# Optimal vehicle speed scheduling#

A vehicle (say, an airplane) travels along a fixed path of $$n$$ segments, between $$n+1$$ waypoints labeled $$0, \ldots, n$$. Segment $$i$$ starts at waypoint $$i-1$$ and terminates at waypoint $$i$$. The vehicle starts at time $$t=0$$ at waypoint $$0$$. It travels over each segment at a constant (nonnegative) speed; $$s_i$$ is the speed on segment $$i$$. We have lower and upper limits on the speeds: $$s^\mathrm{min} \preceq s \preceq s^\mathrm{max}$$. The vehicle does not stop at the waypoints; it simply proceeds to the next segment. The travel distance of segment $$i$$ is $$d_i$$ (which is positive), so the travel time over segment $$i$$ is $$d_i/s_i$$. We let $$\tau_i$$, $$i = 1, \ldots, n$$, denote the time at which the vehicle arrives at waypoint $$i$$. The vehicle is required to arrive at waypoint $$i$$, for $$i=1, \ldots, n$$, between times $$\tau_i^\mathrm{min}$$ and $$\tau_i^\mathrm{max}$$, which are given. The vehicle consumes fuel over segment $$i$$ at a rate that depends on its speed, $$\Phi(s_i)$$, where $$\Phi$$ is positive, increasing, and convex, and has units of kg/s.

You are given the data $$d$$ (segment travel distances), $$s^\mathrm{min}$$ and $$s^\mathrm{max}$$ (speed bounds), $$\tau^\mathrm{min}$$ and $$\tau^\mathrm{max}$$ (waypoint arrival time bounds), and the fuel use function $$\Phi: \mathbf{R} \rightarrow \mathbf{R}$$. You are to choose the speeds $$s_1, \ldots, s_n$$ so as to minimize the total fuel consumed in kg.

## (a)#

Assume that the fuel use function is given by $$\Phi(s_i) = a s_i^2 + b s_i + c$$. Show how to pose this as a convex optimization problem. If you introduce new variables, or change variables, you must explain how to recover the optimal speeds from the solution of your problem. If convexity of the objective or any constraint function in your formulation is not obvious, explain why it is convex.

## (b)#

Carry out the method of part (a) on the problem instance with data below (the parameters $$a$$, $$b$$, and $$c$$ for $$\Phi$$ are defined in the data file). What is the optimal fuel consumption? Plot the optimal speed versus segment.

# data for vehicle speed scheduling problem.
# contains quantities: n, a, b, c, d, smin, smax, tau_min, tau_max
import numpy as np

n = 100
a = 1
b = 6
c = 10
d = np.array(
[
1.9501,
1.2311,
1.6068,
1.4860,
1.8913,
1.7621,
1.4565,
1.0185,
1.8214,
1.4447,
1.6154,
1.7919,
1.9218,
1.7382,
1.1763,
1.4057,
1.9355,
1.9169,
1.4103,
1.8936,
1.0579,
1.3529,
1.8132,
1.0099,
1.1389,
1.2028,
1.1987,
1.6038,
1.2722,
1.1988,
1.0153,
1.7468,
1.4451,
1.9318,
1.4660,
1.4186,
1.8462,
1.5252,
1.2026,
1.6721,
1.8381,
1.0196,
1.6813,
1.3795,
1.8318,
1.5028,
1.7095,
1.4289,
1.3046,
1.1897,
1.1934,
1.6822,
1.3028,
1.5417,
1.1509,
1.6979,
1.3784,
1.8600,
1.8537,
1.5936,
1.4966,
1.8998,
1.8216,
1.6449,
1.8180,
1.6602,
1.3420,
1.2897,
1.3412,
1.5341,
1.7271,
1.3093,
1.8385,
1.5681,
1.3704,
1.7027,
1.5466,
1.4449,
1.6946,
1.6213,
1.7948,
1.9568,
1.5226,
1.8801,
1.1730,
1.9797,
1.2714,
1.2523,
1.8757,
1.7373,
1.1365,
1.0118,
1.8939,
1.1991,
1.2987,
1.6614,
1.2844,
1.4692,
1.0648,
1.9883,
]
)

smin = np.array(
[
0.7828,
0.6235,
0.7155,
0.5340,
0.6329,
0.4259,
0.7798,
0.9604,
0.7298,
0.8405,
0.4091,
0.5798,
0.9833,
0.8808,
0.6611,
0.7678,
0.9942,
0.2592,
0.8029,
0.2503,
0.6154,
0.5050,
1.0744,
0.2150,
0.9680,
1.1708,
1.1901,
0.9889,
0.6387,
0.6983,
0.4140,
0.8435,
0.5200,
1.1601,
0.9266,
0.6120,
0.9446,
0.4679,
0.6399,
1.1334,
0.8833,
0.4126,
1.0392,
0.8288,
0.3338,
0.4071,
0.8072,
0.8299,
0.5705,
0.7751,
0.6514,
0.2439,
0.2272,
0.5127,
0.2129,
0.5840,
0.8831,
0.2928,
0.2353,
0.8124,
0.8085,
0.2158,
0.2164,
0.3901,
0.7869,
0.2576,
0.5676,
0.8315,
0.9176,
0.8927,
0.2841,
0.6544,
0.6418,
0.5533,
0.3536,
0.8756,
0.8992,
0.9275,
0.6784,
0.7548,
0.3210,
0.6508,
0.9159,
1.0928,
0.4731,
0.4548,
1.0656,
0.4324,
1.0049,
1.1084,
0.4319,
0.4393,
0.2498,
0.2784,
0.8408,
0.3909,
1.0439,
0.3739,
0.3708,
1.1943,
]
)

