文章目录

论文代码复现 | 无人机与卡车联合配送(Python+Gurobi)(The flying sidekick traveling salesman problem)
- 无人机配送概述
- 文献笔记
- - 论文摘要
  - 论文主要图表摘录
  - - 无人机与卡车联合运输 - 联合但无交互的模式
    - 无人机与卡车联合运输 - 联合有交互的模式
- 无人机与卡车联合运输(FSTSP)的数学模型
- Python调用Gurobi求解FSTSP
- 数值实验
- - 算例
- 结果展示与可视化
- - 设置`nodeNum = 5`
  - 设置`nodeNum = 7`
  - 设置`nodeNum = 8`
  - 设置`nodeNum = 10`
拓展
参考文献

论文代码复现 | 无人机与卡车联合配送(Python+Gurobi)(The flying sidekick traveling salesman problem)

无人机配送概述

随着无人机技术的不断发展，无人机在工业界的应用场景也日益多样化。近几年，很多物流企业开始将目光瞄向无人机配送。比如亚马逊、DHL、京东、顺丰等。渐渐的，这种配送模式得到了更大的扩展，现如今，很多企业都在使用无人配送的提法，在这种无人配送的场景中， 无人配送小车、无人机是重要的组成部分。本文主要关注无人机配送。

无人机配送的模式多种多样。

第一种就是在城市中选择若干个无人机起飞点，在这个点的无人机从改点出发，去配送周围的客户。

另一种是无人机和卡车联合配送的模式。这种模式也有很多不同的操作方法。

卡车司机带着无人机一起去配送(flying sidekick TSP, FDTSP)，到一些比较难服务的点，就让无人机去配送那个点，然后自己去下一个客户点。无人机配送完后，卡车司机再遥控无人机跟卡车司机会合。

卡车司机不带无人机(parallel drone scheduling TSP, PDTSP)。我们分配任务的时候，决策哪些任务交给无人机去服务，哪些任务交给卡车去服务，然后各自规划自己的服务顺序，最终把所有顾客都服务完。期间，卡车和无人机没有交互。

当然还有其他服务方式。这里不做展开。

文献笔记

本文主要是发表在Transportation Research Part C: Emerging Technologies上的关于无人机和卡车的协同配送的文章的模型部分的代码复现。文章题目为The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery（参考文献[1]）

Murray, C. C., & Chu, A. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies, 54, 86-109.

该问题简称FSTSP。

论文摘要

Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.

论文主要图表摘录

下面我们讲论文中的一些重要的图摘录下来，帮助读者快速理解文章的内容。首先就是亚马逊和DHL的无人机携带包裹的场景。

无人机与卡车联合运输 - 联合但无交互的模式

下面几幅图中

绿色方框 – 代表在无人机飞行里程范围内的客户点，这些点都可以被无人机服务；
红色圆点 – 代表在无人机里程范围之外的客户点，这些点只能被卡车服务，不能被无人机服务。

图a是传统的配送模式，卡车按照访问顺序，从出发点依次服务完所有的顾客点。

图b是卡车和无人机联合配送的场景，但是卡车和无人既没有互动。可以看到，顾客2和顾客9是无法被无人机服务的点，而其余的点都是可以被无人机服务的。一个可行解就是：

卡车的服务顺序depot →9→2→7→\rightarrow 9 \rightarrow 2 \rightarrow 7 \rightarrow→9→2→7→ depot
无人机服务的点为：1、3、4、5、6、7、8。每个点无人机都往返依次完成服务。

图c是经过优化的最优配送方案。

无人机与卡车联合运输 - 联合有交互的模式

下图 a是卡车单独运输的最优解。

图b是无人机和卡车联合配送有交互的最优解。可以看到，卡车在配送到客户点4的时候，放飞了无人机，让无人机去服务客户点7，之后又在客户点6放飞了无人机去服务客户点1。分别在客户点6和客户点5回收无人机。(当然是同一架无人机)

无人机与卡车联合运输(FSTSP)的数学模型

其实就是上面讲过的第1种模式。我们再重复一遍。

卡车司机带着无人机一起去配送(flying sidekick TSP, FDTSP)，到一些比较难服务的点，就让无人机去配送那个点，然后自己去下一个客户点。无人机配送完后，卡车司机再遥控无人机跟卡车司机会合。

