-
Notifications
You must be signed in to change notification settings - Fork 26
/
forecast.jl
166 lines (135 loc) · 3.63 KB
/
forecast.jl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
using Random
using JuMP, BilevelJuMP
function bench_forecast(prods, samples, optimizer, mode, seed = 1234)
rng = Random.MersenneTwister(seed)
SampleSize = samples
Products = prods
Samples = 2:SampleSize
phi1 = 0.7 * ones(Products)
phi0 = 3 * ones(Products)
mu = 3.0 / (1.0 - 0.7)
demand = ones(SampleSize, Products)
for p in 1:Products
for t in Samples
demand[t, p] =
max(0.0, phi0[p] + phi1[p] * demand[t-1, p] + 0.2 * randn(rng))
end
end
p_s = 5 .+ 0.1 * collect(1:Products)
p_b = 4 .+ 0.1 * collect(1:Products)
p_r = 3 .+ 0.1 * collect(1:Products)
u = 8 * Products
ctrs = []
vars = []
# MOI.empty!(optimizer)
model = BilevelModel(optimizer; mode = mode)
try
JuMP.set_time_limit_sec(model, MAX_TIME)
catch e
@show e
@show "failed to set limit time"
end
# parameters
v = @variable(Upper(model), 0 <= φ0[1:Products] <= 10)
push!(vars, vec(v))
v = @variable(Upper(model), -1 <= φ1[1:Products] <= 1)
push!(vars, vec(v))
#=
lower model
=#
# buying quantity is the only
v = @variable(Lower(model), 0 <= q_b[p = 1:Products, t = Samples] <= u)
push!(vars, vec(v))
#
v = @variable(Lower(model), 0 <= q_s[p = 1:Products, t = Samples] <= u)
push!(vars, vec(v))
v = @variable(Lower(model), 0 <= q_r[p = 1:Products, t = Samples] <= u)
push!(vars, vec(v))
c = @constraint(
Lower(model),
[p = 1:Products, t = Samples],
q_s[p, t] + q_r[p, t] <= q_b[p, t]
)
push!(ctrs, vec(c))
c = @constraint(
Lower(model),
[p = 1:Products, t = Samples],
q_s[p, t] <= φ0[p] + φ1[p] * demand[t-1, p]
)
push!(ctrs, vec(c))
c = @constraint(
Lower(model),
[t = Samples],
sum(q_b[p, t] for p in 1:Products) <= u
)
push!(ctrs, vec(c))
@objective(
Lower(model),
Min,
sum(
p_b[p] * q_b[p, t] - p_s[p] * q_s[p, t] - p_r[p] * q_r[p, t] for
t in Samples, p in 1:Products
)
)
#=
upper model
=#
#
v = @variable(Upper(model), 0 <= q2_s[p = 1:Products, t = Samples] <= u)
push!(vars, vec(v))
v = @variable(Upper(model), 0 <= q2_r[p = 1:Products, t = Samples] <= u)
push!(vars, vec(v))
c = @constraint(
Upper(model),
[p = 1:Products, t = Samples],
q2_s[p, t] + q2_r[p, t] <= q_b[p, t]
)
push!(ctrs, vec(c))
c = @constraint(
Upper(model),
[p = 1:Products, t = Samples],
q2_s[p, t] <= demand[t, p]
)
push!(ctrs, vec(c))
@objective(
Upper(model),
Min,
sum(
p_b[p] * q_b[p, t] - p_s[p] * q2_s[p, t] - p_r[p] * q2_r[p, t] for
t in Samples, p in 1:Products
)
)
#=
Optimize
=#
optimize!(model)
#=
for v in vars
val = value(v) # >= 0
end
for c in ctrs
val = value(c) - normalized_rhs(c) # >= 0
end
=#
@show primal_st = primal_status(model)
@show term_st = termination_status(model) #in [MOI.OPTIMAL, MOI.LOCALLY_SOLVED, MOI.ALMOST_LOCALLY_SOLVED]
solve_t = solve_time(model)
build_t = BilevelJuMP.build_time(model)
obj_l = try
objective_value(Lower(model))
catch
NaN
end
obj_u = try
objective_value(Upper(model))
catch
NaN
end
gap = try
bound = objective_bound(Upper(model))
abs(obj_u - bound) / max(abs(bound), 1e-8)
catch
NaN
end
return primal_st, term_st, solve_t, build_t, obj_l, obj_u, gap
end