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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Beschreibung
Zusammenfassung: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).
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1936-9735
1936-9743
DOI:10.1007/s12063-021-00233-9