Unrelated Parallel Machine Scheduling Problem Considering Job Splitting, Inventories, Shortage, and Resource: A Meta-Heuristic Approach

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey...

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Bibliographic Details
Published in:Systems (Basel) 2024-01, Vol.12 (2), p.37
Main Authors: Arani, Mohammad, Momenitabar, Mohsen, Priyanka, Tazrin Jahan
Format: Article
Language:English
Subjects:
Algorithms
Completion time
Energy consumption
Genetic algorithms
Grey Wolf Optimizer
Heuristic
Heuristic methods
Heuristic programming
Integer programming
inventory
Linear programming
Literature reviews
Manufacturers
Manufacturing
Methods
Mixed integer
Optimization
Parallel processing
Production planning
Productivity
Renewable resources
resource constraints
Scheduling
Scheduling (Management)
shortage
Shortages
Splitting
Tests, problems and exercises
unrelated parallel machine scheduling problem
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Summary:This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities.
ISSN:2079-8954
2079-8954
DOI:10.3390/systems12020037