def h(S,completion,items,lambda1,lambda2): # redefine the contribution of one item for the obj function n = len(items) lateness = generate.late(completion,items) value = [None]*n Rb,c_max = generate.balance_rate(completion,S) Ru = generate.idle_rate(items,completion,c_max,S) for s in S: l = S.index(s) for j in s: value[j] = lambda1*(math.fabs(items[j].wt*lateness[j]))/Ru[l] + lambda2*items[j].wc*completion[j]*math.exp(-Rb) return value
def value_generator(S,items,lambda1,lambda2): completion = complete_time(S,items) lateness = generate.late(completion,items) tardiness = generate.tard(lateness) item_values = [] for j in xrange(len(items)): item = items[j] wt,wc = item.wt,item.wc t,c = tardiness[j],completion[j] value = h(t,c,wt,wc,lambda1,lambda2) item_values.append(value) return completion,tardiness,item_values
def h(S, completion, items, lambda1,
lambda2): # redefine the contribution of one item for the obj function
n = len(items)
lateness = generate.late(completion, items)
value = [None] * n
Rb, c_max = generate.balance_rate(completion, S)
Ru = generate.idle_rate(items, completion, c_max, S)
for s in S:
l = S.index(s)
for j in s:
value[j] = lambda1 * (math.fabs(items[j].wt * lateness[j])) / Ru[
l] + lambda2 * items[j].wc * completion[j] * math.exp(-Rb)
return value
def value_generator(S, items, lambda1, lambda2): # generate completion, tardniess, and item contribution values
completion = complete_time(S, items)
item = [items[j] for j in S]
lateness = generate.late(completion, item)
tardiness = generate.tard(lateness)
item_values = []
for j in xrange(len(item)):
itm = item[j]
wt, wc = itm.wt, itm.wc
t, c = tardiness[j], completion[j]
value = h(t, c, wt, wc, lambda1, lambda2)
item_values.append(value)
return completion, tardiness, item_values
def solve(input_data, N, NL, m, lambda1):
lambda2 = 1 - lambda1
Data = input_data.split("\n") # load data
n = len(Data) - 1 # get the amount of items
items = []
for j in xrange(n):
data = Data[j]
parts = data.split()
p = int(parts[0]) # get the process time
s = int(parts[2]) # get the setup time
d = int(parts[3]) # get the due date
wt = int(parts[4]) # get the tardiness weights
wc = int(parts[5]) # get the completion weights
items.append(Item(p + s, d, wt, wc)) # combine those item data
print "Data loaded!"
S, L, completion = generate.initialization(items, n, m)
lateness = generate.late(completion, items)
tardiness = generate.tard(lateness)
item_values = []
for j in xrange(len(items)):
item = items[j]
wt, wc = item.wt, item.wc
t, c = tardiness[j], completion[j]
value = h(t, c, wt, wc, lambda1, lambda2)
item_values.append(value)
print "Initialization done!"
G = generate.H(item_values, L)
line_values = []
for s in S:
value = generate.H(item_values, s)
line_values.append(value)
print "Initial values done!"
for l in xrange(m):
delta, S[l] = basictabu.tabu(1500, NL, S[l], items, completion, tardiness, lambda1, lambda2)
G += delta
line_values[l] += delta
completion, tardiness, item_values = generate.verify(S, items, lambda1, lambda2)
print "Initial Tabu Search Done!"
delta, S, L, line_values, item_values, completion, tardiness = tabu(
N, NL, S, L, items, completion, tardiness, line_values, item_values, G, lambda1, lambda2
)
G += delta
completion, tardiness, item_values = generate.verify(S, items, lambda1, lambda2)
u = tardiness.count(0)
cv = u / len(tardiness)
print tardiness
return G, cv, S
def solve(input_data,m,lambda1,NR):
lambda2 = 1- lambda1
Data = input_data.split('\n') # load data
n = len(Data) -1 # get the amount of items
items = []
for j in xrange(n):
data = Data[j]
parts = data.split()
p = int(parts[0]) # get the process time
s = int(parts[2]) # get the setup time
d = int(parts[3]) # get the due date
wt = int(parts[4]) # get the tardiness weights
wc = int(parts[5]) # get the completion weights
items.append(Item(p+s,d,wt,wc)) # combine those item data
print 'Data loaded!'
