Developing Approaches For Distributed Hybrid Flow Shop Scheduling Problem With Makespan Minimization

Abstract

The optimization of production processes and efficient scheduling have become crucial for companies seeking to enhance competitiveness in the challenging manufacturing landscape. In particular, the hybrid flow shop scheduling problem (HFSP) has gained attention as it offers the potential to improve efficiency and productivity in manufacturing systems. This research focuses on the distributed hybrid flow shop scheduling problem (DHFSP) with makespan minimization as the primary objective. The DHFSP involves allocating jobs to a set of factories with multiple stages and parallel machines. The main challenges lie in distributing work among machines at each step and determining the processing order for jobs on each machine. This study explores a basic variant of the DHFSP, considering identical factories. The research aims to develop approaches, including a mixed-integer linear programming (MIP) model, the Distributed Nawaz-Enscore-Ham (DNEH) algorithm, and the DIPAK heuristics, to effectively solve the DHFSP and minimize the makespan. The proposed methods will be evaluated through numerical analysis to assess their effectiveness. Additionally, this study addresses the complexities and challenges inherent in distributed manufacturing systems and aims to optimize task scheduling across multiple stages and machines. The findings and solutions derived from this research have practical implications for industries such as manufacturing, production, and project management, where efficient job scheduling can lead to reduced production time and effective resource allocation