这里最主要的决策变量有两个：

xijx_{ij}xij: 0-1变量。表示卡车是否从点iii直接运行到点jjj；
yijky_{ijk}yijk：0-1变量。如果无人机从点iii被发射，去服务客户点jjj，然后在客户点kkk被回收，则yijk=1y_{ijk}=1yijk=1，否则为0。这里论文中强调，收发、访问点互不相同，也就是i,j,ki,j,ki,j,k互不相同。
tit_iti：到达顾客点iii的时间；
uiu_iui:为了消除子环路，也可以看做是第iii个客户点的访问顺序，也就是他是第几个被访问的；

重要参数

无人机的单次发射时间；

无人机的回收时间；

这些时间可以自己设置。另外，无人机的飞行速度、卡车的行驶速度等，都可以自己进行设置测试。

目标函数:最小化服务完所有顾客的时间。

接下来，我们贴上该问题的数学模型。数学模型的具体解释请读者自行读论文，这里我们就不在赘述。

Python调用Gurobi求解FSTSP

这里我们贴上Python调用Gurobi求解FSTSP的完整代码。代码中包含了可视化最优解的部分，方便读者查看最优解的情况。

# _*_coding:utf-8 _*_
from __future__ import print_function
from gurobipy import *
import re
import math
# from test.pickletester import BigmemPickleTests
import matplotlib.pyplot as plt
import numpy
import pandas as pdclass Data:customerNum = 0 nodeNum     = 0 range       = 0 lunchingTime= 0 recoverTime = 0 cor_X       = [] cor_Y       = [] demand      = [] serviceTime = [] readyTime   = [] dueTime     = [] disMatrix   = [[]] # 读取数据# function to read data from .txt files
def readData(data, path, customerNum):data.customerNum = customerNum data.nodeNum = customerNum + 2 f = open(path, 'r') lines = f.readlines() count = 0 # read the infofor line in lines:count = count + 1 if(count == 2):line = line[:-1] str = re.split(r" +", line) data.range = float(str[0]) elif(count == 5):line = line[:-1] str = re.split(r" +", line) data.lunchingTime = float(str[0]) data.recoverTime = float(str[1]) elif(count >= 9 and count <= 9 + customerNum): # (count >= 9 and count <= 9 + customerNum)line = line[:-1] str = re.split(r" +", line) data.cor_X.append(float(str[2])) data.cor_Y.append(float(str[3])) data.demand.append(float(str[4])) data.readyTime.append(float(str[5])) data.dueTime.append(float(str[6])) data.serviceTime.append(float(str[7])) data.cor_X.append(data.cor_X[0]) data.cor_Y.append(data.cor_Y[0]) data.demand.append(data.demand[0]) data.readyTime.append(data.readyTime[0]) data.dueTime.append(data.dueTime[0]) data.serviceTime.append(data.serviceTime[0]) # compute the distance matrixdata.disMatrix = [([0] * data.nodeNum) for p in range(data.nodeNum)]  # 初始化距离矩阵的维度,防止浅拷贝# data.disMatrix = [[0] * nodeNum] * nodeNum]  这个是浅拷贝，容易重复for i in range(0, data.nodeNum):for j in range(0, data.nodeNum):temp = (data.cor_X[i] - data.cor_X[j])**2 + (data.cor_Y[i] - data.cor_Y[j])**2 data.disMatrix[i][j] = math.sqrt(temp)
#             if(i == j):
#                 data.disMatrix[i][j] = 0 # print("%6.2f" % (math.sqrt(temp)), end = " ") temp = 0 return data def printData(data, customerNum):print("下面打印数据\n") print("UAV range = %4d" % data.range) print("UAV lunching time = %4d" % data.lunchingTime) print("UAV recover time = %4d" % data.recoverTime) for i in range(len(data.demand)):print('{0}\t{1}\t{2}\t{3}'.format(data.demand[i], data.readyTime[i],data.dueTime[i],  data.serviceTime[i])) print("-------距离矩阵-------\n") for i in range(data.nodeNum):for j in range(data.nodeNum):#print("%d   %d" % (i, j)) print("%6.2f" % (data.disMatrix[i][j]), end = " ") print() class Solution:ObjVal = 0 X = [[]] Y = [[[]]] U = [] P = [] T = [] Tt = [] route_Truck = [] route_UAV = [] #     def __init__(self):
#         solution = Solution()
#         # X_ij
#         solution.X = [[[] for i in range(data.nodeNum)] for j in range(data.nodeNum)]
#         # Y_ijk
#         solution.Y = [[[[] for k in range(data.nodeNum)] for j in range(data.nodeNum)] for i in range(data.nodeNum)]
