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运筹系列87：julia求解随机动态规划问题入门

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随机动态规划问题的特点是：

  1. 有多个阶段，每个阶段的随机性互不相关，且有有限个实现值 (finite realizations)
  2. 具有马尔可夫性质，即每个阶段只受上一个阶段影响，可以用状态转移方程来描述阶段与阶段之间的变化过程。

我们使用julia的SDDP算法包来求解随机动态规划问题。

1. 入门案例：LinearPolicyGraph

看一个简单的数值优化的例子：

我们将其建立为一个N阶段的问题：

初始值为M。
使用SDDP.jl进行求解：

using SDDP
import Ipopt

M, N = 5, 3

model = SDDP.LinearPolicyGraph(
    stages = N,
    lower_bound = 0.0,
    optimizer = Ipopt.Optimizer,
) do subproblem, node
    @variable(subproblem, s >= 0, SDDP.State, initial_value = M)
    @variable(subproblem, x >= 0)
    @stageobjective(subproblem, x^2)
    @constraint(subproblem, x <= s.in)
    @constraint(subproblem, s.out == s.in - x)
    if node == N
        fix(s.out, 0.0; force = true)
    end
    return
end

SDDP.train(model)
println(SDDP.calculate_bound(model))
simulations = SDDP.simulate(model, 1, [:x])
for data in simulations[1]
    println("x_$(data[:node_index]) = $(data[:x])")
end

结果为

8.333333297473812
x_1 = 1.6666655984419778
x_2 = 1.6666670256548375
x_3 = 1.6666673693365108

非常接近理论最优值。

2 报童模型：加入随机变量

报童每天早上进一批报纸，需求有个大致的数，但是并不确定。那么报童要进多少报纸合适？
变量如下：

  • 报纸成本c，零售价v
  • 残值s，缺货损失p
  • 需求为随机变量x，概率密度为f
  • 决策变量为订货量Q
  • 目标函数为max(利润-成本)，假如x>Q，则利润=vQ，成本=cQ+p(x-Q)
  • 最佳订货量Q满足到Q的概率积分=(v+p-c)/(v+p-s)，推倒过程这里忽略

.我们缺货无损失，残值为0，零售价30.5，订货价20.5，则最佳订货量满足积分=0.328。若f为均值300，方差50的正态分布函数，则最佳订货量 = -0.445*50+300 = 278
我们使用Julia进行求解，首先要将正态分布离散化：

using Distributions,StatsPlots
D = Distributions.Normal(300,50)
N = 1000
d = rand(D, N)
P = fill(1 / N, N)
StatsPlots.histogram(d; bins = 80, label = "", xlabel = "Demand")


接下来是一个trick，这里是先决策再实现随机变量，因此把问题建成两阶段的模型，第一阶段决策为购买量u_make，第二阶段实现随机变量ω，随后决策为销售量u_sell。
模型如下：

using SDDP, HiGHS
model = SDDP.LinearPolicyGraph(;
    stages = 2,
    sense = :Max,
    upper_bound = 20 * maximum(d),  # The `M` in θ <= M
    optimizer = HiGHS.Optimizer,
) do subproblem::JuMP.Model, stage::Int
    @variable(subproblem, x >= 0, SDDP.State, initial_value = 0)
    if stage == 1
        @variable(subproblem, u_make >= 0, Int)
        @constraint(subproblem, x.out == x.in + u_make)
        @stageobjective(subproblem, -20.5 * u_make)
    else
        @variable(subproblem, u_sell >= 0, Int)
        @constraint(subproblem, u_sell <= x.in)
        @constraint(subproblem, x.out == x.in - u_sell)
        # 把连续随机变量离散化
        SDDP.parameterize(subproblem, d, P) do ω
            set_upper_bound(u_sell, ω)
            return
        end
        @stageobjective(subproblem, 30.5 * u_sell)
    end
end
SDDP.train(model;)
rule = SDDP.DecisionRule(model; node = 1)
SDDP.evaluate(rule; incoming_state = Dict(:x => 0.0),controls_to_record = [:u_make])

