def CheckValidSolution(solution, data):
for nurse in solution:
schedule = nurse['schedule']
# Try to fill all schedule
valid = fillScheduleFunctions.FillSchedule(nurse, data)
if (valid == False):
return False
#Max presence
presence = nurse['end'] - nurse['ini'] + 1
if (presence > data.maxPresence):
return False
#Max hours
if (nurse['workingHours'] > data.maxHours):
return False
#Min hours
if (nurse['workingHours'] < data.minHours):
return False
#Max consec
if (checkFunctions.CheckMaxConsecutiveHours(nurse['schedule'],
data.maxConsec) == False):
return False
#Check rest
if (checkFunctions.CheckRest(nurse['schedule']) == False):
return False
return True
def construct(E, alpha, data, seed):
random.seed(seed)
# Initialize all schedules to no working
solution = list(
map(
lambda x: {
'schedule': np.zeros(data.nHours),
'workingHours': 0,
'ini': data.nHours,
'end': 0
}, [None] * data.nNurses))
timegreedying = 0
timeRCL = 0
timeUpdate = 0
while E:
t_start = time.time()
#Compute greedy cost
greedy_cost = compute_greedy_cost(E, solution, data)
t_greedy = time.time()
#RCL
max_cost = max(greedy_cost)
min_cost = min(greedy_cost)
threshold = min_cost + alpha * (max_cost - min_cost)
RCL = [e for (e, cost) in zip(E, greedy_cost) if cost >= threshold]
pick = random.randint(0, len(RCL) - 1)
pickNurse = RCL[pick][0]
pickHour = RCL[pick][1]
#Set nurse-hour to work
SetWorkingHour(pickHour, solution[pickNurse], data)
t_rcl = time.time()
# Update candidates
E = update(E, solution, pickNurse, data)
t_update = time.time()
timegreedying += t_greedy - t_start
timeRCL += t_rcl - t_greedy
timeUpdate += t_update - t_rcl
# Remove unused nurses
solution = [n for n in solution if n['workingHours'] > 0]
#Fill schedules (to guarantee all constrains (minHours, rest,...))
valid = FillAllSchedules(solution, data)
#Check all constrains
if valid == True:
valid = Check.CheckAllConstrains(solution, data, False)
if valid == False:
return None, False
return solution, True
obj_hist = [
0
] * MAXITER #records objective function value of the best so far
data = DataClass(sourcedata)
E = get_solution_elements(data)
t_begin = time.time()
for i in range(MAXITER):
sol, valid = construct(E, ALPHA, data, seeds[i])
if valid:
sol = local_search(sol, data)
obj = compute_obj(sol)
#Check all constrains
valid = Check.CheckAllConstrains(sol, data, True)
if valid == True and obj < obj_best:
sol_best = sol
obj_best = obj
obj_hist[i] = obj_best
print "It" + str(i) + "(" + str(obj_best) + ")",
print ""
t_end = time.time()
#print solution obtained
#print('---------- SOLUTION -----------')
print(' - Total time: {:.2f}s'.format(t_end - t_begin))
if not sol_best:
print(' - Nurses: Could not find any feasible solution')
else:
bestIndividual = brkga.getBestFitness(population)
#Print some results
print('Fitness: ' + str(bestIndividual['fitness']) + ' nNurses: ' +
str(len(bestIndividual['solution'])))
print('Time solving: ' + str(t_exec) + ' sec')
plt.plot(evol)
plt.xlabel('number of generations')
plt.ylabel('Fitness of best individual')
plt.axis([0, len(evol), 0, 1 + int(data['nNurses'])])
plt.show()
print("nNurses : " + str(bestIndividual['fitness']))
#Check all constrains
if Check.CheckDemand(bestIndividual['solution'], data['demand']) == False:
print(
'//////////////########################## ERROR DEMAND ########################'
)
i = 1
for nurse in bestIndividual['solution']:
if Check.CheckMaxConsecutiveHours(nurse['schedule'],
data['maxConsec']) == False:
print(
'//////////////########################## ERROR MAXCONSEC ########################'
)
if Check.CheckMaxPresence(nurse['schedule'],
data['maxPresence']) == False:
print(