Production Scheduling for Hybrid Flow Shop Systems with Heterogeneous Parallel Machines and Integrated Work-in-Progress Inventory

Abstract

To secure a larger market share in the dynamically evolving personalized market, enterprises must adopt more flexible production modes. One critical challenge in this context is the optimization of production scheduling within hybrid flow shop systems featuring heterogeneous parallel machines. In such systems, machines differ in capabilities, setup requirements, and processing speeds, and not all machines are qualified to process every job - adding complexity to scheduling decisions. This study proposes a multi-objective hybrid flow shop scheduling model that integrates both time and material flow considerations. The model is designed to minimize two key objectives: the minimum of the makespan and the  Work-in-Progress (WIP) inventory, which together influence overall system efficiency and responsiveness. By leveraging the strengths of traditional scheduling strategies, the proposed approach supports better planning and execution under increasing demand conditions. A comprehensive scheduling model incorporating time and cost constraints is developed, and numerical experiments are conducted to validate its effectiveness. The results demonstrate that the proposed model significantly improves production efficiency, reduces operational costs, and increases adaptability to market variations. Furthermore, the study provides actionable insights for decision-makers in complex manufacturing environments, offering a scalable framework for dynamic scheduling optimization. These findings contribute to advancing research in production scheduling and support practical applications in industries seeking to enhance competitiveness through agile and cost-effective operations.