modeling lateral transshipment
AnsweredHi
I am modelling a lateral transshipment problem (two echelon supply chain) and I have difficultis to model the below conservative constraint.
x(ijt) is the transshipment dicision variable
\begin{equation}
v_{i}^{t}=f_{i}^{t}+\sum_{j \in R \setminus \{i\}}x_{ji(t-l_{ji})}+y_{i}^{t}-\sum_{j \in R \setminus \{i\}} x_{ijt}\quad \forall i \in R, t \in T
\tag{3}
\end{equation}
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Official comment
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Gurobi Staff
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Hi,
What exactly do you have difficulties with? Could you post a minimal working example of what you already tried?
I would recommend having a look at our Introduction to Modeling with Python webinar. Additionally, having a glance at our Python examples should help.
To model a constraint of the form \(\sum_{j\in I \\\{i\}} x_{jit} = v_i^t\), you can use the following snippet
import gurobipy as gp
m = gp.Model()
I = [0,1,2,3]
J = [0,1,2,3]
T = [0,1,2,3]
x = m.addVars(I,J,T,name="x")
v = m.addVars(I,T,name="v")
m.addConstrs((gp.quicksum(x[j,i,t] for j in J if i != j) == v[i,t] for i in I for t in T), name="constr")Best regards,
Jaromił0 -
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Dear Jaromił,
Thanks for your response. I have read Gurobi examples, but I couldn’t find my answer.
In my constraint, there is a decision variable (x[ijt]) which I want to model its input and output flow. And I don’t know how to code it.
\begin{equation}
v_{i}^{t}=f_{i}^{t}+\sum_{j \in R \setminus \{i\}}x_{jit}+y_{i}^{t}-\sum_{j \in R \setminus \{i\}} x_{ijt}\quad \forall i \in R, t \in T
\tag{3}
\end{equation}, in which, (i, j) are indices of the locations and t is time period.
Here is some parts of my code:
input data________________
# number of regions
n = 5
regions = [*range(1,n)]# planning periods
t = 3
days = [*range(t)]
# Create a dictionary to capture the coordinates of regions and their demand
coordinates = {
1:(1, 2), 2:(2.5, 1), 3:(5, 1), 4:(6.5, 3.5)}
# Compute pairwise distance matrix
# numpy linalg norm = euclidean n=2def distance(city1, city2):
c1 = coordinates[city1]
c2 = coordinates[city2]
diff = (c1[0]-c2[0], c1[1]-c2[1])
return math.sqrt(diff[0]*diff[0]+diff[1]*diff[1])dist = {(c1, c2): distance(c1, c2) for c1, c2 in combinations(regions, 2)}
model_________________________
# Transshipment amount from region i to region j in period t
x = m.addVars(dist, days, vtype=GRB.CONTINUOUS, name='transshipment')# Inventory level at region i at the end of period t
v = m.addVars(regions, days, vtype=GRB.CONTINUOUS, name='inventory_end')# Relief supply receive from Centeral Agency to region i in period t
y = m.addVars(regions, days, vtype=GRB.CONTINUOUS, name='receive')# Inventory level at region in at the beginning of period t
f = m.addVars(regions, days, vtype=GRB.CONTINUOUS, name='inventory_beginning')# 3. Transshipment and Inventory (my difficulty)
m.addConstrs((v[region, day] == f[region, day] + x.sum('*', day) + y[region, day] - x.sum('*', day)
for region in regions for day in days), name='balance')0 -
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Your sums have additional conditions so I would prefer using the quicksum function here.
m.addConstrs((v[region, day] == f[region, day] + gp.quicksum(x[j,region,day] for j in regions if j != region)
+ y[region, day] - gp.quicksum(x[region,j,day] for j in regions if j != region)
for region in regions for day in days), name='balance')Please note that this code results in a KeyError
KeyError: (2, 1, 0)
That is because \(\texttt{x[2,1,0]}\) is not present. However, according to your formulation, it should be there. Your \(\texttt{dist}\) dictionary looks like
{(1, 2): 1.8027756377319946, (1, 3): 4.123105625617661, (1, 4): 5.70087712549569, (2, 3): 2.5, (2, 4): 4.716990566028302, (3, 4): 2.9154759474226504}so it looks like there is indeed something missing. You don't have backward arcs like \(\texttt{(2,1),(3,1),...}\) and you don't have any arcs with \(\texttt{i=j}\) which makes the condition \(j \in R \\\{i\}\) redundant in your formulation.
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Yes, that’s right. Could you please guide me how I can fix it?
I didn’t define my arcs correctly, resulting in key errors in other constraints too.
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You are probably looking for itertools.product.
dist = {(c1, c2): distance(c1, c2) for c1, c2 in product(regions,regions)}
print(dist)results in
{(1, 1): 0.0, (1, 2): 1.8027756377319946, (1, 3): 4.123105625617661, (1, 4): 5.70087712549569, (2, 1): 1.8027756377319946, (2, 2): 0.0, (2, 3): 2.5, (2, 4): 4.716990566028302, (3, 1): 4.123105625617661, (3, 2): 2.5, (3, 3): 0.0, (3, 4): 2.9154759474226504, (4, 1): 5.70087712549569, (4, 2): 4.716990566028302, (4, 3): 2.9154759474226504, (4, 4): 0.0}0 -
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How I can output x[ijt] values in Gurobi?
I also tried to output other decision variables like v[it], but the output is not correct. Here is my code.
rows = days.copy()
columns = regions.copy()
v_plan = pd.DataFrame(columns=columns, index=rows, data=0.0)for region, day in v.keys():
if (abs(v[region, day].x) > 1e-6):
v_plan.loc[region, day] = np.round(v[region, day].x, 1)
v_plan0 -
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What is the error you get?
You can only access solution values after a successful optimization run, i.e., after calling model.optimize and finding at least 1 feasible solution.
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I call model.optimize() and get obj value, but when using the code for output display, it isn't right.
The output for one of my variables is here. I don't know why there is column [0]
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Your days go from 0 to 2
# planning periods
t = 3
days = [*range(t)]
print(days)
>[0, 1, 2]0 -
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Columns are regions and rows are planning periods. So why there is a column[0] and a row [3]?
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That's because \(\texttt{v.keys()}\) looks like this
print(v.keys())
( 1 , 0 )
( 1 , 1 )
( 1 , 2 )
( 2 , 0 )
( 2 , 1 )
( 2 , 2 )
( 3 , 0 )
( 3 , 1 )
( 3 , 2 )
( 4 , 0 )
( 4 , 1 )
( 4 , 2 )so in the \(\texttt{for}\)-loop you add the 0 column and 3 row. You have to swap columns and rows
rows = days.copy()
columns = regions.copy()
v_plan = pd.DataFrame(columns=rows, index=columns, data=0.0)0 -
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Thanks, could you help me how I can output x[ijt] values?
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You can access the solution values of the x variables via
for i in regions:
for j in regions:
for d in days:
print(x[i,j,d])I am not aware of a good way to show 3D data in a table via pandas. You could try asking in a forum dedicated for pandas, e.g., stackoverflow, or someone in this forum with more knowledge might help.
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Thank you so much. I genuinely appreciate all your help.
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