Contingent Portfolio Programming

Warning

This example discusses adding constraints and decision variables to the Decision Programming formulation, as well as custom path utility calculation. Because of this, it is quite advanced compared to the earlier ones.

Description

1, section 4.2:

For instance, assume that the first-stage decisions specify which technology development projects will be started to generate patent-based intellectual property $(P)$ for a platform. This intellectual property contributes subject to some uncertainties to the technical competitiveness $(T)$ of the platform. In the second stage, it is possible to carry out application $(A)$ development projects which, when completed, yield cash flows that depend on the market share of the platform. This market share $(M)$ depends on the competitiveness of the platform and the number of developed applications. The aim is to maximize the cash flows from application projects less the cost of technology and application development projects.

Influence Diagram: Projects

The influence diagram of the contingent portfolio programming (CPP) problem.

There are $n_{T}$ technology development projects and $n_{A}$ application development projects.

Decision states to develop patents

d_{i}^{P} \in D_{i}^{P} = {[q_{1}^{P}, q_{2}^{P}), [q_{2}^{P}, q_{3}^{P}) \dots, [q_{| D^{P} |}^{P}, q_{| D^{P} | + 1}^{P})} .

Chance states of technical competitiveness $c_{j}^{T} \in C_{j}^{T}$ .

Decision states to develop applications

d_{k}^{A} \in D^{A} = {[q_{1}^{A}, q_{2}^{A}), [q_{2}^{A}, q_{3}^{A}) \dots, [q_{| D^{A} |}^{A}, q_{| D^{A} | + 1}^{A})} .

Chance states of market size $c_{j}^{M} \in C_{j}^{M}$ .

import DecisionProgramming as dp
import numpy as np

dp.activate()
np.random.seed(42)


diagram = dp.InfluenceDiagram()

DP = dp.DecisionNode("DP", [], ["0-3 patents", "3-6 patents", "6-9 patents"])
diagram.add_node(DP)

CT = dp.ChanceNode("CT", ["DP"], ["low", "medium", "high"])
diagram.add_node(CT)

DA = dp.DecisionNode("DA", ["DP", "CT"], ["0-5 applications", "5-10 applications", "10-15 applications"])
diagram.add_node(DA)

CM = dp.ChanceNode("CM", ["CT", "DA"], ["low", "medium", "high"])
diagram.add_node(CM)

diagram.generate_arcs()

Technical competitiveness probability

Probability of technical competitiveness $c_{j}^{T}$ given the range $d_{i}^{P} : P (c_{j}^{T} ∣ d_{i}^{P}) \in [0, 1]$ . A high number of patents increases probability of high competitiveness and a low number correspondingly increases the probability of low competitiveness.

X_CT = diagram.construct_probability_matrix("CT")
X_CT[0, :] = [1/2, 1/3, 1/6]
X_CT[1, :] = [1/3, 1/3, 1/3]
X_CT[2, :] = [1/6, 1/3, 1/2]
diagram.set_probabilities("CT", X_CT)

Generating the Influence Diagram

We are going to be using a custom objective function, and don’t need the default path utilities for that.

diagram.generate(default_utility=False)

Decision Model: Portfolio Selection

We create the decision variables $z (s_{j} ∣ s_{I (j)})$ and notice that the activation of paths that are compatible with the decision strategy is handled by the problem specific variables and constraints together with the custom objective function, eliminating the need for separate variables representing path activation.

model = dp.Model()
z = diagram.decision_variables(model)

Creating problem specific variables

In pyDecisionProgramming problems specific constraints are defined as strings. The syntax is closer to Julia than Python. First, it is convenient to define the variables we will need in dp.julia. These will be available when defining the constraints.

We recommend reading section 4.2. in 1 for motivation and details of the formulation.

Technology project $t$ costs $I_{t} \in R^{+}$ and generates $O_{t} \in N$ patents.

Application project $a$ costs $I_{a} \in R^{+}$ and generates $O_{a} \in N$ applications. If completed, provides cash flow $V (a ∣ c_{l}^{M}) \in R^{+}$ .

n_T = 5               # number of technology projects
n_A = 5               # number of application projects

# Here we set stuff in Julia name space directly
I_t = np.random.random(n_T)*0.1   # costs of technology projects
O_t = np.random.randint(1, 4, n_T)   # number of patents for each tech project
I_a = np.random.random(n_T)*2     # costs of application projects
O_a = np.random.randint(2, 5, n_T)   # number of applications for each appl. project

# Set the names in dp.julia to use them when setting constraints
dp.julia.I_t = I_t
dp.julia.O_t = O_t
dp.julia.I_a = I_a
dp.julia.O_a = O_a

V_A = np.random.random((n_CM, n_A)) + 0.5  # Value of an application
V_A[0, :] += -0.5           # Low market share: less value
V_A[2, :] += 0.5            # High market share: more value

dp.julia.V_A = V_A

Decision variables $x^{T} (t) \in {0, 1}$ indicate which technologies are selected.

