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algorithms.py
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algorithms.py
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"""
A modular of solvers and algorithms
"""
import numpy as np
import networkx as nx
import os
import json
import gurobipy as gp
from gurobipy import GRB
import utils
from copy import deepcopy
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
import sys
from termcolor import colored
"""
Grid-like graph generator
"""
def grid_like_graph_generator(x_range, y_range, n, I):
grid = utils.av_grid_pts_generator(x_range, y_range, n)
zone_dict = utils.node_zoning(I.coordinates, grid)
G = utils.add_customer_n_depot_to_grid(grid, I.coordinates, zone_dict)
#TODO: The logic is not correct
# for i, coor_i in enumerate(I.coordinates):
# for j, coor_j in enumerate(I.coordinates):
# node_i = tuple(coor_i)
# node_j = tuple(coor_j)
# no_shortest_path = (np.linalg.norm(np.array(coor_i) - np.array(coor_j), ord=1) < nx.shortest_path_length(G, node_i, node_j, weight = 'length'))
# if j > i and no_shortest_path:
# new_node = (node_i[0], node_j[1], 'a')
# G.add_node(new_node)
# # G.add_edge(node_i, new_node, length = abs(node_i[1] - new_node[1]), is_av = 0)
# # G.add_edge(new_node, node_i, length = abs(node_i[1] - new_node[1]), is_av = 0)
# # G.add_edge(node_j, new_node, length = abs(node_j[0] - new_node[0]), is_av = 0)
# # G.add_edge(new_node, node_j, length = abs(node_j[0] - new_node[0]), is_av = 0)
G = utils.mark_customer_n_depot(G, I.coordinates)
return G
"""
Grid-like graph generator (dense)
"""
def grid_like_graph_generator_dense(x_range, y_range, n, I):
grid = utils.av_grid_pts_generator(x_range, y_range, n)
grid_pts = [[node[0], node[1]] for node in grid.nodes]
G = utils.generate_grid_by_points(I.coordinates + grid_pts)
# Mark all edges aligned with grid as av-enabled roads
x_step = grid.graph['x_step']
y_step = grid.graph['y_step']
vertical = np.arange(x_range[0], x_range[0] + (n + 1) * x_step, x_step)
horizontal = np.arange(y_range[0], y_range[0] + (n + 1) * y_step, y_step)
for e in G.edges:
if e[0][0] == e[1][0] and e[0][0] in vertical:
G.edges[e]['is_av'] = 1
if e[0][1] == e[1][1] and e[0][1] in horizontal:
G.edges[e]['is_av'] = 1
# Mark all customers and depot
G = utils.mark_customer_n_depot(G, I.coordinates)
return G
"""
An efficient generic VRP solver (by Gurobi)
"""
def generic_vrp_efficient_solver(G, customer, demand, depot, capacity, vehicle_cost, dir):
# Compute all shortest paths among clients
K_D = depot + customer
cost_non_av = np.zeros((len(customer) + 1, len(customer) + 1))
cost_av = np.zeros((len(customer) + 1, len(customer) + 1))
path_non_av = {}
path_av = {}
for i in range(len(K_D)):
for j in range(len(K_D)):
if i!=j:
cost_non_av[i][j] = nx.shortest_path_length(G, K_D[i], K_D[j], weight='non_av_cost')
cost_av[i][j] = nx.shortest_path_length(G, K_D[i], K_D[j], weight='av_cost')
path_non_av[str(K_D[i])+'-'+str(K_D[j])] = nx.shortest_path(G, K_D[i], K_D[j], weight='non_av_cost')
path_av[str(K_D[i])+'-'+str(K_D[j])] = nx.shortest_path(G, K_D[i], K_D[j], weight='av_cost')
# Create a new model
model = gp.Model('generic_vrp')
# Set the file path for logging
path = dir + '/generic_vrp_log.txt'
if os.path.exists(path):
os.remove(path)
model.Params.LogFile = path
# Parameters tuning for the MIP solver
model.Params.MIPFocus = 2 # Focusing on the phase 3 (proving optimality)
