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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00377483" target="_blank" >RIV/68407700:21730/24:00377483 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-71645-4_17" target="_blank" >https://doi.org/10.1007/978-3-031-71645-4_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-71645-4_17" target="_blank" >10.1007/978-3-031-71645-4_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 NP-hard nature of the FJSSP with worker assignments and reconfigurable tools, a memetic algorithm (MA) is proposed. This meta-heuristic 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’s 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. IFIP International Federation for Information Processing 2024.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 NP-hard nature of the FJSSP with worker assignments and reconfigurable tools, a memetic algorithm (MA) is proposed. This meta-heuristic 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’s 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. IFIP International Federation for Information Processing 2024.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments (APMS 2024), 43rd IFIP WG 5.7 International Conference, APMS 2024, Chemnitz, Germany, September 8-12, 2024, Proceedings, Part VI

ISBN

978-3-031-71644-7

ISSN

1868-4238

e-ISSN

1868-422X

Počet stran výsledku

14

Strana od-do

244-257

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Chemnitz

Datum konání akce

8. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001356142100017