def runIntegerProgram(csvFile, timeLimit = -1, outputFlag = 0):
taskList = createTasksFromCsv.getTaskList(csvFile)
helperFunctions.preprocessTimeWindows(taskList)
schedule, isOptimal = integerProgramSolve(taskList, timeLimit, outputFlag)
# assert(isFeasible(taskList, schedule))
return schedule, isOptimal
def runGreedyByOrder(csvFile,orderMethod):
taskList = createTasksFromCsv.getTaskList(csvFile)
helperFunctions.preprocessTimeWindows(taskList)
# create a list of tuples (time of latest time window ending, taskId)
timeWindowsAndPriorities = []
for task in taskList:
latestTime = 0
for day in task.timeWindows:
for window in day:
if window[1] >= latestTime:
latestTime = window[1]
timeWindowsAndPriorities.append([latestTime, task.priority, task.id, float(task.priority)/max(float(task.getNumTimeWindows()), 1), task.dependencyTasks])
# sorting by deadline
timeWindowsAndPriorities = orderMethod(timeWindowsAndPriorities)
# sorting by priority
taskOrdering = []
for timeTaskTuple in timeWindowsAndPriorities:
taskOrdering.append(timeTaskTuple[2])
return helperFunctions.createOptimalSchedule(taskList, taskOrdering)
def runIntegerProgram(csvFile, timeLimit=-1, outputFlag=0):
taskList = createTasksFromCsv.getTaskList(csvFile)
helperFunctions.preprocessTimeWindows(taskList)
schedule, isOptimal = integerProgramSolve(taskList, timeLimit, outputFlag)
# assert(isFeasible(taskList, schedule))
return schedule, isOptimal
def runGreedyByOrder(csvFile, orderMethod):
taskList = createTasksFromCsv.getTaskList(csvFile)
helperFunctions.preprocessTimeWindows(taskList)
# create a list of tuples (time of latest time window ending, taskId)
timeWindowsAndPriorities = []
for task in taskList:
latestTime = 0
for day in task.timeWindows:
for window in day:
if window[1] >= latestTime:
latestTime = window[1]
timeWindowsAndPriorities.append([
latestTime, task.priority, task.id,
float(task.priority) / max(float(task.getNumTimeWindows()), 1),
task.dependencyTasks
])
# sorting by deadline
timeWindowsAndPriorities = orderMethod(timeWindowsAndPriorities)
# sorting by priority
taskOrdering = []
for timeTaskTuple in timeWindowsAndPriorities:
taskOrdering.append(timeTaskTuple[2])
return helperFunctions.createOptimalSchedule(taskList, taskOrdering)
def makeSchedule(taskList):
fullTaskList = copy.deepcopy(taskList)
helperFunctions.preprocessTimeWindows(taskList)
schedule = Schedule()
lastDay = len(taskList[0].timeWindows)
for day in range(lastDay):
schedule.append(Route())
lastTimeWindowEndings = [0 for i in range(lastDay + 1)]
for task in taskList:
for index, day in enumerate(task.timeWindows):
for timeWindow in day:
if timeWindow[1] > lastTimeWindowEndings[index]:
lastTimeWindowEndings[index] = timeWindow[1]
insertionSuccesful = True
while insertionSuccesful:
insertionSuccesful = False
bestTaskInfo = (None, None, None, None)
bestTask = None
for task in taskList:
taskInfo = helperFunctions.isTaskInsertable(schedule, task, lastTimeWindowEndings, fullTaskList)
# if task can be finished earliest
if (taskInfo[2] != None and (bestTaskInfo[2] == None or taskInfo[2] < bestTaskInfo[2])):
bestTaskInfo = taskInfo
bestTask = task
if (bestTaskInfo != None and bestTaskInfo[2] != None):
insertionSuccesful = True
isInsertable, endingTime, endingDay, insertPosition = bestTaskInfo
schedule.routeList[endingDay].taskList.insert(insertPosition, bestTask)
schedule.routeList[endingDay].endingTimes.insert(insertPosition, endingTime)
for index, task in enumerate(taskList):
if (task.id == bestTask.id):
taskList.pop(index)
return schedule
def runGreedyConstructiveHeuristic(csvFile):
unscheduledTaskList = createTasksFromCsv.getTaskList(csvFile)
helperFunctions.preprocessTimeWindows(unscheduledTaskList)
fullTaskList = copy.deepcopy(unscheduledTaskList)
avgDistance = findAvgDistance(fullTaskList)
avgPriority = findAvgPriority(fullTaskList)
avgDuration = findAvgDuration(fullTaskList)
priorityWeight = avgPriority / (avgDistance + avgDuration)
schedule = Schedule()
numDays = len(fullTaskList[0].timeWindows)
for day in range(numDays):
route = Route()
schedule.append(route)
for task in unscheduledTaskList:
print "one task down!"