Decision variables $x^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) \in {0, 1}$ indicate which applications are selected.

x_T = dp.JuMP.Array(model, [n_DP, n_T], binary=True)
x_A = dp.JuMP.Array(model, [n_DP, n_CT, n_DA, n_A], binary=True)
dp.julia.x_T = x_T
dp.julia.x_A = x_A

Number of patents $x^{T} (t) = \sum_{i} x_{i}^{T} (t) z (d_{i}^{P})$ .

Number of applications $x^{A} (a ∣ d_{i}^{P}, x_{j}^{T}) = \sum_{k} x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) z (d_{k}^{A} ∣ d_{i}^{P}, x_{j}^{T})$ .

Helpful variables:

Large constant $M$ (e.g. $\frac{3}{2} max {\sum_{t} O_{t}, \sum_{a} O_{a}}$ ).

Small constant $ϵ = \frac{1}{2} min {O_{t}, O_{a}}$ .

dp.julia.M = 20                        # a large constant
dp.julia.eps = 0.5*np.min([O_t, O_a])  # a helper variable, allows using ≤ instead of < in constraints (28b) and (29b)

Limits $q_{i}^{P}$ and $q_{k}^{A}$ of the intervals

dp.julia.q_P = [0, 3, 6, 9]          # limits of the technology intervals
dp.julia.q_A = [0, 5, 10, 15]        # limits of the application intervals

Shorthand for the decision variables $z$

dp.julia.z_dP = z.z[0]
dp.julia.z_dA = z.z[1]

The diagram itself

dp.julia.diagram = diagram

Creating problem specific constraints

\sum_{t} x_{i}^{T} (t) \leq z (d_{i}^{P}) n_{T}, \forall i

model.constraint(
    f"[i=1:{n_DP}]",
    f"sum(x_T[i,t] for t in 1:{n_T}) <= z_dP[i]*{n_T}"
)

\sum_{a} x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) \leq z (d_{i}^{P}) n_{A} \forall i, j, k

model.constraint(
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"sum(x_A[i,j,k,a] for a in 1:{n_A}) <= z_dP[i]*{n_A}"
)

\sum_{a} x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) \leq z (d_{k}^{A} ∣ d_{i}^{P}, c_{j}^{T}) n_{A}, \forall i, j, k

model.constraint(
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"sum(x_A[i,j,k,a] for a in 1:{n_A}) <= z_dA[i,j,k]*{n_A}"
)

q_{i}^{P} - (1 - z (d_{i}^{P})) M \leq \sum_{t} x_{i}^{T} (t) O_{t} \leq q_{i + 1}^{P} + (1 - z (d_{i}^{P})) M - ϵ, \forall i

model.constraint(
    f"[i=1:{n_DP}]",
    f"q_P[i] - (1 - z_dP[i])*M <= sum(x_T[i,t]*O_t[t] for t in 1:{n_T})"
)
model.constraint(
    f"[i=1:{n_DP}]",
    f"sum(x_T[i,t]*O_t[t] for t in 1:{n_T}) <= q_P[i+1] + (1 - z_dP[i])*M - eps"
)

q_{k}^{A} - (q - z (d_{k}^{A} ∣ d_{i}^{A}, c_{j}^{T})) M \leq \sum_{a} x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) O_{a} \leq q_{k + 1}^{A} + (1 - z (d_{k}^{A} ∣ d_{i}^{P}, c_{j}^{T})) M - ϵ, \forall i, j, k

model.constraint(
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"q_A[k] - (1 - z_dA[i,j,k])*M <= sum(x_A[i,j,k,a]*O_a[a] for a in 1:{n_A})"
)
model.constraint(
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"sum(x_A[i,j,k,a]*O_a[a] for a in 1:{n_A}) <= q_A[k+1] + (1 - z_dA[i,j,k])*M - eps"
)

We can also model dependencies between the technology and application projects, e.g. application project $a$ can be completed only if technology project $t$ has been completed. This is done by adding constraints

x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) \leq x_{i}^{T} (t), \forall i, j, k

As an example, we state that application projects 1 and 2 require technology project 1, and application project 2 also requires technology project 2.

model.constraint(
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"x_A[i,j,k,1] <= x_T[i,1]"
)
model.constraint(
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"x_A[i,j,k,2] <= x_T[i,1]"
)
model.constraint(
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"x_A[i,j,k,2] <= x_T[i,2]"
)

x_{i}^{T} (t) \in {0, 1}, \forall i

x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) \in {0, 1}, \forall i, j, k