smax = np.array(
[
1.9624,
1.6036,
1.6439,
1.5641,
1.7194,
1.9090,
1.3193,
1.3366,
1.9470,
2.8803,
2.5775,
1.4087,
1.6039,
2.9266,
1.4369,
2.3595,
3.2280,
1.8890,
2.8436,
0.5701,
1.1894,
2.4425,
2.2347,
2.2957,
2.7378,
2.8455,
2.1823,
1.6209,
1.2499,
1.3805,
1.5589,
2.8554,
1.8005,
3.0920,
2.1482,
1.8267,
2.1459,
1.5924,
2.7431,
1.4445,
1.7781,
0.8109,
2.7256,
2.4290,
2.5997,
1.8125,
1.9073,
1.5275,
2.1209,
2.5419,
1.7032,
0.5636,
1.3669,
2.3200,
2.1006,
2.7239,
2.8726,
1.3283,
1.7769,
2.5750,
1.4963,
2.3254,
1.6548,
1.9537,
1.5557,
1.6551,
2.7307,
1.8018,
2.5287,
1.9765,
1.8387,
2.3525,
1.7362,
1.6805,
1.9640,
2.8508,
1.9424,
2.0780,
2.1677,
2.1863,
2.0541,
1.9734,
2.7687,
2.3715,
1.1449,
2.1560,
3.3310,
2.3456,
2.7120,
2.3783,
0.9611,
2.0690,
1.2805,
0.8585,
2.2744,
2.3369,
2.6918,
2.6728,
2.5941,
1.6120,
]
)

tau_min = np.array(
[
1.0809,
2.7265,
3.5118,
5.3038,
5.4516,
7.1648,
9.2674,
12.1543,
14.4058,
16.6258,
17.9214,
19.8242,
22.2333,
22.4849,
25.3213,
28.0691,
29.8751,
30.6358,
33.2561,
34.7963,
36.9943,
38.2610,
41.1451,
41.3613,
43.0215,
43.8974,
46.4713,
47.4786,
49.5192,
49.6795,
50.7495,
52.2444,
53.5477,
55.2351,
57.0850,
57.4250,
60.1198,
62.3834,
64.7568,
67.2016,
69.2116,
69.8143,
70.6335,
72.5122,
74.1228,
74.3013,
74.5682,
75.3821,
76.6093,
78.0315,
80.7584,
82.5472,
83.5340,
84.9686,
86.7601,
87.2445,
89.7329,
92.6013,
94.3879,
94.4742,
96.9105,
98.7409,
100.8453,
101.1219,
102.3966,
103.5233,
104.0218,
106.5212,
109.0372,
110.3920,
113.2618,
113.7033,
116.3131,
118.6214,
119.9539,
121.8157,
124.6708,
126.5908,
127.3328,
128.3909,
128.9545,
130.4264,
131.6542,
133.0448,
134.8776,
135.0912,
136.0340,
137.8591,
138.3842,
140.2473,
140.9852,
142.7472,
144.2654,
145.6597,
147.2840,
150.1110,
151.1363,
152.3417,
153.2647,
154.4994,
]
)

tau_max = np.array(
[
4.6528,
6.5147,
7.5178,
9.7478,
9.0641,
10.3891,
13.1540,
16.0878,
17.4352,
20.9539,
22.3695,
23.3875,
25.7569,
26.9019,
29.8890,
33.0415,
33.8218,
35.4414,
37.1583,
39.4054,
41.6520,
41.5935,
44.9329,
45.4028,
47.4577,
48.0358,
50.3929,
51.3692,
52.6947,
53.5665,
54.4821,
55.8495,
58.2514,
59.7541,
61.9845,
61.5409,
63.1482,
66.5758,
69.3892,
72.1558,
72.6555,
74.2216,
74.6777,
77.3780,
78.5495,
77.7574,
78.4675,
78.7265,
81.5470,
81.7429,
83.8565,
87.0579,
88.3237,
88.5409,
90.2625,
92.1100,
92.9949,
97.4829,
98.7916,
99.1695,
100.3291,
102.6510,
104.0075,
105.8242,
106.5207,
107.1619,
107.7716,
111.2568,
112.7815,
113.5394,
116.6615,
116.8022,
120.4465,
121.8652,
123.9981,
125.0498,
129.2106,
130.3409,
131.9796,
131.4842,
133.1503,
135.3247,
135.2318,
137.8225,
138.0808,
138.2218,
139.5026,
142.7253,
141.5105,
143.7757,
145.9842,
146.1712,
148.2622,
149.2407,
151.6295,
155.0270,
155.6694,
156.6739,
156.5266,
157.6903,
]
)

import cvxpy as cp


import matplotlib.pyplot as plt
plt.xlabel("$i$")
plt.ylabel("$s_i$")