schedule, taskToRemove = returnScheduleInsertedWithBestTask(
schedule, unscheduledTaskList, fullTaskList, priorityWeight)
# we have to do this because remove doesn't use equality testing,
# so it doesn't know tasks in the deepcopy are the same
for task in unscheduledTaskList:
if task == taskToRemove:
unscheduledTaskList.remove(task)
break
# taskList.remove(taskToRemove) # I don't know if I can actually do this....
return schedule
def findBestSchedule(taskList, permutations):
helperFunctions.preprocessTimeWindows(taskList)
bestSchedule = Objects.Schedule()
for perm in permutations:
newSchedule = helperFunctions.createOptimalSchedule(taskList, perm)
if isBetterSchedule(newSchedule, bestSchedule):
bestSchedule = newSchedule
return bestSchedule
def findBestSchedule(taskList, permutations):
helperFunctions.preprocessTimeWindows(taskList)
bestSchedule = Objects.Schedule()
for perm in permutations:
newSchedule = helperFunctions.createOptimalSchedule(taskList, perm)
if isBetterSchedule(newSchedule, bestSchedule):
bestSchedule = newSchedule
return bestSchedule
def findBestScheduleWithTimeLimit(taskList, permutations, timeLimit):
'''
A function given a list of task objects and all the potential task permutations
will create a schedule for each ordering and output one of the best schedules in the
time it has that is has tried.
'''
start = time.time()
helperFunctions.preprocessTimeWindows(taskList)
bestSchedule = Objects.Schedule()
for perm in permutations:
newSchedule = helperFunctions.createOptimalSchedule(taskList, perm)
if isBetterSchedule(newSchedule, bestSchedule):
bestSchedule = newSchedule
if time.time() - start >= timeLimit:
return bestSchedule
return bestSchedule
def randomlyPickBestScheduleUnderTime(csvFile, numSeconds):
taskList = createTasksFromCsv.getTaskList(csvFile)
helperFunctions.preprocessTimeWindows(taskList)
bestSchedule, bestProfit = None, 0
taskIds = [task.id for task in taskList]
start = time.time()
while time.time() < (start + numSeconds):
# Get a random permutation
orderToTry = random.sample(taskIds, len(taskIds))
schedule = helperFunctions.createOptimalSchedule(taskList, orderToTry)
if schedule.getProfit() > bestProfit:
bestProfit = schedule.getProfit()
bestSchedule = schedule
return bestSchedule
def randomlyPickBestScheduleUnderTime(csvFile, numSeconds):
taskList = createTasksFromCsv.getTaskList(csvFile)
helperFunctions.preprocessTimeWindows(taskList)
bestSchedule, bestProfit = None, 0
taskIds = [task.id for task in taskList]
start = time.time()
while time.time() < (start + numSeconds):
# Get a random permutation
orderToTry = random.sample(taskIds, len(taskIds))
schedule = helperFunctions.createOptimalSchedule(taskList, orderToTry)
if schedule.getProfit() > bestProfit:
bestProfit = schedule.getProfit()
bestSchedule = schedule
return bestSchedule
def findBestScheduleWithTimeLimit(taskList, permutations, timeLimit):
'''
A function given a list of task objects and all the potential task permutations
will create a schedule for each ordering and output one of the best schedules in the
time it has that is has tried.
'''
start = time.time()
helperFunctions.preprocessTimeWindows(taskList)
bestSchedule = Objects.Schedule()
for perm in permutations:
newSchedule = helperFunctions.createOptimalSchedule(taskList, perm)
if isBetterSchedule(newSchedule, bestSchedule):
bestSchedule = newSchedule
if time.time() - start >= timeLimit:
return bestSchedule
return bestSchedule