Giter VIP home page Giter VIP logo

blume's Introduction

blume

This distance_optimized.py contains the data, you can input the distance matrix by yourself acc to your need. Increasing the number of vehicle can also be done by changing data["vehicles_num"] matrix

Inputs

    data["no_of_depot"] = 3
    data["no_of_store"] = 22
    data["distance_matrix"] = [
                            # fmt: off
                            [0, 500, 500, 180, 130, 240, 210, 220, 170, 180, 700, 720, 750, 320, 340, 750, 740, 600, 650, 650, 500, 540, 560, 580, 600],
                            [500, 0, 500, 600, 650, 700, 620, 630, 640, 650, 220, 250, 260, 270, 230, 240, 250, 260, 250, 235, 500, 510, 520, 530, 540],
                            [500, 500, 0, 500, 520, 530, 540, 600, 620, 540, 530, 550, 560, 590, 600, 630, 650, 620, 610, 500, 200, 210, 220, 230, 240],
                            [180, 600, 500, 0, 20, 25, 30, 26, 35, 15, 500, 510, 520, 540, 550, 530, 560, 500, 570, 600, 580, 540, 570, 576, 578],
                            [130, 650, 520, 20, 0, 15, 18, 20, 25, 24, 500, 510, 520, 540, 550, 530, 560, 500, 570, 600, 580, 540, 570, 576, 578],
                            [240, 700, 530, 25, 15, 0, 20, 24, 23, 21, 500, 510, 520, 540, 550, 530, 560, 500, 570, 600, 580, 540, 570, 576, 578],
                            [210, 620, 540, 30, 18, 20, 0, 25, 23, 20, 500, 510, 520, 540, 550, 530, 560, 500, 570, 600, 580, 540, 570, 576, 578],
                            [220, 630, 600, 26, 20, 24, 25, 0, 15, 18, 500, 510, 520, 540, 550, 530, 560, 500, 570, 600, 580, 540, 570, 576, 578],
                            [170, 640, 620, 35, 25, 23, 23, 15, 0, 20, 500, 510, 520, 540, 550, 530, 560, 500, 570, 600, 580, 540, 570, 576, 578],
                            [180, 650, 540, 15, 24, 21, 20, 18, 20, 0, 30, 20, 15, 13, 15, 16, 18, 20, 21, 23, 500, 510, 520, 530, 540],
                            [700, 220, 530, 500, 500, 500, 500, 500, 500, 30, 0, 15, 18, 20, 13, 18, 29, 14, 16, 19, 500, 510, 520, 530, 540],
                            [720, 250, 550, 510, 510, 510, 510, 510, 510, 20, 15, 0, 20, 21, 14, 15, 18, 20, 15, 18, 500, 510, 520, 530, 540],
                            [750, 260, 560, 520, 520, 520, 520, 520, 520, 15, 18, 20, 0, 24, 15, 16, 18, 12, 19, 20, 500, 510, 520, 530, 540],
                            [320, 270, 590, 540, 540, 540, 540, 540, 540, 13, 20, 21, 24, 0, 15, 23, 12, 15, 16, 18, 500, 510, 520, 530, 540],
                            [340, 230, 600, 550, 550, 550, 550, 550, 550, 15, 13, 14, 15, 15, 0, 15, 18, 17, 20, 15, 500, 510, 520, 530, 540],
                            [750, 240, 630, 530, 530, 530, 530, 530, 530, 16, 18, 15, 16, 23, 15, 0, 17, 25, 23, 15, 500, 510, 520, 530, 540],
                            [740, 250, 650, 560, 560, 560, 560, 560, 560, 18, 29, 18, 18, 12, 18, 17, 0, 24, 15, 18, 500, 510, 520, 530, 540],
                            [600, 260, 620, 500, 500, 500, 500, 500, 500, 20, 14, 20, 12, 15, 17, 25, 24, 0, 16, 17, 500, 510, 520, 530, 540],
                            [650, 250, 610, 570, 570, 570, 570, 570, 570, 21, 16, 15, 19, 16, 20, 23, 15, 16, 0, 18, 500, 510, 520, 530, 540],
                            [650, 235, 500, 600, 600, 600, 600, 600, 600, 23, 19, 18, 20, 18, 15, 15, 18, 17, 18, 0, 500, 510, 520, 530, 540],
                            [500, 500, 200, 580, 580, 580, 580, 580, 580, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 0, 15, 25, 35, 5],
                            [540, 510, 210, 540, 540, 540, 540, 540, 540, 510, 510, 510, 510, 510, 510, 510, 510, 510, 510, 510, 15, 0, 35, 45, 50],
                            [560, 520, 220, 570, 570, 570, 570, 570, 570, 520, 520, 520, 520, 520, 520, 520, 520, 520, 520, 520, 25, 35, 0, 10, 15],
                            [580, 530, 230, 576, 576, 576, 576, 576, 576, 530, 530, 530, 530, 530, 530, 530, 530, 530, 530, 530, 35, 45, 10, 0, 16],
                            [600, 540, 240, 578, 578, 578, 578, 578, 578, 540, 540, 540, 540, 540, 540, 540, 540, 540, 540, 540, 5, 50, 15, 16, 0]
                            # fmt: on
    ]