#         # U_i
#         solution.U = [[] for i in range(data.nodeNum)]
#         # P_ij
#         solution.P = [[[] for j in range(data.nodeNum)] for i in range(data.nodeNum)]
#         # T_i, T_i'
#         solution.T = [[] for i in range(data.nodeNum)]
#         solution.Tt = [[] for i in range(data.nodeNum)]
#         return solution def getSolution(self, data, model):solution = Solution() solution.ObjVal = model.ObjVal # X_ijsolution.X = [([0] * data.nodeNum) for j in range(data.nodeNum)]  # Y_ijksolution.Y = [[([0] * data.nodeNum) for j in range(data.nodeNum)] for i in range(data.nodeNum)] # U_isolution.U = [[0] for i in range(data.nodeNum)] # P_ijsolution.P = [[[0] for j in range(data.nodeNum)] for i in range(data.nodeNum)] # T_i, T_i'solution.T = [[0] for i in range(data.nodeNum)] solution.Tt = [[0] for i in range(data.nodeNum)] a = U[0].x for m in model.getVars():str = re.split(r"_", m.VarName) if(str[0] == "X" and m.x == 1):solution.X[int(str[1])][int(str[2])] = m.x print(str, end = "") print(" = %d" % m.x) elif(str[0] == "Y" and m.x == 1):solution.Y[int(str[1])][int(str[2])][int(str[3])] = m.x elif(str[0] == "U" and m.x > 0) :solution.U[int(str[1])] = m.x elif(str[0] == "T" and m.x > 0):solution.T[int(str[1])] = m.x  elif(str[0] == "Tt" and m.x > 0):solution.Tt[int(str[1])] = m.x elif(str[0] == "P" and m.x > 0):solution.P[int(str[1])][int(str[2])] = m.x   # get the route of truck and UAVj = 0 for i in range(data.nodeNum):i = j   # note that the variable is whether is a local variable or a global variable# print("i = %d, j = %d" % (i, j), end = "        ") for j in range(data.nodeNum):if(solution.X[i][j] == 1):solution.route_Truck.append(i) print(" %d -" % i, end = " ") # print("   i = %d, j = %d" % (i, j)) break print(" 0")  solution.route_Truck.append(0) print("\n\n ------Route of UAV ------- ") count = 0 for i in range(data.nodeNum):for j in range(data.nodeNum):for k in range(data.nodeNum):if(solution.Y[i][j][k] == 1):count  = count + 1 #print("UAV %d : %d - %d - %d" % (count, i, j, k))    temp = [i, j, k] solution.route_UAV.append(temp) for i in range(len(solution.route_Truck)):print(" %d " %  solution.route_Truck[i], end = " ") print() print("\n\n ------Route of UAV ------- ") for i in range(len(solution.route_UAV)):for j in range(len(solution.route_UAV[0])):print("UAV %d : %d - %d - %d" % (i, solution.route_UAV[i][0], solution.route_UAV[i][1], solution.route_UAV[i][2]))    # print(solution.route_UAV)     return solution  # reading data
data = Data()
# path = r'C:\Users\hsingluLiu\eclipse-workspace\PythonCallGurobi_Applications\FSTSP\c101.txt'
path = 'c101.txt' customerNum = 10
readData(data, path, customerNum)
printData(data, customerNum) # =========build the model===========
big_M = 10000
# construct the model object
model = Model("FSTSP") # Initialize variables
# create variables: Muiti-dimension vector: from inner to outer
# X_ij
X = [[[] for i in range(data.nodeNum)] for j in range(data.nodeNum)]  # Y_ijk
Y = [[[[] for k in range(data.nodeNum)] for j in range(data.nodeNum)] for i in range(data.nodeNum)] # U_i
U = [[] for i in range(data.nodeNum)] # P_ij
P = [[[] for j in range(data.nodeNum)] for i in range(data.nodeNum)] # T_i, T_i'
T = [[] for i in range(data.nodeNum)]
