty and Student Satisfaction

• Faculty schedules were respected, with most instructors receiving their preferred time slots.
• Students appreciated the transparency of the new system, which allowed them to view and resolve clashes proactively.

5. Flexibility for Late Changes

• The system’s dynamic adjustment capability enabled smooth handling of last-minute changes in course enrollments, with minimal disruptions to the overall timetable.

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