Date of Award


Publication Type


Degree Name



Industrial and Manufacturing Systems Engineering


Appointment Scheduling;Min-Max Optimization;Mixed Integer Linear Program;Multi-mode Model;Robust Optimization


Ahmed Azab


Fazle Baki



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


The appointment scheduling issue has been widely used in a variety of industries, including those that provide services, healthcare, finance, and legal advice. Uncertainty of processing time and job no-shows make the problem more challenging. The majority of the literature now in existence makes unrealistic assumptions about most real-world scenarios, such as constant service time, and they use a vast quantity of data to view the service time distribution or failure to account for work no-shows. In the research, we address this issue by building a robust appointment scheduling model that uses min-max optimization to generate appointment time for a multi-mode system while considering customer no-shows and uncertain service times. The objective is to minimize the total expected cost of the job waiting time and service provider's idling and overtime for the worst-case scenario under any realization of the processing time and no-shows of the jobs. The advantage of the suggested methodology is that distributional data about the uncertain service time is not required. Since it just needs to take into account the extreme boundaries of the uncertain parameters, it can provide the best solution with less knowledge about the uncertain parameters. This method can be used with any probability distribution of the uncertain parameters. We formulated a mixed integer linear programming model to solve the problem. We ran some experimental runs and checked the effect of the problem parameters on the end of the day, the job waiting time, server idle time, and the total overall cost. This work will contribute to the literature related to uncertainty handling, job no-shows in decision-making, and industries that aim to achieve an efficient service system.

Available for download on Monday, September 09, 2024