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genetic-algorithm-for-job-shop-scheduling-and-nsga-ii's Introduction

Genetic-Algorithm-for-Job-Shop-Scheduling-and-NSGA-II

本主題主要介紹如何透過基因演算法 (Genetic Algorithm, GA) 與非凌越排序基因演算法 (Nondominated Sorting Genetic Algorithm II, NSGA-II) 來求解 Job Shop 排程問題。一開始會先進行 GA 及 NSGA-II 的概念介紹,最後再透過 Python 來進行實作並說明。

§ Introduction

更新時間 文章
2018 Genetic Algorithm (GA)
2018 Nondominated Sorting Genetic Algorithm II (NSGA-II)

§ Implementation with Python

更新時間 文章 連結
2018 GA For Flow Shop Documentation / Example / code / Folder
2018 GA For Job Shop Documentation / Example / code / Folder
2018 NSGA-II Documentation / Example / code / Folder

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genetic-algorithm-for-job-shop-scheduling-and-nsga-ii's Issues

"break" in selection section

Hi,

Thank you for your excellent work!
However, it seemed that there was a mistake in the selection section.

 if selection_rand[i]<=qk[0]:
            population_list[i][:]=total_chromosome[0][:]
            break  # <- the break was weird
        else:
            for j in range(0,population_size*2-1):
                if selection_rand[i]>qk[j] and selection_rand[i]<=qk[j+1]:
                    population_list[i][:]=total_chromosome[j+1][:]
                    break  # -> should be moved here?

JSP_dataset

Hi,
I am just wondering have you got JSP_dataset.xlsx for NSGA-II code.py ??

Thanks.

Lot Streamming

Hi Cheng-Man,

Did you consider demand parameter in this software and lot streaming optimization?

Thanks,
Duy Khai

The solution didn't respect Machine sequence constraints

Thank you for the excelent work.
Looks like the outcome didn't respect the machine sequence constraints because for job J3, J4 and J5 the last operation should have been O9. Instead the solution returned as their last operation Machine 5, Machine 6 and Machine 7 respectively.

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