Methods for hybrid flow shop scheduling and a case study in an aerospace company

Thesis Type: Postgraduate

Institution Of The Thesis: Middle East Technical University, Faculty of Engineering, Department of Industrial Engineering, Turkey

Approval Date: 2019

Student: Yiğitalp Özmen



In this study, we address the scheduling problem in Hybrid Flow Shop (HFS) with makespan objective. Since this problem is known to be NP-hard and an HFS is a common environment in real-life manufacturing systems, several approximate solution approaches have been proposed in the literature. Hence, we resort to some of these such as MILP model, dispatching rules, Palmer, CDS, NEH, and Bottleneck Heuristic. Due to the complexity of HFS scheduling problem, MILP model provides only a near optimal solution by using CPLEX for the real problem which we are inspired by the scheduling problem in the manufacturing of fuselage panels at an aerospace company as a case study whose current hybrid job shop is converted to an HFS by discrete event simulation to improve the output quality, lessen materials handling and shorten the manufacturing lead time. The job sequences of these approaches are simulated to compute makespan values of HFS scheduling problem. Moreover, we propose a Constraint Programming (CP) model for solving HFS scheduling problem to optimality for the real problem and test problems. We also propose a Hybrid Algorithm (HA) and a Galactic Swarm Optimization (GSO) in order not to be stuck in local optima for most of the test problems and to solve the real problem for optimality within an acceptable computational time. While HA and GSO seem to be promising for solving most of the test problems to optimality, the CP model outperforms the other approaches in the literature by solving all of them to optimality.