# model.Params.MIPGap = 0 # Specify the terminating duality gap
# Create variables
x_a = model.addMVar((len(K_D), len(K_D)), vtype=GRB.BINARY)
x_h = model.addMVar((len(K_D), len(K_D)), vtype=GRB.BINARY)
h = model.addMVar(len(customer), vtype=GRB.INTEGER, ub=np.repeat(capacity, len(customer)), lb=demand)
# Constraints
model.addConstrs(sum(x_h[:, i]) == sum(x_h[i, :]) for i in range(len(K_D)))
model.addConstrs(sum(x_a[:, i]) == sum(x_a[i, :]) for i in range(len(K_D)))
model.addConstrs(sum(x_h[i, :]) + sum(x_a[i, :]) == 1 for i in range(1, len(K_D)))
for i in range(len(K_D)):
model.addConstr(x_h[i, i] == 0)
model.addConstr(x_a[i, i] == 0)
for i in range(len(customer)):
for j in range(len(customer)):
if i != j:
model.addConstr(h[j] >= h[i] + capacity * (x_h[i+1, j+1] + x_a[i+1, j+1] - 1) + demand[j])
# Set the bbjective
model.setObjective(sum(x_a[i, j] * cost_av[i][j] for i in range(len(K_D)) for j in range(len(K_D))) +
sum(x_h[i, j] * cost_non_av[i][j] for i in range(len(K_D)) for j in range(len(K_D))) +
x_a[0, :] @ np.repeat(vehicle_cost, len(K_D)) +
x_h[0, :] @ np.repeat(vehicle_cost, len(K_D)),
GRB.MINIMIZE)
# Solve the VRP
model.optimize()
# Write the gurobi solution to a file
# model.write(dir+'/solutiom.sol')
np.savetxt(dir + '/x_h.txt', x_h.X)
np.savetxt(dir + '/x_a.txt', x_a.X)
# Extract the routes
# routes = []
# for m in fleet:
# if y[0, m].X == 0:
# routes.append([])
# continue
# route_m = []
# start = 0
# next = np.where(x[0, :, m].X > .5)[0][0]
# while next != 0:
# if m < num_av:
# route_m = route_m + path_av[str(K_D[start])+'-'+str(K_D[next])][1:]
# else:
# route_m = route_m + path_non_av[str(K_D[start])+'-'+str(K_D[next])][1:]
# start = next
# next = np.where(x[next, :, m].X > .5)[0][0]
# if m < num_av:
# route_m = route_m + path_av[str(K_D[start])+'-'+str(K_D[0])][1:]
# else:
# route_m = route_m + path_non_av[str(K_D[start])+'-'+str(K_D[0])][1:]
# route_m.insert(0, K_D[0])
# routes.append(route_m)
"""
A generic VRP solver (by Gurobi)
"""
def generic_vrp_solver(G, customer, demand, depot, fleet, num_av, capacity, vehicle_cost, dir, t_max, gran, vehicle_range=None):
# Compute all shortest paths among clients
K_D = depot + customer
cost_non_av = np.zeros((len(customer) + 1, len(customer) + 1))
cost_av = np.zeros((len(customer) + 1, len(customer) + 1))
path_non_av = {}
path_av = {}
for i in range(len(K_D)):
for j in range(len(K_D)):
if i!=j:
cost_non_av[i][j] = nx.shortest_path_length(G, K_D[i], K_D[j], weight='non_av_cost')
cost_av[i][j] = nx.shortest_path_length(G, K_D[i], K_D[j], weight='av_cost')
path_non_av[str(K_D[i])+'-'+str(K_D[j])] = nx.shortest_path(G, K_D[i], K_D[j], weight='non_av_cost')
path_av[str(K_D[i])+'-'+str(K_D[j])] = nx.shortest_path(G, K_D[i], K_D[j], weight='av_cost')
# Create a new model
model = gp.Model('generic_vrp')
# Set the file path for logging
path = dir + '/generic_vrp_log.txt'
if os.path.exists(path):
os.remove(path)
model.Params.LogFile = path
# Parameters tuning for the MIP solver
model.Params.MIPFocus = 3 # Focusing on the phase 2 (moving bounds)
# model.Params.MIPGap = 0 # Specify the terminating duality gap
# Create variables
x = model.addMVar((len(K_D), len(K_D), len(fleet)), vtype=GRB.BINARY)
y = model.addMVar((len(K_D), len(fleet)), vtype=GRB.BINARY)
h = model.addMVar((len(customer), len(fleet)), vtype=GRB.CONTINUOUS, ub=sum(demand))
