Thesis Defense - Mücahit Kaan Kaleli (MSIE)

Mücahit Kaan Kaleli – M.Sc. Industrial Engineering

Asst. Prof. Zehra Melis Teksan – Advisor

Asst. Prof. Erinç Albey – Co-advisor

Date: 14.06.2021

Time: 10:00

Location: This meeting will be held ONLINE. Please send an e-mail to gizem.bakir@ozyegin.edu.tr in order to participate in this defense.

Nonidentical Parallel Machine Scheduling

With Time-Dependent Deterioration of Jobs

Thesis Committee:

Asst. Prof. Zehra Melis Teksan, Özyeğin University

Asst. Prof. Erinç Albey, Özyeğin University

Prof.  Z. Caner Taşkın, Boğaziçi University

Asst.  Prof.  Mehmet Önal, Özyeğin University

Asst.  Prof.  İhsan Yanıkoğlu, Özyeğin University

Abstract:

We consider the scheduling of deteriorating jobs on nonidentical parallel machines where the objective is to minimize mean flow time while ensuring the average value (i.e., quality) of processed jobs on machines exceeds a threshold. Deterioration is considered to be time-dependent and has a two-fold effect on jobs. The processing time of jobs, as well as their values, are deteriorating with time. We analyze the scheduling problem where the processing time and the value functions take linear (piece-wise linear) forms. We first formulate the problem as a mixed integer linear program and obtain optimal solutions for limited problem sizes. We then develop a heuristic algorithm to solve the problem and several ideas to sort jobs in the system. Linear and poisson regression models are trained to predict position of jobs on machines and also used in rank aggregation with simple sorting lists to provide better sorting approaches.

Bio: 

Mücahit Kaan Kaleli graduated from Izmir Nevvar Salih İşgören Anatolian High School in 2013. He received his B.S degree in Industrial Engineering from Özyeğin University in February 2019. After graduation, he joined the Master of Science program in Industrial Engineering at Özyeğin University and has been working under the supervision of Dr. Z. Melis Teksan and Dr. Erinç Albey. He has worked as a teaching assistant and assists Dr. Teksan in course of Productions Systems Analysis. His research focuses on optimization in scheduling problems, machine learning and rank aggregation methods.