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import pandas as pd
data = pd.read_csv("附件1：订单信息.csv")
def stringToNum(x):
return int(x[1:])
OrderNo = list(map(stringToNum, data["OrderNo"]))
ItemNo = list(map(stringToNum, data["ItemNo"]))
OrderObject = []
index = 0
for i in range(0, 20340):
if OrderNo[i] > index:
OrderObject.append([])
index += 1
OrderObject[index-1].append(ItemNo[i])
OrderLen = []
for k,v in enumerate(OrderObject):
OrderLen.append([k, len(v)])
OrderLen.sort(key=lambda item:item[1], reverse=True)
def getFitness(i, j):
item1 = OrderObject[OrderLen[i][0]]
item2 = OrderObject[OrderLen[j][0]]
tmp = set(item1)
tmp.update(item2)
return 1-(len(tmp)-len(item1))/len(item2)
for i in range(len(OrderLen)-1):
maxfitindex = i+1
maxfit = 0
for j in range(i+1, len(OrderLen)):
fitness = getFitness(i, j)
if fitness > maxfit:
maxfitindex = j
maxfit = fitness
tmp = OrderLen[i+1]
OrderLen[i+1] = OrderLen[maxfitindex]
OrderLen[maxfitindex] = tmp
len(OrderLen)
GroupNo = []
for orderInf in iter(OrderLen):
overIndex = -1
overDelta = orderInf[1]
overSet = set()
product = set(OrderObject[orderInf[0]])
cache = set()
for i, j in enumerate(GroupNo):
cache = product.copy()
cache.update(j[0])
if len(cache) > 200:
continue
if len(cache)-len(j[0])==0:
overIndex = i
overSet = cache
break
if len(cache)-len(j[0]) <= overDelta:
overIndex = i
overDelta = len(cache)-len(j[0])
overSet = cache
continue
if overIndex == -1:
GroupNo.append([product, [orderInf[0]]])
else:
GroupNo[overIndex][0] = overSet
GroupNo[overIndex][1].append(orderInf[0])
print(len(GroupNo))
res1 = []
for k, v in enumerate(GroupNo):
for i in iter(v[1]):
res1.append([i+1,k+1])
len(res1)
def NumToStringD(x):
return ["D{0:0>4}".format(x[0]), x[1]]
res1.sort(key=lambda item: item[0])
result1 = list(map(NumToStringD, res1))
result1 = pd.DataFrame(result1, columns=['OrderNo','GroupNo']);
result1.to_csv("result1.csv", index=False)
ProductOrderList = []
DistanceList = []
def sumDistance(TargetSort, NormOrder):
sum = 0
for item in iter(NormOrder):
if len(item) == 1:
continue
min, max = 0, len(TargetSort)-1
for k, v in enumerate(TargetSort):
if v in item:
min = k
break
for k, v in enumerate(reversed(TargetSort)):
if v in item:
max = len(TargetSort)-k-1
break
sum += max-min
return sum
for temp in GroupNo:
N = len(temp[0])
NormOrder = []
ProductList = list(temp[0])
for k, v in enumerate(temp[1]):
NormOrder.append([])
for i in iter(OrderObject[v]):
NormOrder[k].append(ProductList.index(i))
TargetSort = list(range(0, N))
currentSum = sumDistance(TargetSort, NormOrder)
for i in range(0, N):
for j in range(i+1, N):
tmp = TargetSort[j]
TargetSort[j] = TargetSort[i]
TargetSort[i] = tmp
tmpSum = sumDistance(TargetSort, NormOrder)
if tmpSum < currentSum:
currentSum = tmpSum
continue
elif tmpSum == currentSum:
if j-i < (N-i)/1:
continue
else:
break
else:
tmp = TargetSort[j]
TargetSort[j] = TargetSort[i]
TargetSort[i] = tmp
if j-i < (N-i)/1:
continue
else:
break
DistanceList.append(currentSum)
ProductOrderList.append(list(map(lambda ind: ProductList[ind],TargetSort)))
res2 = []
for k, v in enumerate(ProductOrderList):
for i, j in enumerate(v):
res2.append([j, k+1, i+1])
print(len(res2))
sum(DistanceList)
def NumToStringP(x):
return ["P{0:0>4}".format(x[0]), x[1], x[2]]
result2 = list(map(NumToStringP, res2))
result2 = pd.DataFrame(result2, columns=['ItemNo','GroupNo','ShelfNo']);
result2.to_csv("result2.csv", index=False)
TaskList = []
TimeList = []
def sumDeltaDistance(pos, Product, CurrentOrder):
min, max = 0, len(Product)-1
for k, v in enumerate(Product):
if v in CurrentOrder:
min = k
break
for k, v in enumerate(reversed(Product)):
if v in CurrentOrder:
max = len(Product)-k-1
break
if pos <= min:
return max-min, max
elif pos >= max:
return max-min, min
else:
if pos - min < max - pos:
return max-min+pos-min, max
else:
return max-min+max-pos, min
for indexNum in range(len(GroupNo)):
TaskList.append([])
CurrentTask = [[],[],[],[],[]]
CurrentPos = [0, 0, 0, 0, 0]
CurrentSum = [0, 0, 0, 0, 0]
temp = GroupNo[indexNum]
tempProduct = ProductOrderList[indexNum]
ProductList = list(temp[0])
for v in iter(temp[1]):
minIndex = 0
minPos = 0
minSum = max(CurrentSum) + len(ProductList)
for i in range(0,5):
delta, tmpPos = sumDeltaDistance(CurrentPos[i], ProductList, OrderObject[v])
if CurrentSum[i]+delta < minSum:
minSum = CurrentSum[i]+delta
minIndex = i
minPos = tmpPos
CurrentTask[minIndex].append(v)
CurrentPos[minIndex] = minPos
CurrentSum[minIndex] = minSum
TaskList[indexNum] = CurrentTask
TimeList.append(max(CurrentSum))
res3 = []
for k, v in enumerate(TaskList):
for i in range(0, 5):
for tn, pi in enumerate(v[i]):
res3.append([pi+1, k+1, i+1, tn+1])
print(len(res3))
sum(TimeList)
def NumToString(x):
return ["D{0:0>4}".format(x[0]), x[1], x[2], x[3]]
result3 = list(map(NumToString, res3))
result3 = pd.DataFrame(result3, columns=['OrderNo','GroupNo','WorkerNo', 'TaskNo']);
result3.to_csv("result3.csv", index=False)
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