使用Pulp建模非方阵时遭遇索引越界问题求助

Hey there! Let's break down why you're hitting that IndexError: list index out of range and get your model working with non-square matrices like your target 365×24 setup.

The Root Cause #

The problem lies in how you're constructing your Excel data matrices (TEST, YAPR, MAPR) versus how you're accessing them later.

Looking at your matrix-building code:

TEST = [[sheet11.cell_value(i,j) for i in range(YPER)] for j in range(HE)]

This creates a column-first matrix: the outer loop iterates over columns (j, 0-5 for HE=6), and the inner loop over rows (i, 0-4 for YPER=5). The result is a 6-row × 5-column structure where TEST[j][i] maps to Excel's cell (i,j).

But later in your constraint, you're trying to access it as TEST[i][j]—treating it like a row-first matrix. Since each sublist in TEST only has 5 elements (one per row), when j hits 5 (the 6th column), you're trying to access an index that doesn't exist in the sublist, hence the error.

The Fix #

You have two straightforward options, but the first is more intuitive and aligns with standard row-first thinking:

Option 1: Build Row-First Matrices

Adjust your list comprehensions to iterate over rows first, then columns. This creates a YPER×HE matrix (5×6 for your test case) where TEST[i][j] directly maps to Excel's cell (i,j):

# Build matrices row-first to match your variable indexing
TEST = [[sheet11.cell_value(i,j) for j in range(HE)] for i in range(YPER)]
YAPR = [[sheet12.cell_value(i,j) for j in range(HE)] for i in range(YPER)]
MAPR = [[sheet13.cell_value(i,j) for j in range(HE)] for i in range(YPER)]

Option 2: Adjust Indexing When Accessing Matrices

If you prefer to keep the column-first matrix build, swap the indices when accessing the values in your constraint:

model += pulp.lpSum([YAHL[i][j] + MAHL[i][j]]) == TEST[j][i]

But Option 1 is cleaner and less prone to future mix-ups, especially as you scale to larger matrices like 365×24.

Bonus: Simplify Your Constraint #

You can also remove the unnecessary brackets around TEST[i][j]—you're comparing a scalar sum to a single value, no need for a list:

for i,j in yearlyhours:
    model += pulp.lpSum([YAHL[i][j] + MAHL[i][j]]) == TEST[i][j]

Full Modified Code #

Here's the complete fixed code for your test case:

from pulp import *
import pandas as pd
import numpy as np
import xlrd

model = pulp.LpProblem("Basic Model", pulp.LpMinimize)
YPER = 5
HE = 6
yearlyhours = [(i,j) for i in range(YPER) for j in range(HE)]

book = xlrd.open_workbook('Stack.xlsx')
sheet11 = book.sheet_by_name('Sheet11')
sheet12 = book.sheet_by_name('Sheet12')
sheet13 = book.sheet_by_name('Sheet13')

# Fixed: build row-first matrices
TEST = [[sheet11.cell_value(i,j) for j in range(HE)] for i in range(YPER)]
YAPR = [[sheet12.cell_value(i,j) for j in range(HE)] for i in range(YPER)]
MAPR = [[sheet13.cell_value(i,j) for j in range(HE)] for i in range(YPER)]

YAHL = pulp.LpVariable.dicts("YAHL", (range(YPER), range(HE)), lowBound=0, cat='Continuous')
MAHL = pulp.LpVariable.dicts("MAHL", (range(YPER), range(HE)), lowBound=0, cat='Continuous')

##OBJECTIVE##
model += pulp.lpSum([YAPR[i][j] * YAHL[i][j] + MAPR[i][j] * MAHL[i][j] for i in range(YPER) for j in range(HE)]), 'Sum_of_Value'

# Fixed constraint indexing and simplified syntax
for i,j in yearlyhours:
    model += pulp.lpSum([YAHL[i][j] + MAHL[i][j]]) == TEST[i][j]

LpSolverDefault.msg = 1
model.writeLP('Opt.lp')
model.solve()

print("Status:", LpStatus[model.status])
obj = value(model.objective)
print("Total Cost: ${:.2f}".format(obj))
print('\n')

Scaling to 365×24 #

This fix will work seamlessly for your 365×24 matrix—just update YPER = 365 and HE = 24, and the row-first matrix structure will align perfectly with your variables and constraints, no more index errors.

内容的提问来源于stack exchange，提问作者bathtub2007