输出为：
(stage_objective = -5699.0, outgoing_state = Dict(:x => 278.0), controls = Dict(:u_make => 278.0))
接下来进行仿真观察：

simulations = SDDP.simulate(
    model,
    10,  #= number of replications =#
    [:x, :u_make, :u_sell];  #= variables to record =#
    skip_undefined_variables = true,
);
simulations[1][1]


收益如下：

3. 水火力发电：另一个经典例子

52个星期，每个星期的用电需求和火力发电成本是确定的。
每个星期做3个决策：水电发电量；火电发电量；泄洪量
水力发电没有成本；水库容量320，初始位置300；
代码入下：

T = 52
model = Model(HiGHS.Optimizer)
set_silent(model)
# 状态变量
@variable(model, 0 <= x_storage[1:T+1] <= 320)
fix(x_storage[1], 300; force = true)

# 决策变量
@variable(model, 0 <= u_spill[1:T])      # 泄洪
@variable(model, 0 <= u_flow[1:T] <= 12) # 水力发电
@variable(model, 0 <= u_thermal[1:T])    # 火力发电

# 随机变量
@variable(model, ω_inflow[1:T])
for t in 1:T;fix(ω_inflow[t], data[t, :inflow]);end

# 最小化成本
@constraint(model,[t in 1:T],x_storage[t+1] == x_storage[t] - u_flow[t] - u_spill[t] + ω_inflow[t])
@constraint(model, [t in 1:T], u_flow[t] + u_thermal[t] == data[t, :demand])
@objective(model, Min, sum(data[t, :cost] * u_thermal[t] for t in 1:T))

optimize!(model)
objective_value(model)

使用SDDP.jl改造代码为：

model = SDDP.LinearPolicyGraph(
    stages = T,
    sense = :Min,
    lower_bound = 0.0,
    optimizer = HiGHS.Optimizer,
) do sp, t
    @variable(sp, 0 <= x_storage <= 320, SDDP.State, initial_value = 300,)
    @variable(sp, 0 <= u_flow <= 12)
    @variable(sp, 0 <= u_thermal)
    @variable(sp, 0 <= u_spill)
    
    @variable(sp, ω_inflow)
    #fix(ω_inflow, data[t, :inflow])
    Ω, P = [-2, 0, 5], [0.3, 0.4, 0.3]
    SDDP.parameterize(sp, Ω, P) do ω
        fix(ω_inflow, data[t, :inflow] + ω)
        return
    end
    
    @constraint(sp, x_storage.out == x_storage.in - u_flow - u_spill + ω_inflow)
    @constraint(sp, u_flow + u_thermal == data[t, :demand])
    @stageobjective(sp, data[t, :cost] * u_thermal)
    return
end
SDDP.train(model; iteration_limit = 100)
SDDP.calculate_bound(model)

4 探索迷宫：UnicyclicGraph

首先构造一个迷宫：

M, N = 3, 4
initial_square = (1, 1)
reward, illegal_squares, penalties = (3, 4), [(2, 2)], [(3, 1), (2, 4)]
path = fill("⋅", M, N)
path[initial_square...] = "1"
for (k, v) in (illegal_squares => "▩", penalties => "†", [reward] => "*")
    for (i, j) in k
        path[i, j] = v
    end
end
print(join([join(path[i, :], ' ') for i in 1:size(path, 1)], '\n'))


定义P为所有满足可以移动到 (i, j)的(a, b) 集合。
使用SDDP.UnicyclicGraph，定义一个无限的循环图

discount_factor = 0.9
graph = SDDP.UnicyclicGraph(discount_factor)