Tt = [[] for i in range(data.nodeNum)] for i in range(data.nodeNum):name1 = 'U_' + str(i) name2 = 'T_' + str(i) name3 = 'Tt_' + str(i) U[i] = model.addVar(0, data.nodeNum, vtype = GRB.CONTINUOUS, name = name1) T[i] = model.addVar(0, big_M, vtype = GRB.CONTINUOUS, name = name2) Tt[i] = model.addVar(0, big_M, vtype = GRB.CONTINUOUS, name = name3) for j in range(data.nodeNum):name4 = 'X_' + str(i) + "_"+ str(j) name5 = 'P_' + str(i) + "_" + str(j) X[i][j] = model.addVar(0, 1, vtype = GRB.BINARY, name = name4) P[i][j] = model.addVar(0, 1, vtype = GRB.BINARY, name = name5) for k in range(data.nodeNum):name6 = 'Y_' + str(i) + "_" + str(j) + "_" + str(k) Y[i][j][k] = model.addVar(0, 1, vtype = GRB.BINARY, name = name6) # Add constraints
# create the objective expression(1)
obj = LinExpr(0) # add the objective function into the model
model.setObjective(T[data.nodeNum - 1], GRB.MINIMIZE) # constraint (2)
for j in range(1, data.nodeNum - 1): # 这里需要注意，i的取值范围，否则可能会加入空约束 expr = LinExpr(0) for i in range(0, data.nodeNum - 1): # i -- N0if(i != j):expr.addTerms(1, X[i][j]) for k in range(1, data.nodeNum): # k -- N+if(i != k and j != k):expr.addTerms(1, Y[i][j][k]) model.addConstr(expr == 1, "c1") expr.clear() # constraint (3)
expr = LinExpr(0)
for j in range(1, data.nodeNum):expr.addTerms(1, X[0][j])
model.addConstr(expr == 1, "c2")
expr.clear() # constraint (4)
expr = LinExpr(0)
for i in range(data.nodeNum - 1):expr.addTerms(1, X[i][data.nodeNum - 1])
model.addConstr(expr == 1.0, "c3")
expr.clear() # constraint (5)
for i in range(1, data.nodeNum - 1):for j in range(1, data.nodeNum):if(i != j):model.addConstr(U[i] - U[j] + 1 <= big_M  - big_M * X[i][j], 'c5') # constraint (6)
for j in range(1, data.nodeNum - 1):expr1 = LinExpr(0) expr2 = LinExpr(0) for i in range(0, data.nodeNum - 1):if(j != i):expr1.addTerms(1, X[i][j]) for k in range(1, data.nodeNum):if(j != k):expr2.addTerms(1, X[j][k]) model.addConstr(expr1 == expr2, "c6") expr1.clear() expr2.clear() # constraint (7)
for i in range(data.nodeNum - 1):expr = LinExpr(0) for j in range(1, data.nodeNum - 1):if(i != j ):for k in range(1, data.nodeNum):if(i != k and j != k):expr.addTerms(1, Y[i][j][k]) model.addConstr(expr <= 1, 'c7') expr.clear()         # constraint (8)
for k in range(1, data.nodeNum):expr = LinExpr(0) for i in range(0, data.nodeNum - 1):if(i != k ):for j in range(1, data.nodeNum - 1):if(j != i and j != k):expr.addTerms(1, Y[i][j][k]) model.addConstr(expr <= 1, 'c8') expr.clear() # constraint (9)
for i in range(1, data.nodeNum - 1):for j in range(1, data.nodeNum):for k in range(1, data.nodeNum):if(i != j and i != k and j != k):expr1 = LinExpr(0) expr2 = LinExpr(0) for h in range(data.nodeNum - 1):if(h != i):expr1.addTerms(1, X[h][i]) for l in range(1, data.nodeNum - 1):if(l != k):expr2.addTerms(1, X[l][k]) model.addConstr(2 * Y[i][j][k] <= expr1 + expr2, "c9") expr1.clear() expr2.clear() # constraint (10)
for j in range(1, data.nodeNum - 1):for k in range(1, data.nodeNum):if(j != k):expr = LinExpr(0) for h in range(1, data.nodeNum - 1):expr.addTerms(1, X[h][k]) model.addConstr(Y[0][j][k] <= expr, "c10") expr.clear() # constraint (11)
for i in range(1, data.nodeNum - 1):for k in range(1, data.nodeNum):if(k != i):expr = LinExpr(0) for j in range(1, data.nodeNum - 1):if(i != j and j != k):expr.addTerms(big_M, Y[i][j][k]) model.addConstr(U[k] - U[i] >= 1 - big_M + expr, "c11") expr.clear() # constraint (12)
