def gene_alg(timelimit,
avaliable_sol,
fix,
gen,
per_month_dir=tl.DIR_PER_MONTH,
fixed_dir=tl.DIR_PARA + 'fixed/',
posibility=0.05): #avaliavle_sol 可行解列表 fix 不能移動的列表
if platform.system() == "Linux":
os.system("g++ -o ./tool/c++/score_linux ./tool/c++/score.cpp")
main = "./tool/c++/score_linux "
print("使用的作業系統為：Linux")
elif platform.system() == "Darwin":
os.system("g++ -o ./tool/c++/score_mac ./tool/c++/score.cpp")
main = "./tool/c++/score_mac "
print("使用的作業系統為：Mac")
elif platform.system() == "Windows":
print("使用的作業系統為：Windows")
os.system("g++ -o ./tool/c++/score.exe ./tool/c++/score.cpp")
try:
open('./tool/c++/score.exe', 'r')
except FileNotFoundError:
ERROR('找不到score.exe檔案，請用C++編譯生成一個執行檔。')
director = str(os.getcwd()).replace("\\", "/")
main = director + "/tool/c++/score.exe "
A_t = pd.read_csv(fixed_dir + 'fix_class_time.csv', header=0, index_col=0)
EMPLOYEE_t = tl.Employee_t
Shift_name = tl.CLASS_list
nightdaylimit = EMPLOYEE_t['night_perWeek']
year = tl.YEAR
month = tl.MONTH
nEMPLOYEE = tl.nE
nDAY = tl.nD
nK = tl.nK
nT = tl.nT
nR = tl.nR
nW = tl.nW
mDAY = tl.mDAY
DEMAND = tl.DEMAND
P0, P1, P2, P3, P4 = tl.P
SHIFTset = tl.K_CLASS_set
s_break = tl.K_BREAK_set
DAY = [tmp for tmp in range(nDAY)] #DAY - 日子集合，J=0,…,nJ-1
DATES = tl.DATE_list #所有的日期 - 對照用
D_WEEK = tl.D_WEEK_set #D_WEEK - 第 w 週中所包含的日子集合
WEEK_of_DAY = tl.WEEK_list #WEEK_of_DAY - 日子j所屬的那一週\
#1
A_t_s = ""
for i in A_t.values.tolist():
for j in i:
A_t_s += str(j)
A_t_s += ","
A_t_s += "!"
main += A_t_s
main += " "
#2
Shift_name_s = ""
for i in Shift_name:
Shift_name_s += i
Shift_name_s += ","
main += Shift_name_s
main += " "
#3
nightdaylimit_s = ""
for i in nightdaylimit.values.tolist():
nightdaylimit_s += str(i)
nightdaylimit_s += ","
main += nightdaylimit_s
main += " "
#4
year_s = str(year)
month_s = str(month)
nEMPLOYEE_s = str(nEMPLOYEE)
nDAY_s = str(nDAY)
nK_s = str(nK)
nT_s = str(nT)
nR_s = str(nR)
nW_s = str(nW)
mDAY_s = str(mDAY)
main += year_s
main += " "
main += month_s
main += " "
main += nEMPLOYEE_s
main += " "
main += nDAY_s
main += " "
main += nK_s
main += " "
main += nT_s
main += " "
main += nR_s
main += " "
main += nW_s
main += " "
main += mDAY_s
main += " "
#13
DEMAND_s = ""
for i in DEMAND:
for j in i:
DEMAND_s += str(j)
DEMAND_s += ","
DEMAND_s += "!"