定义了一个不断自循环的图：

Root
 0
Nodes
 1
Arcs
 0 => 1 w.p. 1.0
 1 => 1 w.p. 0.9

import HiGHS

model = SDDP.PolicyGraph(
    graph;
    sense = :Max,
    upper_bound = 1 / (1 - discount_factor),
    optimizer = HiGHS.Optimizer,
) do sp, _
    # Our state is a binary variable for each square
    @variable(
        sp,x[i = 1:M, j = 1:N],Bin,SDDP.State, # 每一阶段都是M*N的状态变量
        initial_value = (i, j) == initial_square, # 初始位置(1,1)
    )
    # 只能在一个位置
    @constraint(sp, sum(x[i, j].out for i in 1:M, j in 1:N) == 1)
    # Incur rewards and penalties
    # 这里reward是终点位置，penalties是所有惩罚点的位置
    @stageobjective(sp, x[reward...].out - sum(x[i, j].out for (i, j) in penalties))
    # Constraints on valid moves
    for i in 1:M, j in 1:N
        # 允许移动的位置
        moves = [(i - 1, j), (i + 1, j), (i, j), (i, j + 1), (i, j - 1)]
       filter!(v -> 1 <= v[1] <= M && 1 <= v[2] <= N && !(v in illegal_squares), moves)
        # 移动出去的值不能大于其他位置进去的值的和。由于都是Bin变量，因此等价于说从i，j移动到了某个允许的a，b上
        @constraint(sp, x[i, j].out <= sum(x[a, b].in for (a, b) in moves))
    end
    return
end
SDDP.train(model)
simulations = SDDP.simulate(
    model,
    1,
    [:x];
    sampling_scheme = SDDP.InSampleMonteCarlo(
        max_depth = 10,
        #terminate_on_dummy_leaf = false,
    ),
);
for (t, data) in enumerate(simulations[1]), i in 1:M, j in 1:N
    if data[:x][i, j].in > 0.5
        path[i, j] = "$t"
    end
end

print(join([join(path[i, :], ' ') for i in 1:size(path, 1)], '\n'))

注意Simulating a cyclic policy graph requires an explicit sampling_scheme that does not terminate early based on the cycle probability。结果为：

1 2 3 ⋅
⋅ ▩ 4 †
† ⋅ 5 *

5 牛奶制造：MarkovianGraph

使用MarkovianGraph可以接受一个仿真器graph = SDDP.MarkovianGraph(simulator; budget = 30, scenarios = 10_000);
buget是node的个数，scenarios是计算转移概率时的采样个数。这里使用Markovian过程来模拟一个随时间变化的过程，node的个数需要大于stage的个数，node越多，模拟越精确。

这里问题描述如下：

  • 产奶量不确定，牛奶市场价格不确定
  • 需求无限。未售出的牛奶可以制成奶粉
  • 公司也可以接订单，按照当前价格收款，但是4个月之后交付。届时产能不够的话，需要额外购买牛奶。
  • 决策为需要接多少订单

首先我们用预测模型给出牛奶的价格，假设模型如下：

function simulator()
    residuals = [0.0987, 0.199, 0.303, 0.412, 0.530, 0.661, 0.814, 1.010, 1.290]
    residuals = 0.1 * vcat(-residuals, 0.0, residuals)
    scenario = zeros(12)
    y, μ, α = 4.5, 6.0, 0.05
    for t in 1:12
        y = exp((1 - α) * log(y) + α * log(μ) + rand(residuals))
        scenario[t] = clamp(y, 3.0, 9.0)
    end
    return scenario
end

plot = Plots.plot(
    [simulator() for _ in 1:500];
    color = "gray",
    opacity = 0.2,
    legend = false,
    xlabel = "Month",
    ylabel = "Price [\$/kg]",
    xlims = (1, 12),
    ylims = (3, 9),
)

