Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic

This paper, for the first time, presents a production scheduling model for a production line considering physical distancing between the machines' workforces. The production environment is an unrelated parallel-machine, in which for producing each part, different machines with different product...

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Veröffentlicht in:	Operations management research Jg. 15; H. 1-2; S. 503 - 527
Hauptverfasser:	Bazargan-Lari, Mohammad Reza, Taghipour, Sharareh, Zaretalab, Arash, Sharifi, Mani
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2022 Springer Springer Nature B.V
Schlagworte:	Algorithms Business and Management COVID-19 Engineering Economics Epidemics Genetic algorithms Heuristic methods Industrial and Production Engineering Industrial production Injection molding Innovation/Technology Management Integer programming Linear programming Logistics Management Marketing Mathematical models Mixed integer Operations Management Operations research Operations Research/Decision Theory Optimization Organization Production lines Production planning Production scheduling Safety stocks Scheduling Social distancing Sorting algorithms COVID-19 pandemic NSGA-II Production planning-scheduling Plastic injection mold Parallel machines Physical distancing NRGA
ISSN:	1936-9735, 1936-9743
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Abstract	This paper, for the first time, presents a production scheduling model for a production line considering physical distancing between the machines' workforces. The production environment is an unrelated parallel-machine, in which for producing each part, different machines with different production rates and the required number of workers are available. We propose a three-objective mixed-integer linear programming mathematical model that aims to maximize the manufacturer's total benefit, parts' safety stock (SS) index, and the workforce's physical distance over a finite horizon (one year) by determining the optimal scheduling of the parts on the machines. Since a large production scheduling problem belongs to the Np-Hard category of problems, a non-dominated sorting genetic algorithm, and a non-dominated ranked GA algorithm are developed to solve the presented model in two stages using the empirical data from a Canadian plastic injection mold company. In the first stage, the LP-metrics approach is utilized for validating the meta-heuristics on a reduced-size problem. In the second stage, the validated meta-heuristics are utilized to optimize the company's yearly production schedule. The results indicate both metaheuristics are performing well in determining the optimal solution. Moreover, implementing physical distancing in the company reduces the company's monthly net benefit by around 9.56% compared to the normal operational conditions (without considering physical distancing).
AbstractList	This paper, for the first time, presents a production scheduling model for a production line considering physical distancing between the machines' workforces. The production environment is an unrelated parallel-machine, in which for producing each part, different machines with different production rates and the required number of workers are available. We propose a three-objective mixed-integer linear programming mathematical model that aims to maximize the manufacturer's total benefit, parts' safety stock (SS) index, and the workforce's physical distance over a finite horizon (one year) by determining the optimal scheduling of the parts on the machines. Since a large production scheduling problem belongs to the Np-Hard category of problems, a non-dominated sorting genetic algorithm, and a non-dominated ranked GA algorithm are developed to solve the presented model in two stages using the empirical data from a Canadian plastic injection mold company. In the first stage, the LP-metrics approach is utilized for validating the meta-heuristics on a reduced-size problem. In the second stage, the validated meta-heuristics are utilized to optimize the company's yearly production schedule. The results indicate both metaheuristics are performing well in determining the optimal solution. Moreover, implementing physical distancing in the company reduces the company's monthly net benefit by around 9.56% compared to the normal operational conditions (without considering physical distancing).
Audience	Academic
Author	Bazargan-Lari, Mohammad Reza Zaretalab, Arash Taghipour, Sharareh Sharifi, Mani
Author_xml	– sequence: 1 givenname: Mohammad Reza orcidid: 0000-0002-0199-9667 surname: Bazargan-Lari fullname: Bazargan-Lari, Mohammad Reza organization: Department of Mechanical and Industrial Engineering, Ryerson University – sequence: 2 givenname: Sharareh orcidid: 0000-0003-3816-2462 surname: Taghipour fullname: Taghipour, Sharareh email: Sharareh@ryerson.ca organization: Department of Mechanical and Industrial Engineering, Ryerson University – sequence: 3 givenname: Arash orcidid: 0000-0002-2540-3274 surname: Zaretalab fullname: Zaretalab, Arash organization: Department of Industrial Engineering, Amirkabir University of Technology – sequence: 4 givenname: Mani orcidid: 0000-0002-7682-7077 surname: Sharifi fullname: Sharifi, Mani organization: Department of Mechanical and Industrial Engineering, Ryerson University
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Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. corrected publication 2022 COPYRIGHT 2022 Springer The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. corrected publication 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021, corrected publication 2022
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Keywords	COVID-19 pandemic NSGA-II Production planning-scheduling Plastic injection mold Parallel machines Physical distancing NRGA
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License	This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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SubjectTerms	Algorithms Business and Management COVID-19 Engineering Economics Epidemics Genetic algorithms Heuristic methods Industrial and Production Engineering Industrial production Injection molding Innovation/Technology Management Integer programming Linear programming Logistics Management Marketing Mathematical models Mixed integer Operations Management Operations research Operations Research/Decision Theory Optimization Organization Production lines Production planning Production scheduling Safety stocks Scheduling Social distancing Sorting algorithms
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Title	Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic
URI	https://link.springer.com/article/10.1007/s12063-021-00233-9 https://www.proquest.com/docview/2713129576 https://pubmed.ncbi.nlm.nih.gov/PMC9042670
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