Project Title: Optimizing a Developed Mobile App for Restaurant 
Workforce Scheduling Using Hybrid Heuristic Algorithm   
Student: Shengjie Hou
Course: MS in Computer Science   

Abstract: 
Workforce scheduling is a crucial task for managers. A reasonable timetable can improve 
worker satisfaction, and significantly enhance work efficiency. In the field of workforce 
scheduling, heuristic algorithms are preferred because they can provide nearly optimal solution 
within a reasonable time. This paper investigates how to optimize restaurant staff scheduling 
by improving existing mobile applications with a new hybrid heuristic algorithm. Due to the 
complexity of scheduling problems and the high demands for algorithmic efficiency, this paper 
introduces a new combination of algorithms: the Late Acceptance Hill-Climbing (LAHC) and 
Threshold Acceptance (TA) algorithms, which replace the original Adaptive Large 
Neighborhood Search (ALNS) and Simulated Annealing (SA) algorithms. The purpose of this 
improvement is to maintain or improve the quality of solutions while reducing runtime. This 
paper compares the performance of the new and original algorithms through comparative 
analysis on 18 test instances of varying sizes and complexities. The results show that the new 
algorithm combination not only maintains the quality of solutions but also significantly improve 
the running time, particularly in handling large datasets. Through systematic experiments, this 
paper confirms the practicability and effectiveness of the new algorithm combination in solving 
restaurant staff scheduling problems, indicating its potential advantages in real-world 
applications.