MPDPSD_UAV_healthcare

OR-Tools for Multi-depot Pickup and Delivery Problem with Soft Deadlines

简单记录一下现阶段的实验结果-20230523：

1. 参数设置： （车辆参数） vehicle_num_uav = 5 # 5 vehicle_num_human = 10 # 10 vehicle_num = vehicle_num_uav + vehicle_num_human w_uav = 1 # unitcost_uav per distance # 1 w_human = 50 # unitcost_human per distance # 10-400都可以 capacity_uav = 40 # 30 capacity_human = 100 # 100 speed_uav = 0.2 # speed按理说会影响time_matrix(node to node),但是现阶段的这个矩阵是一样的（设置了一个统一的speed = 0.2） speed_human = 0.1 （location参数）均匀分布 0-10 % if uniform(0,10) 前面的cost就不能设置的太大 我感觉可能是因为计算限制的问题 反正超过200就跑不出来了 loc_p_coor = torch.FloatTensor(size_order, 2).uniform_(0, 10) # P locations coordinates loc_d_coor = torch.FloatTensor(size_order, 2).uniform_(0, 10) # D locations coordinates

（订单参数） size_order = 30 # 因此len(TW)就是 15(车)+30(P)+30(D)=75 demand_p = random.choices(range(20, 41), k=size_order) # 有放回的choices 否则订单数大于20会报错 demand_d = [-x for x in demand_p] # negative demand in D set deadline_d = random.sample(range(40, 101), size_order) # deadlines in D set, otherwise infinity(99999)

3. 实验结果摘录（硬约束）： C:\CJW_2022\DRL_PDPTW_UAV\venu\Scripts\python.exe C:\CJW_2022\DRL_PDPTW_UAV\simpleVRP.py time_windows: 75 distance_matrix: [[0. 5.95191526 0. ... 3.96181941 2.15440035 0.97736061] [5.95191526 0. 5.95191526 ... 2.04630065 6.34482765 6.90714455] [0. 5.95191526 0. ... 3.96181941 2.15440035 0.97736061] ... [3.96181941 2.04630065 3.96181941 ... 0. 4.70486212 4.89719105] [2.15440035 6.34482765 2.15440035 ... 4.70486212 0. 2.5546174 ] [0.97736061 6.90714455 0.97736061 ... 4.89719105 2.5546174 0. ]] % P_D index表格： [[15 45] [16 46] [17 47] [18 48] [19 49] [20 50] [21 51] [22 52] [23 53] [24 54] [25 55] [26 56] [27 57] [28 58] [29 59] [30 60] [31 61] [32 62] [33 63] [34 64] [35 65] [36 66] [37 67] [38 68] [39 69] [40 70] [41 71] [42 72] [43 73] [44 74]] time_matrix: [[ 0 29 0 ... 19 10 4] [29 0 29 ... 10 31 34] [ 0 29 0 ... 19 10 4] ... [19 10 19 ... 0 23 24] [10 31 10 ... 23 0 12] [ 4 34 4 ... 24 12 0]] time_windows: [(0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 99999), (0, 83), (0, 90), (0, 69), (0, 83), (0, 52), (0, 88), (0, 78), (0, 84), (0, 109), (0, 82), (0, 58), (0, 105), (0, 62), (0, 81), (0, 59), (0, 72), (0, 107), (0, 78), (0, 62), (0, 84), (0, 76), (0, 62), (0, 102), (0, 78), (0, 87), (0, 62), (0, 107), (0, 91), (0, 99), (0, 91)] vehicle_cost_map: [1, 1, 1, 1, 1, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50] [40, 40, 40, 40, 40, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] Objective: 7397 % 我们的目标是优化总体的运行distance（当然每个车的distance是和unit_cost直接成倍数关系的，因此相当于优化总体的运送成本，而soft时间窗约束目前还没有加上） Route for vehicle 0: 0 Load(0) -> 29 Load(29) -> 59 Load(0) -> 34 Load(30) -> 64 Load(0) -> 0 Load(0) Distance of the route: 759m Load of the route: 0

Route for vehicle 1: 1 Load(0) -> 1 Load(0) Distance of the route: 0m Load of the route: 0

Route for vehicle 2: 2 Load(0) -> 2 Load(0) Distance of the route: 0m Load of the route: 0

Route for vehicle 3: 3 Load(0) -> 3 Load(0) Distance of the route: 0m Load of the route: 0

Route for vehicle 4: 4 Load(0) -> 42 Load(24) -> 72 Load(0) -> 33 Load(32) -> 63 Load(0) -> 4 Load(0) Distance of the route: 865m Load of the route: 0

Route for vehicle 5: 5 Load(0) -> 5 Load(0) Distance of the route: 0m Load of the route: 0

Route for vehicle 6: 6 Load(0) -> 41 Load(23) -> 17 Load(61) -> 71 Load(38) -> 27 Load(78) -> 47 Load(40) -> 57 Load(0) -> 26 Load(36) -> 56 Load(0) -> 6 Load(0) Distance of the route: 853m Load of the route: 0

Route for vehicle 7: 7 Load(0) -> 15 Load(25) -> 21 Load(54) -> 39 Load(82) -> 51 Load(53) -> 69 Load(25) -> 45 Load(0) -> 7 Load(0) Distance of the route: 769m Load of the route: 0