S,L,completion = generate.initialization(items,n,m)
print 'Initialization done!'
lateness = generate.late(completion,items) # begin the value initialization
tardiness = generate.tard(lateness)
item_values = []
for j in xrange(n):
item = items[j]
wt,wc = item.wt,item.wc
t,c = tardiness[j],completion[j]
value = h(t,c,wt,wc,lambda1,lambda2)
item_values.append(value)
G = generate.H(item_values,L)
line_values = []
for s in S:
value = generate.H(item_values,s)
line_values.append(value)
print 'Initial values done!'
for k in xrange(NR):
l_p,l_m = generate.reorder(items,S,line_values,item_values)
S[l_p],c_p = ATC(items,S[l_p])
S[l_m],c_m = ATC(items,S[l_m])
for j in S[l_p]:
completion[j] = c_p.pop(0)
c = completion[j]
item = items[j]
wt,wc = item.wt,item.wc
late = completion[j] - item.due
t = generate.tard(late)
item_values[j] = h(t[0],c,wt,wc,lambda1,lambda2)
for j in S[l_m]:
completion[j] = c_m.pop(0)
c = completion[j]
item = items[j]
wt,wc = item.wt,item.wc
late = completion[j] - item.due
t = generate.tard(late)
item_values[j] = h(t[0],c,wt,wc,lambda1,lambda2)
delta_p = generate.H(item_values,S[l_p]) - line_values[l_p]
delta_m = generate.H(item_values,S[l_m]) - line_values[l_m]
line_values[l_p] += delta_p
line_values[l_m] += delta_m
G = G + delta_m + delta_p
lateness = generate.late(completion,items)
tardiness = generate.tard(lateness)
u = tardiness.count(0)
cv = u/len(tardiness)
return G,cv,S
def solve(input_data, m, lambda1, NR):
lambda2 = 1 - lambda1
Data = input_data.split('\n') # load data
n = len(Data) - 1 # get the amount of items
items = []
for j in xrange(n):
data = Data[j]
parts = data.split()
p = int(parts[0]) # get the process time
s = int(parts[2]) # get the setup time
d = int(parts[3]) # get the due date
wt = int(parts[4]) # get the tardiness weights
wc = int(parts[5]) # get the completion weights
items.append(Item(p + s, d, wt, wc)) # combine those item data
print 'Data loaded!'
S, L, completion = generate.initialization(items, n, m)
print 'Initialization done!'
lateness = generate.late(completion,
items) # begin the value initialization
tardiness = generate.tard(lateness)
item_values = []
for j in xrange(n):
item = items[j]
wt, wc = item.wt, item.wc
t, c = tardiness[j], completion[j]
value = h(t, c, wt, wc, lambda1, lambda2)
item_values.append(value)
G = generate.H(item_values, L)
line_values = []
for s in S:
value = generate.H(item_values, s)
line_values.append(value)
print 'Initial values done!'
for k in xrange(NR):
l_p, l_m = generate.reorder(items, S, line_values, item_values)
S[l_p], c_p = ATC(items, S[l_p])
S[l_m], c_m = ATC(items, S[l_m])
for j in S[l_p]:
completion[j] = c_p.pop(0)
c = completion[j]
item = items[j]
wt, wc = item.wt, item.wc
late = completion[j] - item.due
t = generate.tard(late)
item_values[j] = h(t[0], c, wt, wc, lambda1, lambda2)
for j in S[l_m]:
completion[j] = c_m.pop(0)
c = completion[j]
item = items[j]
wt, wc = item.wt, item.wc
late = completion[j] - item.due
t = generate.tard(late)
item_values[j] = h(t[0], c, wt, wc, lambda1, lambda2)
delta_p = generate.H(item_values, S[l_p]) - line_values[l_p]
delta_m = generate.H(item_values, S[l_m]) - line_values[l_m]
line_values[l_p] += delta_p
line_values[l_m] += delta_m
G = G + delta_m + delta_p
lateness = generate.late(completion, items)
tardiness = generate.tard(lateness)
u = tardiness.count(0)
cv = u / len(tardiness)
return G, cv, S