for i in range(1, data.nodeNum - 1):expr = LinExpr(0) for j in range(1, data.nodeNum - 1):for k in range(1, data.nodeNum):if(j != i and i != k and j != k):expr.addTerms(big_M, Y[i][j][k]) model.addConstr(Tt[i] >= T[i] - big_M + expr, "c12") expr.clear() # constraint (13)
for i in range(1, data.nodeNum - 1):expr = LinExpr(0) for j in range(1, data.nodeNum - 1):for k in range(1, data.nodeNum):if(j != i and i != k and j != k):expr.addTerms(big_M, Y[i][j][k]) model.addConstr(Tt[i] <= T[i] + big_M - expr, "c13") expr.clear() # constraint (14)
for k in range(1, data.nodeNum):expr = LinExpr(0) for i in range(0, data.nodeNum - 1):for j in range(1, data.nodeNum - 1):if(j != i and i != k and j != k):expr.addTerms(big_M, Y[i][j][k]) model.addConstr(Tt[k] >= T[k] - big_M + expr, "c14") expr.clear()             # constraint (15)
for k in range(1, data.nodeNum):expr = LinExpr(0) for i in range(0, data.nodeNum - 1):for j in range(1, data.nodeNum - 1):if(j != i and i != k and j != k):expr.addTerms(big_M, Y[i][j][k]) model.addConstr(Tt[k] <= T[k] + big_M - expr, "c15") expr.clear()    # constraint (16)
for h in range(data.nodeNum - 1):for k in range(1, data.nodeNum):if(h != k):expr1 = LinExpr(0) expr2 = LinExpr(0) for l in range(1, data.nodeNum - 1):for m in range(1, data.nodeNum):if(k != l and k != m and l != m):expr1.addTerms(data.lunchingTime, Y[k][l][m]) for i in range(data.nodeNum - 1):for j in range(1, data.nodeNum - 1):if(i != j and i != k and j != k):expr2.addTerms(data.recoverTime, Y[i][j][k]) model.addConstr(T[k] >= T[h] + data.disMatrix[h][k] + expr1 + expr2 - big_M + big_M * X[h][k], "c16") expr1.clear() expr2.clear() # constraint (17)
for j in range(1, data.nodeNum - 1):for i in range(data.nodeNum - 1):if(i != j):expr = LinExpr(0) for k in range(1, data.nodeNum):if(i != k and j != k):expr.addTerms(big_M, Y[i][j][k]) model.addConstr(Tt[j] >= Tt[i] + data.disMatrix[i][j] - big_M + expr, "c17") expr.clear() # constraint (18)
for j in range(1, data.nodeNum - 1):for k in range(1, data.nodeNum):if(k != j):expr = LinExpr(0) for i in range(data.nodeNum - 1):if(i != k and i != j):expr.addTerms(big_M, Y[i][j][k]) model.addConstr(Tt[k] >= Tt[j] + data.disMatrix[j][k] + data.recoverTime - big_M + expr, "c18") expr.clear() # constraint (19)
for k in range(1, data.nodeNum):for j in range(1, data.nodeNum - 1):for i in range(data.nodeNum - 1):if(i != j and i != k and j != k):model.addConstr(Tt[k] - Tt[j] + data.disMatrix[i][j] <= data.range + big_M - big_M * Y[i][j][k], "c19") # constraint (20)
for i in range(1, data.nodeNum - 1):for j in range(1, data.nodeNum - 1):if(i != j):model.addConstr(U[i] - U[j] >= 1 - big_M * P[i][j], "c20") # constraint (21)
for i in range(1, data.nodeNum - 1):for j in range(1, data.nodeNum - 1):if(i != j):model.addConstr(U[i] - U[j] <= -1 +big_M - big_M * P[i][j], "c21") # constraint (22)
for i in range(1, data.nodeNum - 1):for j in range(1, data.nodeNum - 1):if(i != j):model.addConstr(P[i][j] + P[j][i] == 1, "c22") # constraint (23)
for i in range(data.nodeNum - 1):for k in range(1, data.nodeNum):for l in range(1, data.nodeNum - 1):if(k != i and l != i and l != k):expr1 = LinExpr(0) expr2 = LinExpr(0) for j in range(1, data.nodeNum - 1):if(k != j and i != j):expr1.addTerms(big_M, Y[i][j][k]) for m in range(1, data.nodeNum - 1):for n in range(1, data.nodeNum):if(l != m and l != n and m != n):expr2.addTerms(big_M, Y[l][m][n]) model.addConstr(Tt[l] >= Tt[k] - 3*big_M + expr1 + expr2 + big_M * P[i][l], "c23") expr1.clear() expr2.clear() # constraint (24)
