job-shop-generator

A machine-agnostic fixed-length job-shop data generator for generating massive timelines.

https://github.com/hokyeejau/job-shop-generator

Science Score: 44.0%

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Repository

A machine-agnostic fixed-length job-shop data generator for generating massive timelines.

Basic Info

Host: GitHub
Owner: HokyeeJau
Language: Python
Default Branch: master
Size: 10.7 KB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0

Created over 2 years ago · Last pushed over 2 years ago

Metadata Files

Readme Citation

Machine-agnostic Fixed-length Job-Shop Data Generator

To deal with the limited datasets related to Job-Shop Scheduling Problem, a machine-agnostic job-shop data generator is developed.

This data generator considers several conditions as follows: - length of timeline - number of empty blocks - length of job-shop - times of repetition of timelines

Arguments of Generator

dataset_num: the number of datasets, each of which is generated based on independently sampled job-shops.
data_root: the root directory for saving generated data.
timeline_length: the number of job-shops in each timeline.
schedule_pool_size: the size of job-shop pool where sample timeline_length job-shops.
timeline_pool_size: the size of timeline pool.
empty_space_maxima: the maximal number of empty spaces in each timeline.
timeline_repeat_time: the times of repetition of timelines.
timeline_maxima: the total time span of each timeline.
schedule_digit_num: the number of digit of each job-shop. Each job-shop is accurate to two decimal places.

Pseudo code of Workflow

```python data_set = list()

for datasetidx in range(datasetnum): for emptylength in range(emptyspacemaxima): schedulepool = generateschedulepool(schedulepoolsize, scheduledigitnum) timelinepool = generatetimelinepool(schedulepool, timelinelength, timelinemaxima)

    for timeline in timeline_pool:
        for rep_idx in range(timeline_repeat_time):

            empty_indexes = sample_index_from_timeline(timeline, empty_length)
            empty_label = generate_one_hot_label_from(empty_indexes, timeline)
            vacated_timeline = vacate_timeline_with_empty_label(empty_label, timeline)
            _data_rows = fill_timeline_with_each_job_shop(vacated_timeline, timeline_pool)
            data_set += _data_rows

```

Requirements

scikit-learn
numpy

Usage

bash python main.py --timeline_length 10 --empty_space_maxima 1

Owner

Login: HokyeeJau
Kind: user

Repositories: 2
Profile: https://github.com/HokyeeJau

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "Machine-agnostic Fixed-length Job-Shop Data Generator"
authors:
- family-names: "Zhou"
  given-names: "Xueyi"
  orcid: "https://orcid.org/0000-0003-0703-2446"

version: 1.0.0
date-released: "2023-10-28"
license: Apache-2.0
url: "https://github.com/HokyeeJau/job-shop-generator"

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