Objective function

The path utility can be calculated as $U (s) \sum_{a} x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) (V (a ∣ c_{l}^{M}) - I_{a}) - \sum_{t} x_{i}^{T} (t) I_{t}$

However, using the expected value objective would lead to a quadratic objective function as the path utility formulation now contains decision variables. In order to keep the problem completely linear, we can use the objective formulation presented in 1:

\sum_{i} {\sum_{i, j, k} p (c_{j}^{T} ∣ d_{i}^{P}) p (c_{l}^{M} ∣ c_{j}^{T}, d_{k}^{A}) [\sum_{a} x_{k}^{A} (a ∣ d_{i}^{P}, c_{j}^{T}) (V (a ∣ c_{l}^{M}) - I_{a}) - \sum_{t} x_{i}^{T} (t) I_{t}]}

dp.julia.patent_investment_cost = dp.JuMP.Expression(
    model,
    f"[i=1:{n_DP}]",
    f"sum(x_T[i, t] * I_t[t] for t in 1:{n_T})"
)

dp.julia.application_investment_cost = dp.JuMP.Expression(
    model,
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}]",
    f"sum(x_A[i, j, k, a] * I_a[a] for a in 1:{n_A})"
)

dp.julia.application_value = dp.JuMP.Expression(
    model,
    f"[i=1:{n_DP}, j=1:{n_CT}, k=1:{n_DA}, l=1:{n_CM}]",
    f"sum(x_A[i, j, k, a] * V_A[l, a] for a in 1:{n_A})"
)

model.objective(
    f"sum( sum( diagram.P(convert.(State, (i,j,k,l))) * (application_value[i,j,k,l] - application_investment_cost[i,j,k]) for j in 1:{n_CT}, k in 1:{n_DA}, l in 1:{n_CM} ) - patent_investment_cost[i] for i in 1:{n_DP} )"
)

Solving the Model

model.setup_Gurobi_optimizer(
   ("IntFeasTol", 1e-9),
   ("LazyConstraints", 1)
)
model.optimize()

Analyzing results

The optimal decision strategy and the utility distribution are printed. The strategy is to make 6-9 patents (state 3 in node 1) and 10-15 applications. The expected utility for this strategy is 1.71.

Z = z.decision_strategy()
S_probabilities = diagram.state_probabilities(Z)

In [1]: S_probabilities.print_decision_strategy()

Out[1]:
┌────────────────┐
│ Decision in DP │
├────────────────┤
│ 6-9 patents    │
└────────────────┘
┌─────────────────────┬────────────────────┐
│ State(s) of DP, CT  │ Decision in DA     │
├─────────────────────┼────────────────────┤
│ 6-9 patents, low    │ 10-15 applications │
│ 6-9 patents, medium │ 10-15 applications │
│ 6-9 patents, high   │ 10-15 applications │
└─────────────────────┴────────────────────┘

We use a custom path utility function to obtain the utility distribution.

# Note that indexing in Julia starts from 1, so we
# must add 1 to each index
dp.julia.DP_i = diagram.index_of("DP") + 1
dp.julia.CT_i = diagram.index_of("CT") + 1
dp.julia.DA_i = diagram.index_of("DA") + 1
dp.julia.CM_i = diagram.index_of("CM") + 1
path_utilities = dp.Diagram.ExpressionPathUtilities(
    model, diagram,
    f'''sum(x_A[s[index_of(diagram, "DP")], s[index_of(diagram, "CT")], s[index_of(diagram, "DA")], a] * (V_A[s[index_of(diagram, "CM")], a] - I_a[a]) for a in 1:{n_A}) -
        sum(x_T[s[index_of(diagram, "DP")], t] * I_t[t] for t in 1:{n_T})
    ''',
    path_name = "s"
)

diagram.set_path_utilities(path_utilities)

U_distribution = diagram.utility_distribution(Z)

In [2]: U_distribution.print_distribution()

Out[2]:
┌───────────┬─────────────┐
│   Utility │ Probability │
│   Float64 │     Float64 │
├───────────┼─────────────┤
│ -1.243076 │    0.152778 │
│  0.605287 │    0.291667 │
│  3.110470 │    0.555556 │
└───────────┴─────────────┘

In [3]: U_distribution.print_statistics()

Out[3]:
┌──────────┬────────────┐
│     Name │ Statistics │
│   String │    Float64 │
├──────────┼────────────┤
│     Mean │   1.714666 │
│      Std │   1.666694 │
│ Skewness │  -0.613535 │
│ Kurtosis │  -1.154244 │
└──────────┴────────────┘

References

1(1,2,3): Salo, A., Andelmin, J., & Oliveira, F. (2019). Decision Programming for Multi-Stage Optimization under Uncertainty, 1–35. Retrieved from http://arxiv.org/abs/1910.09196