用30个点模拟出来的转移概率图如下：

for ((t, price), edges) in graph.nodes
    for ((t′, price′), probability) in edges
        Plots.plot!(
            plot,
            [t, t′],
            [price, price′];
            color = "red",
            width = 3 * probability,
        )
    end
end

plot

接下来给定具体数值：

  • 产奶量不确定，为0.1-0.2之间的均匀分布。生产没有成本。
  • 牛奶市场价格按照node进行概率转移，售出价格1倍
  • 需求无限。未售出的牛奶可以制成奶粉然后在下个月以同样价格售出。
  • 公司也可以接订单，按照当前价格收款，但是4个月之后交付。成本为绝对值0.01，四个月后售出价格为1.05倍
  • 届时产能不够的话，需要额外购买牛奶。费用1.5倍
  • 决策为需要接多少订单

建模如下：

model = SDDP.PolicyGraph(
    graph;# 将price用graph随机过程来模拟，不用再单独建随机变量来模拟
    sense = :Max,
    upper_bound = 1e2,
    optimizer = HiGHS.Optimizer,
) do sp, node
    t, price = node::Tuple{Int,Float64}
    c_buy_premium = 1.5
    Ω_production = range(0.1, 0.2; length = 5)
    c_max_production = 12 * maximum(Ω_production)
    
    # 两个状态变量
    @variable(sp, 0 <= x_stock, SDDP.State, initial_value = 0) # track库存变量
    @variable(sp, 0 <= x_forward[1:4], SDDP.State, initial_value = 0) # track forward变化

    # 三个核心决策变量
    @variable(sp, 0 <= u_spot_sell <= c_max_production)
    @variable(sp, 0 <= u_spot_buy <= c_max_production);c_max_futures = t <= 8 ? c_max_production : 0.0
    @variable(sp, 0 <= u_forward_sell <= c_max_futures)

    # 一个随机变量
    @variable(sp, ω_production)
    # Forward contracting constraints:
    @constraint(sp, [i in 1:3], x_forward[i].out == x_forward[i+1].in)
    @constraint(sp, x_forward[4].out == u_forward_sell)
    @constraint(sp, x_stock.out == x_stock.in + ω_production + u_spot_buy - x_forward[1].in - u_spot_sell)
    Ω = [(price, p) for p in Ω_production]
    SDDP.parameterize(sp, Ω) do ω::Tuple{Float64,Float64}
        fix(ω_production, ω[2])
        @stageobjective(sp,  ω[1] * (u_spot_sell - 1.5 * u_spot_buy) +(ω[1] * 1.05 - 0.01) * u_forward_sell)
        return
    end
    return
end

SDDP.SimulatorSamplingScheme is used in the forward pass. It generates an out-of-sample sequence of prices using simulator and traverses the closest sequence of nodes in the policy graph.翻译过来就是SimulatorSamplingScheme会根据产生的随机变量，寻找最为接近的node路径。
AVaR(β) Computes the expectation of the β fraction of worst outcomes. β must be in [0, 1]. When β=1, this is equivalent to the Expectation risk measure. When β=0, this is equivalent to the WorstCase risk measure. 翻译过来就是AVaR综合了最差情况和均值。

SDDP.train(
    model;
    time_limit = 20,
    risk_measure = 0.5 * SDDP.Expectation() + 0.5 * SDDP.AVaR(0.25),
    sampling_scheme = SDDP.SimulatorSamplingScheme(simulator),
)

我们仿真一次：

simulations = SDDP.simulate(
    model,
    200,
    Symbol[:x_stock, :u_forward_sell, :u_spot_sell, :u_spot_buy];
    sampling_scheme = SDDP.SimulatorSamplingScheme(simulator),
);
simulations[1][12]


可以发现，node_index里面的price下标是7.78204， SDDP.parameterize的实际用的price是7.66547

版权声明：本文为博主作者：IE06原创文章，版权归属原作者，如果侵权，请联系我们删除！

原文链接：https://blog.csdn.net/kittyzc/article/details/135030528

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