Route for vehicle 8: 8 Load(0) -> 8 Load(0) Distance of the route: 0m Load of the route: 0

Route for vehicle 9: 9 Load(0) -> 9 Load(0) Distance of the route: 0m Load of the route: 0

Route for vehicle 10: 10 Load(0) -> 10 Load(0) Distance of the route: 0m Load of the route: 0

Route for vehicle 11: 11 Load(0) -> 44 Load(33) -> 40 Load(72) -> 25 Load(99) -> 55 Load(72) -> 70 Load(33) -> 74 Load(0) -> 11 Load(0) Distance of the route: 1177m Load of the route: 0

Route for vehicle 12: 12 Load(0) -> 16 Load(29) -> 37 Load(68) -> 19 Load(100) -> 67 Load(61) -> 38 Load(81) -> 46 Load(52) -> 49 Load(20) -> 28 Load(53) -> 68 Load(33) -> 58 Load(0) -> 12 Load(0) Distance of the route: 837m Load of the route: 0

Route for vehicle 13: 13 Load(0) -> 23 Load(38) -> 22 Load(59) -> 35 Load(98) -> 53 Load(60) -> 52 Load(39) -> 20 Load(69) -> 36 Load(95) -> 65 Load(56) -> 43 Load(87) -> 66 Load(61) -> 50 Load(31) -> 73 Load(0) -> 13 Load(0) Distance of the route: 1000m Load of the route: 0

Route for vehicle 14: 14 Load(0) -> 24 Load(24) -> 32 Load(61) -> 30 Load(93) -> 62 Load(56) -> 31 Load(88) -> 60 Load(56) -> 18 Load(82) -> 54 Load(58) -> 48 Load(32) -> 61 Load(0) -> 14 Load(0) Distance of the route: 1137m Load of the route: 0

Total distance of all routes: 7397m Total load of all routes: 0

Route for vehicle 0: 0 Time(0,0) -> 29 Time(7,7) -> 59 Time(37,37) -> 34 Time(40,40) -> 64 Time(52,52) -> 0 Time(74,74) Time of the route: 74min

Route for vehicle 1: 1 Time(0,0) -> 1 Time(0,0) Time of the route: 0min

Route for vehicle 2: 2 Time(0,0) -> 2 Time(0,0) Time of the route: 0min

Route for vehicle 3: 3 Time(0,0) -> 3 Time(0,0) Time of the route: 0min

Route for vehicle 4: 4 Time(0,0) -> 42 Time(0,0) -> 72 Time(40,40) -> 33 Time(46,46) -> 63 Time(60,60) -> 4 Time(85,85) Time of the route: 85min

Route for vehicle 5: 5 Time(0,0) -> 5 Time(0,0) Time of the route: 0min

Route for vehicle 6: 6 Time(0,0) -> 41 Time(2,2) -> 17 Time(8,8) -> 71 Time(31,31) -> 27 Time(41,41) -> 47 Time(44,44) -> 57 Time(58,58) -> 26 Time(62,62) -> 56 Time(76,76) -> 6 Time(82,82) Time of the route: 82min

Route for vehicle 7: 7 Time(0,0) -> 15 Time(8,8) -> 21 Time(16,16) -> 39 Time(32,32) -> 51 Time(34,34) -> 69 Time(49,49) -> 45 Time(64,64) -> 7 Time(72,72) Time of the route: 72min

Route for vehicle 8: 8 Time(0,0) -> 8 Time(0,0) Time of the route: 0min

Route for vehicle 9: 9 Time(0,0) -> 9 Time(0,0) Time of the route: 0min

Route for vehicle 10: 10 Time(0,0) -> 10 Time(0,0) Time of the route: 0min

Route for vehicle 11: 11 Time(0,0) -> 44 Time(0,0) -> 40 Time(7,7) -> 25 Time(28,28) -> 55 Time(48,48) -> 70 Time(60,60) -> 74 Time(76,76) -> 11 Time(116,116) Time of the route: 116min

Route for vehicle 12: 12 Time(0,0) -> 16 Time(0,0) -> 37 Time(5,5) -> 19 Time(9,9) -> 67 Time(29,29) -> 38 Time(31,31) -> 46 Time(33,33) -> 49 Time(49,49) -> 28 Time(54,54) -> 68 Time(63,63) -> 58 Time(71,71) -> 12 Time(79,79) Time of the route: 79min

Route for vehicle 13: 13 Time(0,0) -> 23 Time(0,0) -> 22 Time(5,5) -> 35 Time(6,6) -> 53 Time(11,11) -> 52 Time(25,25) -> 20 Time(38,38) -> 36 Time(49,49) -> 65 Time(53,53) -> 43 Time(56,56) -> 66 Time(59,59) -> 50 Time(75,75) -> 73 Time(85,85) -> 13 Time(95,95) Time of the route: 95min

Route for vehicle 14: 14 Time(0,0) -> 24 Time(0,0) -> 32 Time(12,12) -> 30 Time(12,12) -> 62 Time(28,28) -> 31 Time(29,29) -> 60 Time(36,36) -> 18 Time(52,52) -> 54 Time(72,72) -> 48 Time(80,80) -> 61 Time(86,86) -> 14 Time(108,108) Time of the route: 108min

Total time of all routes: 711min

Process finished with exit code 0