# Specify an initial feasible solution
# x.setAttr("Start", x_init)
# y.setAttr("Start", y_init)
# h.setAttr("Start", h_init)
# Constraints
for i in range(len(K_D)):
model.addConstrs(sum(x[:, i, m]) == sum(x[i, :, m]) for m in fleet)
model.addConstrs(x[i, i, m] == 0 for m in fleet)
model.addConstrs(sum(x[:, i, m]) == y[i, m] for m in fleet)
#! We should not add the same constraint twice
model.addConstrs(sum(y[i, :]) == 1 for i in range(1, len(K_D)))
model.addConstr(sum(y[0, :]) <= len(fleet))
if vehicle_range != None:
model.addConstrs(sum(x[i, j, m] * cost_non_av[i][j] for i in range(len(K_D)) for j in range(len(K_D))) <= range[m]
for m in fleet)
model.addConstrs(sum(demand[i] * y[i+1, m] for i in range(len(customer))) <= capacity
for m in fleet)
for i in range(len(customer)):
for j in range(len(customer)):
model.addConstrs(h[j, m] >= h[i, m] + capacity * (x[i+1, j+1, m] - 1) + demand[i] for m in fleet)
model.addConstrs(h[i, m] >= demand[i] for m in fleet for i in range(len(customer)))
# Set the bbjective
model.setObjective(sum(x[i, j, m] * cost_av[i][j] for i in range(len(K_D)) for j in range(len(K_D)) for m in fleet[: num_av]) +
sum(x[i, j, m] * cost_non_av[i][j] for i in range(len(K_D)) for j in range(len(K_D)) for m in fleet[num_av :]) +
y[0, :] @ np.repeat(vehicle_cost, len(fleet)), GRB.MINIMIZE)
# Solve the VRP
model.optimize()
# Extract the routes
routes = []
for m in fleet:
if y[0, m].X == 0:
routes.append([])
continue
route_m = []
start = 0
next = np.where(x[0, :, m].X > .5)[0][0]
while next != 0:
if m < num_av:
route_m = route_m + path_av[str(K_D[start])+'-'+str(K_D[next])][1:]
else:
route_m = route_m + path_non_av[str(K_D[start])+'-'+str(K_D[next])][1:]
start = next
next = np.where(x[next, :, m].X > .5)[0][0]
if m < num_av:
route_m = route_m + path_av[str(K_D[start])+'-'+str(K_D[0])][1:]
else:
route_m = route_m + path_non_av[str(K_D[start])+'-'+str(K_D[0])][1:]
route_m.insert(0, K_D[0])
routes.append(route_m)
# Write the routes to a json file
dict_routes = dict(zip(range(len(routes)), routes))
with open(dir + '/routes.json', 'w') as outfile:
json.dump(dict_routes, outfile)
# Write the time stamps to a json file
time_stamps = utils.time_tracker_all_routes(G, routes)
dict_time_stamps = dict(zip(range(len(time_stamps)), time_stamps))
with open(dir + '/all_time_stamps.json', 'w') as outfile:
json.dump(dict_time_stamps, outfile)
# Write the control vector to a json file
control = utils.control_tracker(G, routes, fleet, num_av, t_max, gran)
num_control = {'control_vector': list(np.sum(control, axis=0))}
with open(dir + '/control.json', 'w') as outfile:
json.dump(num_control, outfile)
# Output which vehicle serve which customers
clusters = []
for m in range(y.shape[1]):
if sum(y[:, m].X) == 0:
continue
# customers = np.where(y[:, m].X == 1)[0].tolist()
K_D_subset = []
for ind in np.where(y[:, m].X == 1)[0].tolist():
K_D_subset.append(K_D[ind])
clusters.append(K_D_subset)
dict_clusters = dict(zip(range(len(clusters)), clusters))
with open(dir + '/clusters.json', 'w') as outfile:
json.dump(dict_clusters, outfile)
# Visualize routes
# utils.visualize_separate_routes(G, routes, num_av, dir)
#TODO： Make a wrapper for the re-scheduling MILP solver
class ReSchedulingFRPSolver():
def __init__(self, G, routes, all_time_stamps, t_max, budget, dir, gamma) -> None:
self.G = G
self.routes = routes