main += DEMAND_s
main += " "
#14
P0_s = str(P0)
P1_s = str(P1)
P2_s = str(P2)
P3_s = str(P3)
P4_s = str(P4)
main += P0_s
main += " "
main += P1_s
main += " "
main += P2_s
main += " "
main += P3_s
main += " "
main += P4_s
main += " "
#19
all_s = ""
morning_s = ""
noon_s = ""
night_s = ""
phone_s = ""
other_s = ""
for i in SHIFTset['all']:
all_s += str(i)
all_s += ","
for i in SHIFTset['morning']:
morning_s += str(i)
morning_s += ","
for i in SHIFTset['noon']:
noon_s += str(i)
noon_s += ","
for i in SHIFTset['night']:
night_s += str(i)
night_s += ","
for i in SHIFTset['phone']:
phone_s += str(i)
phone_s += ","
for i in SHIFTset['other']:
other_s += str(i)
other_s += ","
main += all_s
main += " "
main += morning_s
main += " "
main += noon_s
main += " "
main += night_s
main += " "
main += phone_s
main += " "
main += other_s
main += " "
#25
s_break_s = ""
for i in s_break:
for j in i:
s_break_s += str(j)
s_break_s += ","
s_break_s += "!"
main += s_break_s
main += " "
#26
DAY_s = ""
for i in DAY:
DAY_s += str(i)
DAY_s += ","
main += DAY_s
main += " "
#27
DATES_s = ""
for i in DATES:
DATES_s += str(i)
DATES_s += ","
main += DATES_s
main += " "
#28
D_WEEK_s = ""
for i in D_WEEK:
for j in i:
D_WEEK_s += str(j)
D_WEEK_s += ","
if len(i) == 0:
D_WEEK_s += ","
D_WEEK_s += "!"
main += D_WEEK_s
main += " "
#29
WEEK_of_DAY_s = ""
for i in WEEK_of_DAY:
WEEK_of_DAY_s += str(i)
WEEK_of_DAY_s += ","
main += WEEK_of_DAY_s
main += " "
#print(main)
shiftset = []
shiftset.extend(SHIFTset['phone'])
for i in SHIFTset['not_assigned']:
if i in shiftset:
shiftset.remove(i)
print('per_month_dir =', per_month_dir)
i_nb = []
tStart = time.time() #紀錄演算法開始的時間
for p in range(len(avaliable_sol)):
#i_nb.append(np.vectorize({v: k for k, v in K_type_dict.items()}.get)(np.array(avaliable_sol[p])).tolist())
i_nb.append(avaliable_sol[p])
score_liz = []
gene_log = []
for i, j in zip(i_nb, fix):
score_liz.append((i, j, score(i, main)))
score_liz.sort(key=lambda s: s[2])
for i in range(gen): #重複指定的次數
if time.time() - tStart > timelimit: #如果時間已到，就跳出
print('限制時間已至，於第', i, '世代跳出')
break
score_liz = alg(score_liz, main, nDAY, nEMPLOYEE, shiftset, posibility)
if i % 100 == 0:
print('\n第', i + 1, '世代最佳分數：', score_liz[0][2], ' Time: ',
int(time.time() - tStart), 's')
else:
sys.stdout.write("\r(" + str(float((i) * 100 / (gen))) + "%)")
sys.stdout.flush()
gene_log.append([i + 1, time.time() - tStart, score_liz[0][2]])
gene_log = pd.DataFrame(np.array(gene_log),
columns=['generation', 'time', 'score'])
gene_log.to_csv('gene_log.csv')
result = score_liz[0][0]
print('\n\n基因演算法最佳解：', score_liz[0][2])
return result
def alg(score_liz, main, nDAY, nEMPLOYEE, shiftset, posibility=0.05):
org_len = len(score_liz)
#sort = sorted(score_liz, key = lambda s: s[2]) #親代排名
new = np.copy(score_liz[:int(len(score_liz) / 3)]) #取出前1/3
num_list = list(range(len(new)))
random.shuffle(num_list)
#print(num_list[0],num_list[1], end=' ')
union = np.logical_or(new[num_list[0]][1], new[num_list[1]][1])
one_not_avb = union * new[num_list[0]][0]
one_avb = new[num_list[0]][0] - one_not_avb
two_not_avb = union * new[num_list[1]][0]
two_avb = new[num_list[1]][0] - two_not_avb
one_org = np.array(new[num_list[0]][0]) #沒有fix的班表
two_org = np.array(new[num_list[1]][0])
#隨機決定切分點
sp_row = random.randint(0, nDAY - 1)