model.addConstr(T[0] == 0, "c24") # constraint (25)
model.addConstr(Tt[0] == 0, "c25") # constraint (26)
for j in range(1, data.nodeNum - 1):model.addConstr(P[0][j] == 1, "c26") # constraint (27)
for i in range(data.nodeNum):for j in range(data.nodeNum):if(i == j):model.addConstr(X[i][j] == 0, "c27") for k in range(data.nodeNum):if(i == j or i == k or k == j):model.addConstr(Y[i][j][k] == 0, "c28") # solve the problem
model.write('a.lp')
model.Params.timelimit = 3600
model.optimize() # get the solution info
solution = Solution()
solution = solution.getSolution(data, model)
print("\n\n\n\n-----optimal value-----")
print("Obj: %g" % solution.ObjVal)
print("\n\n ------Route of truck------")
# print("Truck: ", end = " ")
j = 0
for i in range(data.nodeNum):i = j   # note that the variable is whether is a local variable or a global variable# print("i = %d, j = %d" % (i, j), end = "        ") for j in range(data.nodeNum):if(solution.X[i][j] == 1):print(" %d -" % i, end = " ") # print("   i = %d, j = %d" % (i, j)) break
print(" 0")  print("\n\n ------Route of UAV ------- ")
count = 0
for i in range(data.nodeNum):for j in range(data.nodeNum):for k in range(data.nodeNum):if(solution.Y[i][j][k] == 1):count  = count + 1 print("UAV %d : %d - %d - %d" % (count, i, j, k)) # draw the route graph
# draw all the nodes first
# data1 = Data()
# readData(data1, path, 100)
fig = plt.figure(figsize=(15,10))
font_dict = {'family': 'Arial',   # serif'style': 'normal',   # 'italic','weight': 'normal','color':  'darkred', 'size': 30,}
font_dict2 = {'family': 'Arial',   # serif'style': 'normal',   # 'italQic','weight': 'normal','color':  'darkred', 'size': 24,}
plt.xlabel('x', font_dict)
plt.ylabel('y', font_dict)
plt.title('Optimal Solution for FSTSP (5 customers)', font_dict)
plt.xticks(fontsize=22)
plt.yticks(fontsize=22)    # plt.yticks(fontsize=30)
plt.grid(True, color='r', linestyle='-', linewidth=2)'''
marker='o'
marker=','
marker='.'
marker=(9, 3, 30)
marker='+'
marker='v'
marker='^'
marker='<'
marker='>'
marker='1'
marker='2'
marker='3'
red        blue        green
'''
plt.scatter(data.cor_X[0], data.cor_Y[0], c='blue', alpha=1, marker=',', linewidths=5, label='depot')
plt.scatter(data.cor_X[1:-1], data.cor_Y[1:-1], c='magenta', alpha=1, marker='o', linewidths=5, label='customer') # c='red'定义为红色，alpha是透明度，marker是画的样式# draw the route
for i in range(data.nodeNum):for j in range(data.nodeNum):if(solution.X[i][j] == 1):x = [data.cor_X[i], data.cor_X[j]] y = [data.cor_Y[i], data.cor_Y[j]] plt.plot(x, y, 'b', linewidth = 3)
#             plt.text(data.cor_X[i]-1, data.cor_Y[i], str(i), fontsize=15, color = 'black')
#             plt.text(coverage50index*0.98, 4, coverage50index, fontsize=10, color = 'red')  plt.text(data.cor_X[i]-0.2, data.cor_Y[i], str(i), fontdict = font_dict2) for i in range(data.nodeNum):for j in range(data.nodeNum):for k in range(data.nodeNum):if(solution.Y[i][j][k] == 1):x = [data.cor_X[i], data.cor_X[j], data.cor_X[k]] y = [data.cor_Y[i], data.cor_Y[j], data.cor_Y[k]] plt.plot(x, y, 'r--', linewidth = 3) plt.text(data.cor_X[j]-0.2, data.cor_Y[j], str(j), fontdict = font_dict2)   #plt.plot(x, y, 'r--', label = "UAV", linewidth = 3) # plt.grid(True)
plt.grid(False)
plt.legend(loc='best', fontsize = 20)
plt.show()