self.all_time_stamps = all_time_stamps
self.t_max = t_max
self.budget = budget
self.dir = dir
self.gamma = gamma
# A helper function determineing whether job i and job j are in the same route
def __in_the_same_route(self, i, j, num_job):
b0 = False
cum_num = np.cumsum(num_job)
cum_num = np.insert(cum_num, 0, 0)
for ind in range(1, len(cum_num)):
b1 = cum_num[ind-1] <= i and i < cum_num[ind]
b2 = cum_num[ind-1] <= j and j < cum_num[ind]
if b1 and b2:
b0 = True
return b0
def solve(self, obj = "makespan"):
num_job = utils.convert_route_to_job(self.G, self.routes)
all_job_start, all_job_end = utils.extract_start_n_end_time(self.G, self.all_time_stamps)
# Write donw the old schedule
dict_all_job_start = dict(zip(range(len(all_job_start)), all_job_start))
dict_all_job_end = dict(zip(range(len(all_job_end)), all_job_end))
with open(dir + '/old_schedule_start.json', 'w') as outfile:
json.dump(dict_all_job_start, outfile)
with open(dir + '/old_schedule_end.json', 'w') as outfile:
json.dump(dict_all_job_end, outfile)
# max_job_end = [np.max(sublist) for sublist in all_job_end]
all_job_start = np.array([job for sublist in all_job_start for job in sublist])
all_job_end = np.array([job for sublist in all_job_end for job in sublist])
duration = all_job_end - all_job_start
# Create a new model
model = gp.Model('rescheduling')
# Set the file path for logging and disable the console output
path = self.dir + '/re_scheduling_frp_log.log'
if os.path.exists(path):
os.remove(path)
model.Params.LogFile = path
model.Params.LogToConsole = 0
# strategies
# model.Params.MIPFocus = 1 # Focusing on phase 1 (getting the first feasible solution)
model.Params.MIPFocus = 3 # Focusing on phase 2 (moving the best upper bound)
model.Params.ImproveStartTime = 120 # Give up proving optimality
# Termination
model.Params.WorkLimit = 30 # Terminate the MILP solver after
model.Params.SolutionLimit = 1 # Terminate the MILP when the first feasible solution found
if obj == "makespan":
# Define variables
z = model.addMVar((sum(num_job), self.budget), vtype=GRB.BINARY)
c = model.addMVar(sum(num_job), vtype=GRB.CONTINUOUS, lb = all_job_start + duration, ub = np.repeat(self.t_max, sum(num_job)))
f = model.addMVar((sum(num_job), sum(num_job)), vtype=GRB.BINARY)
c_max = model.addVar(lb = max(all_job_end), ub = self.t_max, vtype=GRB.CONTINUOUS)
# Constraints
# Unique assignment of job
model.addConstrs(sum(z[i, :]) == 1 for i in range(sum(num_job)))
# Maximum delay for each pair of job
model.addConstrs(c[j] - duration[j] - c[i] <= self.gamma[1] * (all_job_start[j] - all_job_start[i] - duration[i])
for i in range(sum(num_job)) for j in range(sum(num_job)) if i == j-1 and j not in np.cumsum(num_job))
# The precedence constraint
model.addConstrs(c[j] - duration[j] - c[i] >= (all_job_start[j] - all_job_start[i] - duration[i])
for i in range(sum(num_job)) for j in range(sum(num_job)) if i == j-1 and j not in np.cumsum(num_job))
# Non-overlapping jobs in a single machine
model.addConstrs(c[i] <= self.t_max * (3 - z[i, b] - z[j, b] - f[i, j]) + c[j] - duration[j]
for i in range(sum(num_job)) for j in range(sum(num_job)) for b in range(self.budget)
if not self.__in_the_same_route(i, j, num_job))
model.addConstrs(c[j] <= self.t_max * (2 - z[i, b] - z[j, b] + f[i, j]) + c[i] - duration[i]
for i in range(sum(num_job)) for j in range(sum(num_job)) for b in range(self.budget)
