A comparison of three evolutionary algorithms for group scheduling in theme parks with multitype facilities

Area of Science:

• Operations Research
• Computational Intelligence
• Tourism Management

Background:

• Theme parks face challenges with long queues and reduced visitor satisfaction during peak seasons, especially for school groups.
• Simultaneous entry and exit requirements for student groups exacerbate waiting times for popular attractions.
• Current systems do not adequately address the specific needs of student groups with diverse facility preferences.

Purpose of the Study:

• To propose a novel Theme Park Problem with Multitype Facilities (TPP-MTF) tailored for student groups.
• To develop an optimized system for assigning groups to facilities, minimizing wait times and maximizing satisfaction.
• To create a win-win scenario for both theme parks and student groups through efficient resource allocation.

Main Methods:

• A new decoding approach for random permutation integer sequences was developed.
• The decoding approach was integrated into immune-based algorithms, genetic algorithms, and particle swarm optimization.
• The algorithms were applied to a real-world case study of a Taiwanese theme park.

Main Results:

• The proposed algorithms effectively solved the TPP-MTF problem, demonstrating significant reductions in group waiting times.
• Comparative analysis showed the effectiveness of the immune-based, genetic, and particle swarm optimization algorithms.
• The developed system facilitates advance ticket sales and visitor number estimation for theme parks.

Conclusions:

• The TPP-MTF model and proposed algorithms offer a viable solution for managing large student groups in theme parks.
• Optimized scheduling enhances visitor satisfaction and park operational efficiency.
• This approach provides a framework for improving theme park management and visitor experience, particularly for educational tours.