Design of a Decision Support System for Solving the Personnel Rostering Problem for a Selected F&B Company

Traditional workforce scheduling methods in restaurants rely heavily on manual inputs, spreadsheets, and static planning routines. These approaches often fail to accommodate the volatility in customer demand and the frequent disruptions in workforce availability. The resulting inefficiencies are either overstaffing which leads to unnecessary labor costs, or understaffing, which compromises service quality, employee morale, and customer satisfaction.

The objective of this project is to design a rostering system that optimizes staff allocation across shifts while balancing operational requirements, employee well-being, and compliance with applicable labor laws and regulations.

This project will involve the following tasks:

Analyzing the restaurant operations to determine the staffing requirements for each shift, considering peak hours, recommended staff-to-customer ratios, and specialized staff needs.
Assessing the current rostering system, to identify inefficiencies, and constraints related to local and international labor regulations.
Forecasting staffing needs by hour, day, and week, accounting for seasonal and demand variations.
Developing an algorithm to construct optimal shift structures aligned with demand profiles.
Creating cost-efficient rosters that incorporate employee preferences, availability, and requests for leave, while minimizing overtime and ensuring a fair distribution of workload.
Designing a decision-support system that helps managers to generate and adjust rosters dynamically, accounting for unexpected events such as sick leaves or sudden surges in number of customers.
Exploring the use of AI to forecast staffing needs based on historical data and predictive analytics and to manage shifts in real-time.

Finally, a cost-benefit analysis should be conducted to evaluate the effectiveness of the proposed solution.

Project Details

Student(s):
Team 1: Majd Nasr, Abbass Twainy, Yehya Karameldine, Ali Bakri
Team 2 : Teya Chebli, Karim El Zarif, Lea El Hachem, Sarah Bou Saab
Advisor(s): Dr. Pierrette Zouein
Year: 2025-2026

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