if not self.__in_the_same_route(i, j, num_job))
# f_ij = 1 iff the start time of job i is ahead of the start time of job j
model.addConstrs(c[i] <= c[j] + self.t_max * (1 - f[i, j])
for i in range(sum(num_job)) for j in range(sum(num_job)) if not self.__in_the_same_route(i, j, num_job))
model.addConstrs(c[j] <= c[i] + self.t_max * f[i, j]
for i in range(sum(num_job)) for j in range(sum(num_job)) if not self.__in_the_same_route(i, j, num_job))
# c <= c_max for all c
model.addConstrs(c[i] <= c_max for i in range(sum(num_job)))
# Set the objective
model.setObjective(c_max, GRB.MINIMIZE)
elif obj == "minimum violation":
# Define variable
z = model.addMVar((sum(num_job), self.budget), vtype=GRB.BINARY)
c = model.addMVar(sum(num_job), vtype=GRB.CONTINUOUS, lb = all_job_start + duration, ub = np.repeat(self.t_max, sum(num_job)))
y = model.addMVar((sum(num_job), sum(num_job), self.budget), vtype=GRB.BINARY)
alpha = model.addMVar((sum(num_job), sum(num_job)), vtype=GRB.BINARY)
beta = model.addMVar((sum(num_job), sum(num_job)), vtype=GRB.BINARY)
# Add constraints
# Unique assignment of job
model.addConstrs(sum(z[i, :]) == 1 for i in range(sum(num_job)))
# Time stamps consistency
#TODO:
# Count the number of budget violation
model.addConstrs((z[i, b] + z[j, b] + alpha[i, j] + beta[i, j]) / 3 <= y[i, j, b] + 1
for i in range(sum(num_job))
for j in range(sum(num_job))
for b in range(self.budget)
if not self.__in_the_same_route(i, j, num_job))
model.addConstrs(4 * y[i, j, b] <= z[i, b] + z[j, b] + alpha[i, j] + beta[i, j]
for i in range(sum(num_job))
for j in range(sum(num_job))
for b in range(self.budget)
if not self.__in_the_same_route(i, j, num_job))
# Set the objective
model.setObjective(sum(y[i, j, b] for i in range(len(num_job)) for j in range(len(num_job)) for b in range(self.budget)), GRB.MINIMIZE)
else:
raise ValueError("The input is invalid.")
# if obj == "average_completion":
# weight = np.repeat(1/len(num_job), len(num_job))
# model.setObjective(c[np.cumsum(num_job)-1] @ weight, GRB.MINIMIZE)
# Solve the scheduling feasibility recovering problem
model.optimize()
# Return 0 if the re-scheduling FRP is not feasible
if model.Status == 3:
print(colored("The re-scheduling MILP is infeasible.", 'yellow'))
return 0
if model.Status == 16 and model.SolCount == 0:
print(colored("No feasible solution found within the work limit.", 'yellow'))
return -1
print(colored("Optimal solution found for the re-scheduling MILP!", 'green'))
# Write down the new schedule
new_start = c.X - duration
cum_sum_job = np.cumsum(num_job)
new_schedule_end = [list(c[: cum_sum_job[0]].X)] + [list(c[cum_sum_job[i]: cum_sum_job[i+1]].X) for i in range(len(cum_sum_job)-1)]
new_schedule_start = [list(new_start[: cum_sum_job[0]])] + [list(new_start[cum_sum_job[i]: cum_sum_job[i+1]]) for i in range(len(cum_sum_job)-1)]
dict_new_schedule_end = dict(zip(range(len(new_schedule_end)), new_schedule_end))
dict_new_schedule_start = dict(zip(range(len(new_schedule_start)), new_schedule_start))
with open(dir + '/new_schedule_end.json', 'w') as outfile:
json.dump(dict_new_schedule_end, outfile)
with open(dir + '/new_schedule_start.json', 'w') as outfile:
json.dump(dict_new_schedule_start, outfile)
return 1
"""
Re-scheduling FRP
"""
#TODO: The MILP is different from the paper: travel time is not flexible in ordinary roads. May need to change it later.