数值实验

算例

算例我们就采用Solomon的VRP benchmark的算例数据，只是抽出其中的一些点作为我们算例的点。
这里我们需要对Solomon benchmark的算例做一些小改动，就是设置无人机的飞行里程RANGE，以及设置无人机的发射时间和回收时间LUNCTING和RECOVER。如下图，我们设置

RANGE = 100
LUNCTING=1
RECOVER=1
其余的数据不做改动。
设置无人机的里程为100修改虽然感觉不合理，但是这样在小规模的情况下能够让无人机被发射出去服务一个顾客点。

举Solomon benchmark的c101为例，修改后的算理数据如下：

RANGE
100LUNCTING RECOVER
1      1CUSTOMER
CUST NO.  XCOORD.   YCOORD.    DEMAND   READY TIME  DUE DATE   SERVICE   TIME0      40         50          0          0       1236          0   1      45         68         10        912        967         90   2      45         70         30        825        870         90   3      42         66         10         65        146         90   4      42         68         10        727        782         90   5      42         65         10         15         67         90   6      40         69         20        621        702         90   7      40         66         20        170        225         90   8      38         68         20        255        324         90   9      38         70         10        534        605         90   10      35         66         10        357        410         90   11      35         69         10        448        505         90   12      25         85         20        652        721         90   13      22         75         30         30         92         90   14      22         85         10        567        620         90   15      20         80         40        384        429         90   16      20         85         40        475        528         90   17      18         75         20         99        148         90   18      15         75         20        179        254         90   19      15         80         10        278        345         90   20      30         50         10         10         73         90   21      30         52         20        914        965         90   22      28         52         20        812        883         90   23      28         55         10        732        777         90   24      25         50         10         65        144         90   25      25         52         40        169        224         90   26      25         55         10        622        701         90   27      23         52         10        261        316         90   28      23         55         20        546        593         90   29      20         50         10        358        405         90   30      20         55         10        449        504         90   31      10         35         20        200        237         90   32      10         40         30         31        100         90   33       8         40         40         87        158         90   34       8         45         20        751        816         90   35       5         35         10        283        344         90   36       5         45         10        665        716         90   37       2         40         20        383        434         90   38       0         40         30        479        522         90   39       0         45         20        567        624         90   40      35         30         10        264        321         90   41      35         32         10        166        235         90   42      33         32         20         68        149         90   43      33         35         10         16         80         90   44      32         30         10        359        412         90   45      30         30         10        541        600         90   46      30         32         30        448        509         90   47      30         35         10       1054       1127         90   48      28         30         10        632        693         90   49      28         35         10       1001       1066         90   50      26         32         10        815        880         90   51      25         30         10        725        786         90   52      25         35         10        912        969         90   53      44          5         20        286        347         90   54      42         10         40        186        257         90   55      42         15         10         95        158         90   56      40          5         30        385        436         90   57      40         15         40         35         87         90   58      38          5         30        471        534         90   59      38         15         10        651        740         90   60      35          5         20        562        629         90   61      50         30         10        531        610         90   62      50         35         20        262        317         90   63      50         40         50        171        218         90   64      48         30         10        632        693         90   65      48         40         10         76        129         90   66      47         35         10        826        875         90   67      47         40         10         12         77         90   68      45         30         10        734        777         90   69      45         35         10        916        969         90   70      95         30         30        387        456         90   71      95         35         20        293        360         90   72      53         30         10        450        505         90   73      92         30         10        478        551         90   74      53         35         50        353        412         90   75      45         65         20        997       1068         90   76      90         35         10        203        260         90   77      88         30         10        574        643         90   78      88         35         20        109        170         90   79      87         30         10        668        731         90   80      85         25         10        769        820         90   81      85         35         30         47        124         90   82      75         55         20        369        420         90   83      72         55         10        265        338         90   84      70         58         20        458        523         90   85      68         60         30        555        612         90   86      66         55         10        173        238         90   87      65         55         20         85        144         90   88      65         60         30        645        708         90   89      63         58         10        737        802         90   90      60         55         10         20         84         90   91      60         60         10        836        889         90   92      67         85         20        368        441         90   93      65         85         40        475        518         90   94      65         82         10        285        336         90   95      62         80         30        196        239         90   96      60         80         10         95        156         90   97      60         85         30        561        622         90   98      58         75         20         30         84         90   99      55         80         10        743        820         90   100      55         85         20        647        726         90

