Abstract:

 

Scheduling manufacturing processes efficiently is a key challenge in many supply chains across a diverse range of industries. Many such manufacturing processes can be modeled as so-called flow shops, in which a set of jobs undergoes sequential processing steps on a set of machines. While conventional flow shops assume that each job traverses the shop only once, many manufacturing processes require the jobs to loop through the shop multiple times before completion. Such a setting is called a flow shop with reentry, and it has numerous industrial applications, e.g., in semiconductor manufacturing. We consider various stylized models of ordered flow shops with reentry with several classical objective functions, such as the makespan, total completion time, or maximum lateness. We introduce new priority rules that are functions of the remaining loops of the jobs and show the conditions under which these rules are optimal.

 

Biography:

 

Nicklas is a postdoctoral researcher in operations research at the University of Bern. During his doctoral studies at the University of Bern, he was a visiting researcher in the Supply Chain Management Department at Rutgers University and in the Department of Technology, Operations, and Statistics at NYU Stern. Before pursuing his Ph.D., he completed a B.Sc. and M.Sc. in Business Mathematics at TU Dortmund University in Germany. His research focuses on developing models and solution approaches for challenging optimization problems in areas such as manufacturing, project management, and supply chain management, using mathematical programming, exact and heuristic algorithms, and machine learning techniques.

 

Everyone is welcome to attend the talk!

SEEM-5202 Website: http://seminar.se.cuhk.edu.hk