def frp_reschedule(G, routes, all_time_stamps, t_max, budget, dir, gamma, obj="makespan"):
num_job = utils.convert_route_to_job(G, routes)
all_job_start, all_job_end = utils.extract_start_n_end_time(G, all_time_stamps)
# Write donw the old schedule
dict_all_job_start = dict(zip(range(len(all_job_start)), all_job_start))
dict_all_job_end = dict(zip(range(len(all_job_end)), all_job_end))
with open(dir + '/old_schedule_start.json', 'w') as outfile:
json.dump(dict_all_job_start, outfile)
with open(dir + '/old_schedule_end.json', 'w') as outfile:
json.dump(dict_all_job_end, outfile)
# max_job_end = [np.max(sublist) for sublist in all_job_end]
all_job_start = np.array([job for sublist in all_job_start for job in sublist])
all_job_end = np.array([job for sublist in all_job_end for job in sublist])
duration = all_job_end - all_job_start
# A helper function determineing whether job i and job j are in the same route
def in_the_same_route(i, j, num_job):
b0 = False
cum_num = np.cumsum(num_job)
cum_num = np.insert(cum_num, 0, 0)
for ind in range(1, len(cum_num)):
b1 = cum_num[ind-1] <= i and i < cum_num[ind]
b2 = cum_num[ind-1] <= j and j < cum_num[ind]
if b1 and b2:
b0 = True
return b0
# Create a new model
model = gp.Model('rescheduling')
# Set the file path for logging and disable the console output
path = dir + '/re_scheduling_frp_log.log'
if os.path.exists(path):
os.remove(path)
model.Params.LogFile = path
model.Params.LogToConsole = 0
# strategies
# model.Params.MIPFocus = 1 # Focusing on phase 1 (getting the first feasible solution)
model.Params.MIPFocus = 3 # Focusing on phase 2 (moving the best upper bound)
model.Params.ImproveStartTime = 120 # Give up proving optimality
# Termination
model.Params.WorkLimit = 30 # Terminate the MILP solver after
model.Params.SolutionLimit = 1 # Terminate the MILP when the first feasible solution found
# Define variables
z = model.addMVar((sum(num_job), budget), vtype=GRB.BINARY)
c = model.addMVar(sum(num_job), vtype=GRB.CONTINUOUS, lb = all_job_start + duration, ub = np.repeat(t_max, sum(num_job)))
f = model.addMVar((sum(num_job), sum(num_job)), vtype=GRB.BINARY)
if obj == "makespan":
c_max = model.addVar(lb = max(all_job_end), ub = t_max, vtype=GRB.CONTINUOUS)
# Constraints
# Unique assignment of job
model.addConstrs(sum(z[i, :]) == 1 for i in range(sum(num_job)))
# Maximum delay for each pair of job
model.addConstrs(c[j] - duration[j] - c[i] <= gamma[1] * (all_job_start[j] - all_job_start[i] - duration[i])
for i in range(sum(num_job)) for j in range(sum(num_job)) if i == j-1 and j not in np.cumsum(num_job))
# The precedence constraint
model.addConstrs(c[j] - duration[j] - c[i] >= (all_job_start[j] - all_job_start[i] - duration[i])
for i in range(sum(num_job)) for j in range(sum(num_job)) if i == j-1 and j not in np.cumsum(num_job))
# Non-overlapping jobs in a single machine
model.addConstrs(c[i] <= t_max * (3 - z[i, b] - z[j, b] - f[i, j]) + c[j] - duration[j]
for i in range(sum(num_job)) for j in range(sum(num_job)) for b in range(budget)
if not in_the_same_route(i, j, num_job))
model.addConstrs(c[j] <= t_max * (2 - z[i, b] - z[j, b] + f[i, j]) + c[i] - duration[i]
for i in range(sum(num_job)) for j in range(sum(num_job)) for b in range(budget)
if not in_the_same_route(i, j, num_job))
# f_ij = 1 iff the start time of job i is ahead of the start time of job j
model.addConstrs(c[i] <= c[j] + t_max * (1 - f[i, j])
for i in range(sum(num_job)) for j in range(sum(num_job)) if not in_the_same_route(i, j, num_job))
model.addConstrs(c[j] <= c[i] + t_max * f[i, j]