结果展示与可视化

由于该问题比较难求解，我们先来一个小算例。

设置`nodeNum = 5`

我们设置nodeNum = 5，结果为:

注意，设置有5个顾客点的时候，点0和点6就代表depot。其余的以此类推。

Explored 3528 nodes (35235 simplex iterations) in 1.15 seconds
Thread count was 8 (of 8 available processors)Solution count 4: 42.2311 42.2311 43.3318 47.0835 Optimal solution found (tolerance 1.00e-04)
Best objective 4.223105625185e+01, best bound 4.223105625185e+01, gap 0.0000%
['X', '0', '5'] = 1
['X', '1', '3'] = 1
['X', '3', '6'] = 1
['X', '4', '1'] = 1
['X', '5', '4'] = 10 -  5 -  4 -  1 -  3 -  0------Route of UAV ------- 0   5   4   1   3   0  ------Route of UAV -------
UAV 0 : 0 - 2 - 6
UAV 0 : 0 - 2 - 6
UAV 0 : 0 - 2 - 6-----optimal value-----
Obj: 42.2311------Route of truck------0 -  5 -  4 -  1 -  3 -  0------Route of UAV -------
UAV 1 : 0 - 2 - 6

其中，
蓝色的路径代表卡车的路径；
红色的路径代表无人机的路径。
可以看到

卡车的服务顺序0→5→4→1→3→00 \rightarrow 5 \rightarrow 4 \rightarrow 1 \rightarrow 3 \rightarrow 00→5→4→1→3→0
无人机服务任务为：0→2→00 \rightarrow 2 \rightarrow 00→2→0
最终服务完所有顾客的时间为42.23。

设置`nodeNum = 7`

运行结果如下：

设置`nodeNum = 8`

nodeNum设置成8。求解时间为4m 26s。

Explored 163568 nodes (11107937 simplex iterations) in 265.29 seconds
Thread count was 16 (of 16 available processors)Solution count 10: 47.0389 47.0389 47.0389 ... 49.1723Optimal solution found (tolerance 1.00e-04)
Best objective 4.703886030563e+01, best bound 4.703886030563e+01, gap 0.0000%
['X', '0', '7'] = 1
['X', '1', '3'] = 1
['X', '3', '5'] = 1
['X', '4', '1'] = 1
['X', '5', '9'] = 1
['X', '6', '4'] = 1
['X', '7', '8'] = 1
['X', '8', '6'] = 10 -  7 -  8 -  6 -  4 -  1 -  3 -  5 -  0------Route of UAV ------- 0   7   8   6   4   1   3   5   0  ------Route of UAV -------
UAV 0 : 0 - 2 - 9
UAV 0 : 0 - 2 - 9
UAV 0 : 0 - 2 - 9-----optimal value-----
Obj: 47.0389------Route of truck------0 -  7 -  8 -  6 -  4 -  1 -  3 -  5 -  0------Route of UAV -------
UAV 1 : 0 - 2 - 9

结果为

设置`nodeNum = 10`

运行3600秒，还是没有找到可行解。如下图所示。

拓展

FSTSP求解起来比较难，本推文介绍的这篇，作者在论文中提出了一些启发式的求解方法。如果后续有机会，我们可以继续拓展。

当然，还有一些论文提出了动态规划的方法。见文献[2][5]。

这里我们也附上几篇其他相关文献。

关注公众号并留言获取参考文献下载链接以及测试算例。

参考文献

[1]:Murray, C. C., & Chu, A. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies, 54, 86-109. https://doi.org/10.1016/j.trc.2015.03.005
[2]: Agatz N, Bouman P, Schmidt M. Optimization approaches for the traveling salesman problem with drone[J]. Transportation Science, 2018, 52(4): 965-981.
[3]:Ham A M. Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming[J]. Transportation Research Part C: Emerging Technologies, 2018, 91: 1-14.
[4]:Chang Y S, Lee H J. Optimal delivery routing with wider drone-delivery areas along a shorter truck-route[J]. Expert Systems with Applications, 2018, 104: 307-317.
[5]:Bouman P, Agatz N, Schmidt M. Dynamic programming approaches for the traveling salesman problem with drone[J]. Networks, 2018, 72(4): 528-542.