    data["depots"] = [
        [4, 5, 6, 7, 8, 9, 10],
        [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
        [21, 22, 23, 24, 25]
    ]
    # [[0, 3, 4, 5, 6, 7, 8, 9],
    # [1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
    # [2, 20, 21, 22, 23, 24]]

    data["vehicles_num"] = [
        [5, 1],
        [3, 1],
        [5, 1]
    ]

    data["vehicle_specs"] = [
        # wgt   vol fixedCost varCost maxDist speed
        [61200, 2389, 1000, 100, 10000, 40],
        [61200, 2389, 2000, 60, 120, 45]
    ]

    data['depot_timings'] = ['10:00', '12:00']

    data["demands"] = [
        ['Demand1',  4,  5497, 322, '10:00', '12:00', '15:00', '19:00'],
        ['Demand2',  5,  6072, 460, '10:00', '12:00', '16:00', '19:00'],
        ['Demand3',  6, 11362, 345, '10:00', '12:00', '17:00', '19:00'],
        ['Demand4',  7,  9568, 552, '10:00', '12:00', '12:00', '19:00'],
        ['Demand5',  8,  7153, 391, '10:00', '12:00', '12:00', '19:00'],
        ['Demand6',  8,  5244, 437, '10:00', '12:00', '12:00', '19:00'],
        ['Demand7', 11,  6095, 483, '10:00', '12:00', '12:00', '19:00'],
        ['Demand8', 12,  6072, 253, '10:00', '12:00', '12:00', '19:00'],
        ['Demand9', 13, 10281, 460, '10:00', '12:00', '15:00', '19:00'],
        ['Demand10', 14, 11063, 391, '10:00', '12:00', '16:00', '19:00'],
        ['Demand11', 15,  5635, 322, '10:00', '12:00', '17:00', '19:00'],
        ['Demand12', 16,  9637, 437, '10:00', '12:00', '12:00', '19:00'],
        ['Demand13', 17,  6578, 322, '10:00', '12:00', '12:00', '19:00'],
        ['Demand14', 18,  8556, 253, '10:00', '12:00', '12:00', '19:00'],
        ['Demand15', 19,  5520, 230, '10:00', '12:00', '15:00', '19:00'],
        ['Demand16', 21,  5037, 575, '10:00', '12:00', '16:00', '19:00'],
        ['Demand17', 22,  8280, 368, '10:00', '12:00', '17:00', '19:00'],
        ['Demand18', 23, 11086, 506, '10:00', '12:00', '12:00', '19:00'],
        ['Demand19', 24, 10189, 414, '10:00', '12:00', '12:00', '19:00'],
        ['Demand20', 25,  8970, 322, '10:00', '12:00', '12:00', '19:00'],
        ['Demand21', 21, 11224, 529, '10:00', '12:00', '12:00', '19:00'],
        ['Demand22', 22, 10994, 552, '10:00', '12:00', '12:00', '19:00'],
        ['Demand23', 23,  7291, 414, '10:00', '12:00', '15:00', '19:00'],
        ['Demand24', 10, 11293, 552, '10:00', '12:00', '16:00', '19:00'],
        ['Demand25',  9,  5175, 299, '10:00', '12:00', '17:00', '19:00'],
        ['Demand26',  8,  8257, 299, '10:00', '12:00', '12:00', '19:00'],
        ['Demand27',  7,  5842, 230, '10:00', '12:00', '12:00', '19:00'],
        ['Demand28',  6, 10235, 345, '10:00', '12:00', '12:00', '19:00'],
        ['Demand29',  5,  9177, 414, '10:00', '12:00', '12:00', '19:00'],
        ['Demand30',  4,  5704, 345, '10:00', '12:00', '15:00', '19:00'],
        ['Demand31',  6,  5704, 414, '10:00', '12:00', '16:00', '19:00'],
        ['Demand32',  7,  6716, 368, '10:00', '12:00', '17:00', '19:00'],
        ['Demand33',  8,  9292, 562, '10:00', '12:00', '12:00', '19:00'],
        ['Demand34',  9,  4292, 562, '10:00', '12:00', '12:00', '19:00'],
        ['Demand35', 10,  5292, 563, '10:00', '12:00', '12:00', '19:00'],
        ['Demand36', 20,  6292, 564, '10:00', '12:00', '12:00', '19:00'],
        ['Demand37', 19,  6711, 562, '10:00', '12:00', '15:00', '19:00'],
        ['Demand38', 18, 11711, 563, '10:00', '12:00', '16:00', '19:00'],
        ['Demand39', 17,  6711, 564, '10:00', '12:00', '17:00', '19:00'],
        ['Demand40', 16,  6711, 565, '10:00', '12:00', '12:00', '19:00'],
        ['Demand41', 15,  6711, 566, '10:00', '12:00', '12:00', '19:00'],
        ['Demand42', 14,  6711, 567, '10:00', '12:00', '12:00', '19:00'],
        ['Demand43', 25,  6711, 568, '10:00', '12:00', '12:00', '19:00'],
        ['Demand44', 25,  6711, 569, '10:00', '12:00', '12:00', '19:00'],
        ['Demand45', 24,  6711, 570, '10:00', '12:00', '16:00', '19:00'],
        ['Demand46', 24,  6711, 571, '10:00', '12:00', '17:00', '19:00'],
        ['Demand47', 23,  6711, 572, '10:00', '12:00', '12:00', '19:00'],
        ['Demand48', 23,  6711, 573, '10:00', '12:00', '12:00', '19:00'],
        ['Demand49', 22,  6711, 574, '10:00', '12:00', '12:00', '19:00'],
        ['Demand50', 21,  6711, 575, '10:00', '12:00', '12:00', '19:00']]