sp_col = random.randint(0, nEMPLOYEE - 1)
#第一組：依據員工、日期切分
one_col_left = one_avb[:sp_col]
one_col_right = one_avb[sp_col:]
one_row_up = one_avb.T[:sp_row].T
one_row_down = one_avb.T[sp_row:].T
one_org_col_left = one_org[:sp_col]
one_org_col_right = one_org[sp_col:]
one_org_row_up = one_org.T[:sp_row].T
one_org_row_down = one_org.T[sp_row:].T
#第二組：的切分
two_col_left = two_avb[:sp_col]
two_col_right = two_avb[sp_col:]
two_row_up = two_avb.T[:sp_row].T
two_row_down = two_avb.T[sp_row:].T
two_org_col_left = two_org[:sp_col]
two_org_col_right = two_org[sp_col:]
two_org_row_up = two_org.T[:sp_row].T
two_org_row_down = two_org.T[sp_row:].T
#將對應的一、二組片段重新組合
#上下黏合
a_one_one_two = np.concatenate(
(one_row_up, two_row_down), axis=1) + one_not_avb
a_two_one_two = np.concatenate(
(one_row_up, two_row_down), axis=1) + two_not_avb
a_one_two_one = np.concatenate(
(two_row_up, one_row_down), axis=1) + one_not_avb
a_two_two_one = np.concatenate(
(two_row_up, one_row_down), axis=1) + two_not_avb
a_org_one_two = np.concatenate((one_org_row_up, two_org_row_down), axis=1)
a_org_two_one = np.concatenate((two_org_row_up, one_org_row_down), axis=1)
#左右黏合
b_one_one_two = np.concatenate(
(one_col_left, two_col_right), axis=0) + one_not_avb
b_two_one_two = np.concatenate(
(one_col_left, two_col_right), axis=0) + two_not_avb
b_one_two_one = np.concatenate(
(two_col_left, one_col_right), axis=0) + one_not_avb
b_two_two_one = np.concatenate(
(two_col_left, one_col_right), axis=0) + two_not_avb
b_org_one_two = np.concatenate((one_org_col_left, two_org_col_right),
axis=0)
b_org_two_one = np.concatenate((two_org_col_left, one_org_col_right),
axis=0)
range_num = int(1 / posibility) - 1
#突變
if random.randint(0, range_num) == 0:
a_one_one_two[random.randint(
0, a_one_one_two.shape[0] - 1)][random.randint(
0, a_one_one_two.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
a_two_one_two[random.randint(
0, a_two_one_two.shape[0] - 1)][random.randint(
0, a_two_one_two.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
a_one_two_one[random.randint(
0, a_one_two_one.shape[0] - 1)][random.randint(
0, a_one_two_one.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
a_two_two_one[random.randint(
0, a_two_two_one.shape[0] - 1)][random.randint(
0, a_two_two_one.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
a_org_one_two[random.randint(
0, a_org_one_two.shape[0] - 1)][random.randint(
0, a_org_one_two.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
a_org_two_one[random.randint(
0, a_org_two_one.shape[0] - 1)][random.randint(
0, a_org_two_one.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
b_one_one_two[random.randint(
0, b_one_one_two.shape[0] - 1)][random.randint(
0, b_one_one_two.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
b_two_one_two[random.randint(
0, b_two_one_two.shape[0] - 1)][random.randint(
0, b_two_one_two.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
b_one_two_one[random.randint(
0, b_one_two_one.shape[0] - 1)][random.randint(
0, b_one_two_one.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
b_two_two_one[random.randint(
0, b_two_two_one.shape[0] - 1)][random.randint(
0, b_two_two_one.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
b_org_one_two[random.randint(
0, b_org_one_two.shape[0] - 1)][random.randint(
0, b_org_one_two.shape[1] - 1)] = random.choice(shiftset)
