PuLP - Python -酒店收入优化

Question

我正在尝试重新创建一篇使用线性规划来优化酒店收入的论文。我有许多不同的xi，j，我试图解决其中x是接受预订，我是入住日，j是退房日。我对我正在导入的这些i，j对中的每一个都有需求，并且接受的预订必须是<=需求。对于我的生活，我不能想出如何编程的约束，即在任何给定的k天，已经在酒店的人+在k天入住的人-在k天结账的人必须是<=容量。下面是我正在尝试编写的代码：

​

到目前为止，我的代码如下：

import pandas as pd
import pulp

# Instantiate our problem class
model = pulp.LpProblem("Hotel revenue maximization", pulp.LpMaximize)

#Import demand info
demand= pd.DataFrame.from_csv("demandSAHRO.csv", index_col=
['Check_in_day_i', 'Check_out_day_j'])

#Will optimize for # bookings to accept for any i,j pairing
bookingsaccepted = pulp.LpVariable.dicts("bookingsaccepted",
                                 ((i, j) for i, j in demand.index), lowBound=0, cat='Integer')


# Objective Function - 0.84 is unit revenue per room
model += (
pulp.lpSum([
    0.84 * bookingsaccepted[i, j] for i, j in demand.index])
)

# Constraints
capacity = 400


#Accepted Check in before day k + accepted check in on day k - accepted check out on day k <= capacity
model +=
for i, j in demand.index:
    for k in range(1,10):
    #Day k between check in and check out dates
        while i<k<j:
            #Rooms already occupied during night k
            pulp.lpSum([bookingsaccepted[i, j] for i, j in demand.index]))
            +
            #Rooms checking in on day k

            -

            #Rooms checking out on day k

我是用Python编码的新手，也是使用PuLP的新手，所以我非常感谢大家的帮助。