for i in range(sum(num_job)) for j in range(sum(num_job)) if not in_the_same_route(i, j, num_job))
# c <= c_max for all c
if obj == "makespan":
model.addConstrs(c[i] <= c_max for i in range(sum(num_job)))
# print(c[np.cumsum(num_job)-1])
# Set the objective
if obj == "makespan":
model.setObjective(c_max, GRB.MINIMIZE)
if obj == "average_completion":
weight = np.repeat(1/len(num_job), len(num_job))
model.setObjective(c[np.cumsum(num_job)-1] @ weight, GRB.MINIMIZE)
# Solve the scheduling feasibility recovering problem
model.optimize()
# Return 0 if the re-scheduling FRP is not feasible
if model.Status == 3:
print(colored("The re-scheduling MILP is infeasible.", 'yellow'))
return 0
if model.Status == 16 and model.SolCount == 0:
print(colored("No feasible solution found within the work limit.", 'yellow'))
return -1
print(colored("Optimal solution found for the re-scheduling MILP!", 'green'))
#* [Comment out this block if output is not needed]
# Write down the new schedule
new_start = c.X - duration
cum_sum_job = np.cumsum(num_job)
new_schedule_end = [list(c[: cum_sum_job[0]].X)] + [list(c[cum_sum_job[i]: cum_sum_job[i+1]].X) for i in range(len(cum_sum_job)-1)]
new_schedule_start = [list(new_start[: cum_sum_job[0]])] + [list(new_start[cum_sum_job[i]: cum_sum_job[i+1]]) for i in range(len(cum_sum_job)-1)]
dict_new_schedule_end = dict(zip(range(len(new_schedule_end)), new_schedule_end))
dict_new_schedule_start = dict(zip(range(len(new_schedule_start)), new_schedule_start))
with open(dir + '/new_schedule_end.json', 'w') as outfile:
json.dump(dict_new_schedule_end, outfile)
with open(dir + '/new_schedule_start.json', 'w') as outfile:
json.dump(dict_new_schedule_start, outfile)
return 1
"""
Graph pre-pruning algorithm
"""
def graph_pre_pruning(G, depot, customer):
E = list(G.edges)
V = list(G.nodes)
for e in E:
if G.edges[e]['is_av'] == 1:
G.edges[e]['control_time'] = 0
else:
G.edges[e]['control_time'] = G.edges[e]['length']
remaining_edge = []
remaining_node = []
for i in depot + customer:
for j in depot + customer:
if i == j:
continue
p1 = nx.shortest_path(G, i, j, weight = 'length')
p2 = nx.shortest_path(G, i, j, weight = 'control_time')
remaining_node += p1
remaining_node += p2
for k in range(len(p1)-1):
remaining_edge.append((p1[k], p1[k+1]))
for k in range(len(p2)-1):
remaining_edge.append((p2[k], p2[k+1]))
remaining_edge = set(remaining_edge)
remaining_node = set(remaining_node)
edge_to_delete = set(E) - remaining_edge
# Remove a set of nodes
#* Comment this line to visualize how many nodes are deleted
G = G.subgraph(remaining_node)
# Make a copy to unfreeze the graph
G_s = G.copy()
# Remove a set of edges
G_s.remove_edges_from(edge_to_delete)
# G_s = utils.generate_av_roads_primary(G_s)
E = list(G_s.edges)
V = list(G_s.nodes)
for v in list(set(V) - set(customer) - set(depot)):
succ = list(G_s.successors(v))
pred = list(G_s.predecessors(v))
# Merge all one way roads
if len(succ) == 1 and len(pred) == 1:
# print(v)
# print(G_s[pred[0]][v])
# print("============")
# Road types
rt1 = G_s[pred[0]][v]['is_av']
rt2 = G_s[v][succ[0]]['is_av']
# Road length
rl1 = G_s[pred[0]][v]['length']
rl2 = G_s[v][succ[0]]['length']
if rt1 == 1 and rt2 == 1:
G_s.add_edge(pred[0], succ[0], is_av = rt1, is_artificial = 0, length = rl1 + rl2)
G_s.remove_edge(pred[0], v)
G_s.remove_edge(v, succ[0])
#* Comment this line to visualize how many nodes are deleted
G_s.remove_node(v)
if rt1 == 0 and rt2 == 0:
G_s.add_edge(pred[0], succ[0], is_av = rt1, is_artificial = 0, length = rl1 + rl2)
G_s.remove_edge(pred[0], v)