if random.randint(0, range_num) == 0:
b_org_two_one[random.randint(
0, b_org_two_one.shape[0] - 1)][random.randint(
0, b_org_two_one.shape[1] - 1)] = random.choice(shiftset)
#print(np.zeros(a_org_one_two.shape))
#判斷是否符合
if confirm(a_one_one_two) == 'All constraints are met.':
#print('a_one_one_two',a_one_one_two)
score_liz.append((a_one_one_two, new[num_list[0]][1],
score(a_one_one_two.tolist(), main)))
#print(score(a_one_one_two.tolist(), main))
if confirm(a_two_one_two) == 'All constraints are met.':
#print('a_two_one_two',a_two_one_two)
score_liz.append((a_two_one_two, new[num_list[1]][1],
score(a_two_one_two.tolist(), main)))
#print(score(a_two_one_two.tolist(), main))
if confirm(a_one_two_one) == 'All constraints are met.':
#print('a_one_two_one',a_one_two_one)
score_liz.append((a_one_two_one, new[num_list[0]][1],
score(a_one_two_one.tolist(), main)))
#print(score(a_one_two_one.tolist(), main))
if confirm(a_two_two_one) == 'All constraints are met.':
#print('a_two_two_one',a_two_two_one)
score_liz.append((a_two_two_one, new[num_list[1]][1],
score(a_two_two_one.tolist(), main)))
#print(score(a_two_two_one.tolist(), main))
if confirm(a_org_one_two) == 'All constraints are met.':
#print('a_org_one_two',a_org_one_two)
score_liz.append((a_org_one_two, np.zeros(a_org_one_two.shape),
score(a_org_one_two.tolist(), main)))
#print(score(a_org_one_two.tolist(), main))
if confirm(a_org_two_one) == 'All constraints are met.':
#print('a_org_two_one',a_org_two_one)
score_liz.append((a_org_two_one, np.zeros(a_org_two_one.shape),
score(a_org_two_one.tolist(), main)))
#print(score(a_org_two_one.tolist(), main))
if confirm(b_one_one_two) == 'All constraints are met.':
#print('b_one_one_two',b_one_one_two)
score_liz.append((b_one_one_two, new[num_list[0]][1],
score(b_one_one_two.tolist(), main)))
#print(score(b_one_one_two.tolist(), main))
if confirm(b_two_one_two) == 'All constraints are met.':
#print('b_two_one_two',b_two_one_two)
score_liz.append((b_two_one_two, new[num_list[1]][1],
score(b_two_one_two.tolist(), main)))
#print(score(b_two_one_two.tolist(), main))
if confirm(b_one_two_one) == 'All constraints are met.':
#print('b_one_two_one',b_one_two_one)
score_liz.append((b_one_two_one, new[num_list[0]][1],
score(b_one_two_one.tolist(), main)))
#print(score(b_one_two_one.tolist(), main))
if confirm(b_two_two_one) == 'All constraints are met.':
#print('b_two_two_one',b_two_two_one)
score_liz.append((b_two_two_one, new[num_list[1]][1],
score(b_two_two_one.tolist(), main)))
#print(score(b_two_two_one.tolist(), main))
if confirm(b_org_one_two) == 'All constraints are met.':
#print('b_org_one_two',b_org_one_two)
score_liz.append((b_org_one_two, np.zeros(b_org_one_two.shape),
score(b_org_one_two.tolist(), main)))
#print(score(b_org_one_two.tolist(), main))
if confirm(b_org_two_one) == 'All constraints are met.':
#print('b_org_two_one',b_org_two_one)
score_liz.append((b_org_two_one, np.zeros(b_org_two_one.shape),
score(b_org_two_one.tolist(), main)))
#print(score(b_org_two_one.tolist(), main))
# sort = sorted(sort, key = lambda s: s[2],reverse = True)
#sort = sorted(sort, key = lambda s: s[2])
score_liz.sort(key=lambda s: s[2])
#print(len(sort))
#score_liz = score_liz[:len(score_liz)]
#print(sort)
#print(len(sort))
return score_liz[:org_len]