A Flexible Job Shop Scheduling Problem Involving Reconfigurable Machine Tools Under Industry 5.0

TL;DR

This paper develops a comprehensive optimization approach for a complex, Industry 5.0-inspired flexible job shop scheduling problem involving reconfigurable machines, human factors, and energy efficiency, using a mixed-integer programming model and a memetic algorithm.

Contribution

It introduces a novel integrated model and a tailored memetic algorithm to solve the FJSSP with reconfigurable tools, worker considerations, and energy minimization, addressing a gap in current scheduling research.

Findings

Memetic algorithm outperforms MIP and GA on large instances.

Proposed model effectively balances machine reconfiguration, human factors, and energy use.

MA achieves high-quality solutions with improved computational efficiency.

Abstract

The rise of Industry 5.0 has introduced new demands for manufacturing companies, requiring a shift in how production schedules are managed to address human centered, environmental, and economic goals comprehensively. The flexible job shop scheduling problem (FJSSP), which involves processing operations on various capable machines, accurately reflects the complexities of modern manufacturing settings. This paper investigates the FJSSP involving reconfigurable machine tools with configuration dependent setup times, while integrating human aspects like worker assignments, moving time, and rest periods, as well as minimizing total energy consumption. A mixed-integer programming (MIP) model is developed to simultaneously optimize these objectives. The model determines the assignment of operations to machines, workers, and configurations while sequencing operations, scheduling worker movements, and respecting rest periods, and minimizing overall energy consumption. Given the NPhard nature of the FJSSP with worker assignments and reconfigurable tools, a memetic algorithm (MA) is proposed. This metaheuristic evolutionary algorithm features a three layer chromosome encoding method, specialized crossover and mutation strategies, and neighborhood search mechanisms to enhance solution quality and diversity. Comparisons of MA with MIP and genetic algorithms (GA) on benchmark instances demonstrate the MA efficiency and effectiveness, particularly for larger problem instances where MIP becomes impractical. This research paves the way for sustainable and resilient production schedules tailored for the factory of the future under the Industry 5.0 paradigm. The work bridges a crucial gap in current literature by integrating worker and environmental impact into the FJSSP with reconfigurable machine models.