Outputs

3000
6
Route for vehicle 0:
 0 Load(0) ->  18 Load(0) ->  17 Load(5292) ->  16 Load(9584) ->  15 Load(18876) ->  14 Load(25592) ->  13 Load(31296) ->  12 Load(37000) ->  11 Load(46177) ->  0 Load(56412)
Cost of the route: 153000 Ruppees

Route for vehicle 1:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Route for vehicle 2:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Route for vehicle 3:
 0 Load(0) ->  3 Load(0) ->  1 Load(11362) ->  0 Load(16859)
Cost of the route: 45500 Ruppees

Route for vehicle 4:
 0 Load(0) ->  10 Load(0) ->  9 Load(5842) ->  8 Load(14099) ->  7 Load(19343) ->  6 Load(30636) ->  5 Load(35811) ->  4 Load(42964) ->  2 Load(52532) ->  0 Load(58604)
Cost of the route: 88600 Ruppees

Route for vehicle 5:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Total Cost of all routes: 287100 Ruppees
2000
4
Route for vehicle 0:
 0 Load(0) ->  6 Load(0) ->  15 Load(9637) ->  14 Load(16348) ->  13 Load(23059) ->  12 Load(29770) ->  11 Load(41481) ->  10 Load(48192) ->  9 Load(54484) ->  0 Load(60004)
Cost of the route: 177600 Ruppees

Route for vehicle 1:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Route for vehicle 2:
 0 Load(0) ->  5 Load(0) ->  8 Load(5635) ->  1 Load(14191) ->  7 Load(20286) ->  4 Load(26864) ->  3 Load(37927) ->  2 Load(48208) ->  16 Load(54280) ->  0 Load(60991)
Cost of the route: 88200 Ruppees

Route for vehicle 3:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Total Cost of all routes: 265800 Ruppees
3000
6
Route for vehicle 0:
 0 Load(0) ->  16 Load(0) ->  15 Load(6711) ->  14 Load(13422) ->  13 Load(20133) ->  12 Load(26844) ->  11 Load(33555) ->  10 Load(40266) ->  9 Load(46977) ->  8 Load(53688) ->  0 Load(60979)
Cost of the route: 208000 Ruppees

Route for vehicle 1:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Route for vehicle 2:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Route for vehicle 3:
 0 Load(0) ->  1 Load(0) ->  0 Load(5037)
Cost of the route: 41000 Ruppees

Route for vehicle 4:
 0 Load(0) ->  7 Load(0) ->  6 Load(10994) ->  5 Load(22218) ->  4 Load(31188) ->  3 Load(41377) ->  2 Load(52463) ->  0 Load(60743)
Cost of the route: 68000 Ruppees

Route for vehicle 5:
 0 Load(0) ->  0 Load(0)
Cost of the route: 0 Ruppees

Total Cost of all routes: 317000 Ruppees

blume's People

Contributors

ayush0chaudhary avatar

Watchers

avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.