tool
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# ax = plt.gca()
# [ax.spines[i].set_visible(False) for i in ["top", "right"]]
def gatt(data):
"""
甘特图
:param data: m行n列，第1行工序编号，值加工时间
:return:
"""
makespan = makespan_value(data)
left = makespan_left(data)
font_size = 8
if data.shape[1] <= 30:
font_size = 10
if data.shape[0] <= 4:
font_size = 10
for i in range(1, data.shape[0]):
for j in range(data.shape[1]):
if data[i, j] != 0:
plt.barh(i, data[i, j], left=left[i, j])
plt.text(left[i, j] + data[i, j] / 8, i, r"$J_{%s}$" % int(data[0, j]), color="k", size=font_size)
plt.plot([makespan] * (left.shape[0] + 1), range(left.shape[0] + 1), 'r--', alpha=0.4)
plt.text(makespan - 0.5, 0, r"$Makespan={%s}$" % np.round(makespan, 2), ha="right", fontdict={"size": 12})
plt.yticks(range(1, left.shape[0]), range(1, left.shape[0]))
plt.ylabel(r"$Machine$ $Number$", fontsize=12)
plt.xlabel(r"$Time$", fontsize=12)
def makespan(data):
"""
工件某工序的完成时间
:param data: m行n列，第1行工序编号，值加工时间
:return:makespan
"""
makespan = np.zeros_like(data)
for i in range(makespan.shape[0]):
for j in range(makespan.shape[1]):
if i == 0:
makespan[i, j] = data[i, j]
if i == 1:
makespan[i, j] = np.sum(data[1, :j + 1])
if j == 0 and i != 0:
makespan[i, j] = np.sum(data[1:i + 1, j])
for i in range(2, makespan.shape[0]):
for j in range(1, makespan.shape[1]):
makespan[i, j] = data[i, j] + max(makespan[i, j - 1], makespan[i - 1, j])
return makespan
def makespan_value(data):
"""
最大生产流程时间
:param data: m行n列，第1行工序编号，值加工时间
:return:makespan_value
"""
makespan_value = makespan(data)[-1, -1]
return makespan_value
def makespan_left(data):
"""
工件某工序的开始时间
:param data: m行n列，第1行工序编号，值加工时间
:return:left
"""
left = makespan(data) - data
left[0] = data[0]
return left
def crtd_fs(n, m, low, high):
"""
生成流水车间作业数据
:param n: 工序数目
:param m: 机器数目
:param low: 加工时间最小值
:param high: 加工时间最大值
:return:data_fs
"""
data_fs = np.zeros([m + 1, n], dtype=int)
data_fs[0] = np.random.permutation(np.arange(1, n + 1))
data_fs[1:] = np.random.randint(low, high + 1, [m, n])
data_fs = data_fs[:, np.argsort(data_fs[0])]
return data_fs
def crtd_hfs(n, s, low, high):
"""
生成混合流水车间作业数据
:param n: 工件数目
:param s: 2行，第1行工序，第2行对应的并行机数目
:param low: 加工时间最小值
:param high: 加工时间最大值
:return:data_hfs
"""
data_hfs = np.zeros([n, np.sum(s[1]) + 1], dtype=int)
data_hfs[:, 0] = np.random.permutation(np.arange(1, n + 1))
data_hfs[:, 1:] = np.random.randint(low, high + 1, [n, np.sum(s[1])])
data_hfs = data_hfs[np.argsort(data_hfs[:, 0]), :]
return data_hfs
def crtd_moore(n, low, high):
"""
生成单机作业数据
:param n: 工序数目
:param low: 加工时间最小值
:param high: 加工时间最大值
:return:data_moore
"""
data_moore = np.zeros([3, n], dtype=int)
data_moore[0] = np.random.permutation(np.arange(1, n + 1))
data_moore[2] = np.random.randint(low, high + 1, n)
data_moore[1] = data_moore[2] + np.random.randint(n * low, n*low + high + 1, n)
data_moore = data_moore[:, np.argsort(data_moore[0])]
return data_moore
ga_crossover
# -*- coding:utf-8 -*-
import numpy as np
def baker(M):
"""
线性排序参数
:param M: 正整数
:return:a,b
"""
b = 2
a = (M + 1) * (1 + b / 2)
return a, b
def get_different(a, b):
"""
在b的元素中获取与a中元素不同的元素
:param a:1维
:param b:1维
:return:different
"""
different = set()
a, b = np.array(a), np.array(b)
for i in range(b.shape[0]):
if b[i] not in a:
different.add((b[i]))
different = list(different)
return different
def same_index(a, b):
"""
获取a中元素包含于b中元素的在a中的索引
:param a:1维
:param b:1维
:return:c
"""
a, b = np.array(a), np.array(b)
c = []
for i in range(a.shape[0]):
for j in range(b.shape[0]):
if a[i] == b[j]:
c.append(i)
c = np.array(c)
return c
def xcx(pop):
"""
循环交叉
:param pop: 若干个排列组合
:return:crossover
"""
crossover = np.zeros_like(pop)
for c in range(pop.shape[0]):
p = np.random.choice(pop.shape[1], 2, replace=False)
p = p[np.argsort(p)]
genetic = pop[c, :p[0]], pop[c, p[0]:p[1] + 1], pop[c, p[1] + 1:]
crossover[c] = np.hstack([genetic[2], genetic[0], genetic[1]])
return crossover
def xpm(pop):
"""
部分匹配交叉、顺序交叉的混合交叉
:param pop: 若干个排列组合
:return:crossover
"""
crossover = np.copy(pop)
cn = crossover.shape[0]
if cn % 2 != 0:
cn -= 1
for c in range(int(cn / 2)):
p = np.random.choice(np.arange(1, pop.shape[1] - 1), 2, replace=False)
p = p[np.argsort(p)]
genetic_a = pop[c, :p[0]], pop[c, p[0]:p[1] + 1], pop[c, p[1] + 1:]
genetic_b = pop[-c - 1, :p[0]], pop[-c - 1, p[0]:p[1] + 1], pop[-c - 1, p[1] + 1:]
beside_a = np.hstack([genetic_a[0], genetic_a[2]])
beside_b = np.hstack([genetic_b[0], genetic_b[2]])
substitute_a = get_different(genetic_b[1], genetic_a[1])
substitute_b = get_different(genetic_a[1], genetic_b[1])
same_index_a = same_index(beside_a, genetic_b[1])
same_index_b = same_index(beside_b, genetic_a[1])
for i in range(same_index_a.shape[0]):
beside_a[same_index_a[i]] = substitute_a[i]
for j in range(same_index_b.shape[0]):
beside_b[same_index_b[j]] = substitute_b[j]
crossover[c, :p[0]] = beside_a[:p[0]]
crossover[c, p[0]:p[1] + 1] = genetic_b[1]
crossover[c, p[1] + 1:] = beside_a[p[0]:]
crossover[-c - 1, :p[0]] = beside_b[:p[0]]
crossover[-c - 1, p[0]:p[1] + 1] = genetic_a[1]
crossover[-c - 1, p[1] + 1:] = beside_b[p[0]:]
return crossover