bonnkleiford
commented
1 year ago SEMI-CONDUCTOR PRODUCTION PROBLEM
Sorry about this. I think I took the wrong model (which should be unbounded). Attaching the new files for this.
Also, here is the SDDP.jl formulation for this problem:
using SDDP, GLPK, Test, Random, Distributions
using LinearAlgebra
# INPUT
T = 4
I = 10
J = 10
K = 2
S = 100
alpha_0 = [686, 784, 540, 641, 1073, 1388, 1727, 1469, 586, 515]
beta_0 = [174, 115, 92, 116, 93, 164, 190, 174, 190, 200]
c = [7,17,11,16,18,7,7,9,8,14]
d_0 = [607,943,732,1279,434,378,1964,430, 410, 525]
d_perturb = [0.0422902245, 0.0549456137, 0.0868569685, 0.0950609064,
0.0538731273, 0.0917075818, 0.0673065114, 0.0594680277, 0.0544299191,
0.0782010312]
beta_perturb = [0.0129739644, 0.063853852, 0.0925580104, 0.0766634092,
0.0953244752, 0.0563760149, 0.075759652, 0.0583249427, 0.0324810132,
0.0694020021]
alpha_perturb = [0.0638533975, 0.068050401, 0.0747693903, 0.0514849591,
0.0323470258, 0.0480910211, 0.0304004586, 0.0976094813, 0.0694752024,
0.0703992735, 0.0775236862]
a = rand(5:9, (I,J,K))
_generate(x_perturb, x_0, i) = round(Int, x_0[i] * rand(Normal(1,
x_perturb[i])))
support = map(1:T) do t
return map(1:S) do s
return (
D = map(j -> _generate(d_perturb, d_0, j), 1:J),
A = map(i -> _generate(alpha_perturb, alpha_0, i), 1:I),
B = map(j -> _generate(beta_perturb, beta_0, j), 1:J),
)
end
end
Tau = 1
model = SDDP.LinearPolicyGraph(
stages = T,
sense = :Min,
lower_bound = 0.0,
optimizer = GLPK.Optimizer
) do subproblem, t
# Define the state variable.
@variable(subproblem,
0 <= X[i = 1:I],
SDDP.State, initial_value = 0)
# Define the control variables.
@variables(subproblem, begin
0 <= u[j=1:J]
0 <= v[i=1:I, j=1:J, k=1:K]
0 <= w[j=1:J]
0 <= x[i=1:I]
alpha[i=1:I] == alpha_0[i]
beta[j=1:J] == beta_0[j]
demand[j=1:J] == d_0[j]
end)
@constraints(subproblem, begin
[i=1:I], X[i].out == X[i].in + x[i]
[i=1:I], sum(sum(a[i,j,k]*v[i,j,k] for k in 1:K) for j in 1:J) <=
c[i]*X[i].out
[j=1:J], w[j] + u[j] >= demand[j]
[j=1:J,k=1:K], sum(v[i,j,k] for i in 1:I) >= w[j]
end)
SDDP.parameterize(subproblem, support[t]) do ω
@stageobjective(subproblem, ω.A' * x + ω.B' * u)
JuMP.fix.(demand , ω.D)
end
endFISH SELLING
Expected bound is 1226.571923 and the simulated bound is 1244.454533.
On Tue, May 2, 2023 at 11:32 PM Oscar Dowson @.***> wrote:
Semiconductor_Production
@bonnkleiford https://github.com/bonnkleiford, the Semiconductor_Production problem includes this constraint:
{ "name":"", "type":"LEQ", "lhs":[ {"name":"v_000","stage":2,"coefficient":[7.0]}, {"name":"v_001","stage":2,"coefficient":[8.0]}, {"name":"v_010","stage":2,"coefficient":[8.0]}, {"name":"v_011","stage":2,"coefficient":[5.0]}, {"name":"v_020","stage":2,"coefficient":[9.0]}, {"name":"v_021","stage":2,"coefficient":[5.0]}, {"name":"v_030","stage":2,"coefficient":[9.0]}, {"name":"v_031","stage":2,"coefficient":[8.0]}, {"name":"v_040","stage":2,"coefficient":[9.0]}, {"name":"v_041","stage":2,"coefficient":[5.0]}, {"name":"v_050","stage":2,"coefficient":[9.0]}, {"name":"v_051","stage":2,"coefficient":[5.0]}, {"name":"v_060","stage":2,"coefficient":[6.0]}, {"name":"v_061","stage":2,"coefficient":[7.0]}, {"name":"v_070","stage":2,"coefficient":[6.0]}, {"name":"v_071","stage":2,"coefficient":[8.0]}, {"name":"v_080","stage":2,"coefficient":[6.0]}, {"name":"v_081","stage":2,"coefficient":[9.0]}, {"name":"v_090","stage":2,"coefficient":[8.0]}, {"name":"v_091","stage":2,"coefficient":[9.0]}, {"name":"x_0","stage":0,"coefficient":[-7.0]}, {"name":"x_0","stage":1,"coefficient":[-7.0]} ], "rhs":[0.0] }
It has variables from stage 0, 1, and 2. Am I to infer that the state variable is really a lag-two state? And so we need to create 2*N state variables? Fish_Selling
Also, what's the optimal bound for the fish problem? I find an optimal policy after a single iteration, which doesn't seem right...
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-- With much respect,
Bonn Kleiford D. Seranilla, MOST PhD in Operations and Supply Chain Management Luxembourg Center for Logistics and Supply Chain Management University of Luxembourg @. @.> @.***
odow
Semiconductor_Production@bonnkleiford, the
Semiconductor_Productionproblem includes this constraint:It has variables from stage 0, 1, and 2. Am I to infer that the state variable is really a lag-two state? And so we need to create 2*N state variables?
Fish_SellingAlso, what's the optimal bound for the fish problem? I find an optimal policy after a single iteration, which doesn't seem right...