GitHub - JDGiacomantonio-98/jobShop-scheduling-simulatedAnniealing: This repo encapsulates a Python implementation of the Simulated Annealing Algorithm to solve by means of a "minimum energy state" heuristic the NP-hard n-machines|no preemption|C_max job shop scheduling problem, considering n=2 machines and jobs having release dates. The code was designed and wrote by me. The whole heuristic design, complexity analysis, optimization, and ideas were made possible by team-working with Arianna Montironi and Chiara Panetta. The developed heuristic is the final deliverable of our project work held during the Quantitative Methods for Decision Aid 2021 Class in Politecnico di Torino.

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This repo encapsulates a Python implementation of the Simulated Annealing Algorithm to solve by means of a "minimum energy state" heuristic the NP-hard n-machines|no preemption|C_max job shop scheduling problem, considering n=2 machines and jobs having release dates. The code was designed and wrote by me. The whole heuristic design, complexity a…

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graphs		graphs
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LICENSE		LICENSE
benchmark.py		benchmark.py
complexity-analysis.txt		complexity-analysis.txt
main.py		main.py
models.py		models.py
requirements.txt		requirements.txt
search.py		search.py

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MIT License

Copyright (c) 2021 J. Di Giacomantonio

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