问题描述
我是 Google OR-Tools 的新手(以及一般的约束编程),我正在尝试将空闲时间添加到 Jobshop 示例中。
可以在此处找到我采用的工作车间示例:https://developers.google.com/optimization/scheduling/job_shop#entire-program
我做了一些修改,因为我包含了一个截止日期变量。现在我唯一想添加到程序中的是一个空闲变量。例如,完成一项任务的机器在开始下一项任务之前必须有一定的空闲时间。我怎么能在代码中实现这个? 我的代码:
import collections
# Import Python wrapper for or-tools CP-SAT solver.
from ortools.sat.python import cp_model
def MinimalJobshopSat():
"""Minimal jobshop problem."""
# Create the model.
model = cp_model.CpModel()
jobs_data = [ # task = (machine_id,processing_time,deadline).
[(0,3,7),(1,2,(2,7)],# Job0
[(0,13),1,4,13)],# Job1
[(1,13)] # Job2
]
#counts the number of machines (3 in this case)
machines_count = 1 + max(task[0] for job in jobs_data for task in job)
all_machines = range(machines_count)
# Computes horizon dynamically as the sum of all durations.
horizon = sum(task[1] for job in jobs_data for task in job)
# Named tuple to store information about created variables.
task_type = collections.namedtuple('task_type','start end deadline interval')
# Named tuple to manipulate solution information.
assigned_task_type = collections.namedtuple('assigned_task_type','start job index duration')
# Creates job intervals and add to the corresponding machine lists.
all_tasks = {}
machine_to_intervals = collections.defaultdict(list)
for job_id,job in enumerate(jobs_data):
for task_id,task in enumerate(job):
machine = task[0]
duration = task[1]
deadline = task[2]
suffix = '_%i_%i' % (job_id,task_id)
start_var = model.NewIntvar(0,horizon,'start' + suffix)
end_var = model.NewIntvar(0,deadline,'end' + suffix)
interval_var = model.NewIntervalVar(start_var,duration,end_var,'interval' + suffix)
deadline_var = model.NewIntvar(deadline,'deadline' + suffix)
all_tasks[job_id,task_id] = task_type(
start=start_var,end=end_var,deadline=deadline_var,interval=interval_var)
machine_to_intervals[machine].append(interval_var)
# Create and add disjunctive constraints.
for machine in all_machines:
model.AddNoOverlap(machine_to_intervals[machine])
# Precedences inside a job.
for job_id,job in enumerate(jobs_data):
for task_id in range(len(job) - 1):
model.Add(all_tasks[job_id,task_id +
1].start >= all_tasks[job_id,task_id].end)
for job_id,task_id].end <= all_tasks[job_id,task_id].deadline)
# Makespan objective.
obj_var = model.NewIntvar(0,'makespan')
model.AddMaxEquality(obj_var,[
all_tasks[job_id,len(job) - 1].end
for job_id,job in enumerate(jobs_data)
])
model.Minimize(obj_var)
# Solve model.
solver = cp_model.cpsolver()
status = solver.solve(model)
if status == cp_model.OPTIMAL:
# Create one list of assigned tasks per machine.
assigned_jobs = collections.defaultdict(list)
for job_id,job in enumerate(jobs_data):
for task_id,task in enumerate(job):
machine = task[0]
assigned_jobs[machine].append(
assigned_task_type(
start=solver.Value(all_tasks[job_id,task_id].start),job=job_id,index=task_id,duration=task[1]))
# Create per machine output lines.
output = ''
for machine in all_machines:
# Sort by starting time.
assigned_jobs[machine].sort()
sol_line_tasks = 'Machine ' + str(machine) + ': '
sol_line = ' '
for assigned_task in assigned_jobs[machine]:
name = 'job_%i_%i' % (assigned_task.job,assigned_task.index)
# Add spaces to output to align columns.
sol_line_tasks += '%-10s' % name
start = assigned_task.start
duration = assigned_task.duration
sol_tmp = '[%i,%i]' % (start,start + duration)
# Add spaces to output to align columns.
sol_line += '%-10s' % sol_tmp
sol_line += '\n'
sol_line_tasks += '\n'
output += sol_line_tasks
output += sol_line
# Finally print the solution found.
print('Optimal Schedule Length: %i' % solver.ObjectiveValue())
print(output)
MinimalJobshopSat()
解决方法
您可以更改此约束:
# Precedences inside a job.
for job_id,job in enumerate(jobs_data):
for task_id in range(len(job) - 1):
model.Add(all_tasks[job_id,task_id +
1].start >= all_tasks[job_id,task_id].end)
到
model.Add(all_tasks[job_id,task_id].end + idle_time)
有关更复杂的示例(任务之间的距离),请参阅:https://github.com/google/or-tools/blob/stable/examples/python/jobshop_ft06_distance_sat.py