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Ship Routing Algorithms for Just-In-Time and Energy Efficient Voyages. By using a genetic algorithm we strive the lowest possible fuel consumption while at the same time keeping the scheduled deadlines. Two different specifications of the algorithm are available, one with a constant engine power, one with an over the route changeable engine power.

Jupyter Notebook 64.47% Python 0.67% HTML 34.86%
optimization genetic-algorithm cmems ship-routing pymoo skimage jupyter-notebook

cms_routing's Issues

An error raised in predictNeededTime()

Hi, while testing colab notebook (simpleAlgorithm.ipynb), I am getting ValueError: start points must all be within the costs array. After some extensive debugging, I found out that in _normalize_indices() function indices and shape condition resulted in None returned. Which, subsequently, lead to this error. Could you help what is wrong? Filename - _mcp.pyx

[question]:Parameters doubts

@PondiB @tnier01 @jsten07 @njakuschona

I would like to ask about what was said in this other issue #3 , the reason for these constants in "prediction.py " at advanced_scripts folder( line 188~197)

I would also like to know the reason for the fuelArray constants in "calculate_objectives.py " at advanced_scripts (line 6):

# Array of fuel cost per hour for the different engine powers [0.6, 0.7, 0.8]
fuelArray=[152*33200, 154*33200, 156*33200]

Thank you very much in advance, amazing work!

Can not download the TEST DATA

UN_CMEMS = input('Please input your CMEMS username: ')
PW_CMEMS = getpass('Please input your CMEMS password: ')
....
(ds_w, ds_p) = get_cmems(startTime, endTime, UN_CMEMS, PW_CMEMS)
....
path_date = date[0:4] + "/" + date[4:6]
url = 'nrt.cmems-du.eu'
path_w = 'Core/GLOBAL_ANALYSIS_FORECAST_WAV_001_027/global-analysis-forecast-wav-001-027/' + path_date
path_p = 'Core/GLOBAL_ANALYSIS_FORECAST_PHY_001_024/global-analysis-forecast-phy-001-024/' + path_date

with ftplib.FTP(url) as ftp:
    try:
...

When we download the data from FTP, it occurred an error !

FTP error: 530 Login incorrect.

So, could you give us your test data by Cloud Dirve ?
In our country, it's so hard to access the test data!

Thank you so much !
Thank you for your project !

some parameter in prediction.py

Hi, bro, I have some questions about some parameter in "prediction.py " at advanced_scripts folder.
From line 188~197:

timeGridE = SOG_E
constE = 70 / np.power(timeGridE, 3)
timeGridE80 = np.cbrt(80 / constE)
timeGridE60 = np.cbrt(60 / constE)
timeGridE = timeGridE[AOI[2]:AOI[3], AOI[0]:AOI[1]]
timeGridE80 = timeGridE80[AOI[2]:AOI[3], AOI[0]:AOI[1]]
timeGridE60 = timeGridE60[AOI[2]:AOI[3], AOI[0]:AOI[1]]
timeGridE = np.where(timeGridE < 0, 10000, (kmGridEW * 1000) / (timeGridE * 30.87))
timeGridE80 = np.where(timeGridE80 < 0, 10000, (kmGridEW * 1000) / (timeGridE80 * 30.87))
timeGridE60 = np.where(timeGridE60 < 0, 10000, (kmGridEW * 1000) / (timeGridE60 * 30.87))

Q1. What is 70 in "constE = 70 / np.power(timeGridE, 3) ", 80 in "timeGridE80 = np.cbrt(80 / constE)", and 60 in "timeGridE60 = np.cbrt(60 / constE)"?

Q2. What is 30.87 in "timeGridE = np.where(timeGridE < 0, 10000, (kmGridEW * 1000) / (timeGridE * 30.87))"?

Thanks a lot!

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