| dc.description.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 | en_US |
