某工厂为七天24小时生产,需要工人值班,分为早、中、晚三班倒,目前有N=12名员工轮换值班,编号为1,2,…,𝑁. 要求: 每人每天只能值一个班,若前一天晚上值班,当天早上不能值班; 每人一周至少休息2天(一天时间 00:00 – 24:00);周一到周日早班分别需要的人数为4,3,3,3,4,2,3;周一到周日中班分别需要的人数为4,3,3,2,3,2,2;周一到周日晚班分别需要的人数为3,2,2,3,3,1,2; 问题: 在保证正常工厂生产的前提下,至多需要裁多少员工,并给出未来一周的排班表,即每人在哪一天的什么时间段值班?
时间: 2023-08-17 19:05:08 浏览: 165
3人24小时值班表,多种排班方式
以下是Python语言实现的代码:
```python
from docplex.mp.model import Model
N = 12
days = 7
shifts = 3
morning_demand = [4, 3, 3, 3, 4, 2, 3]
afternoon_demand = [4, 3, 3, 2, 3, 2, 2]
night_demand = [3, 2, 2, 3, 3, 1, 2]
ss = Model(name='Shift Scheduling')
# Define variables
work_shifts = ss.binary_var_matrix(N, days, shifts, name='work_shifts')
# Define constraints
for j in range(days):
for k in range(shifts):
expr = ss.linear_expr()
for i in range(N):
expr += work_shifts[i, j, k]
if k == 0:
ss.add_eq(expr == morning_demand[j])
elif k == 1:
ss.add_eq(expr == afternoon_demand[j])
else:
ss.add_eq(expr == night_demand[j])
for i in range(N):
for j in range(days):
expr = ss.linear_expr()
for k in range(shifts):
expr += work_shifts[i, j, k]
ss.add_le(expr, 1)
for i in range(N):
for j in range(days):
if j > 0:
if work_shifts[i, j-1, 2].solution_value == 1:
work_shifts[i, j, 0].ub = 0
for i in range(N):
for j in range(days):
if j + 2 < days:
expr = ss.linear_expr()
for k in range(shifts):
expr += work_shifts[i, j, k] + work_shifts[i, j+1, k] + work_shifts[i, j+2, k]
ss.add_le(expr, 2)
# Define objective
ss.maximize(ss.sum(work_shifts))
# Solve model
ss.solve()
# Print results
print('Maximum employees: ' + str(int(ss.objective_value)))
print('Shift schedule:')
for i in range(N):
print('Employee ' + str(i+1) + ': ', end='')
for j in range(days):
for k in range(shifts):
if work_shifts[i, j, k].solution_value == 1:
print('Day ' + str(j+1) + ', Shift ' + str(k+1) + '; ', end='')
print()
```
Python代码解释:
- `N`:员工数目,本题中为12。
- `days`:一周有7天。
- `shifts`:一天有3个班次,分别为早班、中班和晚班。
- `morning_demand`:每天早班需要
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