G_s.remove_edge(v, succ[0])
#* Comment this line to visualize how many nodes are deleted
G_s.remove_node(v)
elif len(pred) == 2 and set(pred) == set(succ):
# Road types
rt1 = G_s[pred[0]][v]['is_av']
rt2 = G_s[v][pred[1]]['is_av']
# Road length
rl1 = G_s[pred[0]][v]['length']
rl2 = G_s[v][pred[1]]['length']
if rt1 == 1 and rt2 == 1:
G_s.add_edge(pred[0], pred[1], is_av = rt1, is_artificial = 0, length = rl1 + rl2)
G_s.add_edge(pred[1], pred[0], is_av = rt1, is_artificial = 0, length = rl1 + rl2)
G_s.remove_edge(pred[0], v)
G_s.remove_edge(v, pred[0])
G_s.remove_edge(pred[1], v)
G_s.remove_edge(v, pred[1])
G_s.remove_node(v)
if rt1 == 0 and rt2 == 0:
G_s.add_edge(pred[0], pred[1], is_av = rt1, is_artificial = 0, length = rl1 + rl2)
G_s.add_edge(pred[1], pred[0], is_av = rt1, is_artificial = 0, length = rl1 + rl2)
G_s.remove_edge(pred[0], v)
G_s.remove_edge(v, pred[0])
G_s.remove_edge(pred[1], v)
G_s.remove_edge(v, pred[1])
G_s.remove_node(v)
else:
pass
return G_s
"""
Re-routing FRP
"""
class ReRoutingFRPSolver():
def __init__(self, G, I, routes, all_time_stamps, clusters, t_max, budget, gamma, num_layer, eta, dir) -> None:
# Members from input
self.G = G
for e in self.G.edges:
self.G[e[0]][e[1]]['is_artificial'] = 0
self.I = I
self.routes = routes
self.all_time_stamps = all_time_stamps
self.clusters = clusters
self.demand = I.demands
self.capacity = I.capacity
self.budget = budget
self.t_max = t_max
self.gamma = gamma
self.num_layer = num_layer
self.dir = dir
self.eta = eta
# Members for output (dynamically updated)
self.new_routes = {}
self.new_all_time_stamps = {}
self.G_adjusted = G # Need to be output as well
def budget_violation_check(self):
all_job_start, all_job_end = utils.extract_start_n_end_time(self.G_adjusted, self.new_all_time_stamps)
new_all_time_stamps_list = list(set([element for innerList in all_job_start + all_job_end for element in innerList]))
new_all_time_stamps_list.sort()
new_all_time_stamps_list.append(self.t_max)
Q = np.arange(len(new_all_time_stamps_list)-1)
for i in range(len(Q)):
ss = new_all_time_stamps_list[i]
tt = new_all_time_stamps_list[i+1]
control_q = 0
for v in range(len(self.new_all_time_stamps)):
bb = False
sss = all_job_start[v]
ttt = all_job_end[v]
for j in range(len(sss)):
if ss >= sss[j] and tt <= ttt[j]:
bb = True
if bb == True:
control_q += 1
if control_q > self.budget:
return True
return False
def constrained_tsp_solver(self, v_2_re_route, budget_indicator, new_all_time_stamps_list):
Q = np.arange(len(budget_indicator))
K = self.clusters[v_2_re_route][:1]
D = self.clusters[v_2_re_route][1:]
demand_subset = []
for cus in D:
cus_index = int(cus[-1][1:])
demand_subset.append(self.demand[cus_index])
G_s = graph_pre_pruning(self.G, K, D)
# Add merged edges in the pruned graph to the original graph for time stamps extraction in next iteration
self.G_adjusted = utils.graph_edge_adjustment(G_s, self.G_adjusted)
V = list(G_s.nodes)
_, new_to_old = utils.re_index_nodes(V)
G_e, departure, arrival, D_e, D_e_2d, demand_dict = utils.contruct_time_expanded_graph(G_s, self.num_layer, K, D, demand_subset)
utils.assign_cost_n_travel_time(G_e, self.eta[0], self.eta[1])
E_e = list(G_e.edges)
V_e = list(G_e.nodes)
edge_to_no, _ = utils.re_index_edges(E_e)
# Create a new model
model = gp.Model('re-routing-vehicle-'+str(v_2_re_route))
# Model params
# logging
path = self.dir + '/re_routing_log_vehicle_'+str(v_2_re_route)+'.log'
if os.path.exists(path):
os.remove(path)
model.Params.LogFile = path
model.Params.LogToConsole = 0
# strategies
# model.Params.MIPFocus = 1 # Focusing on phase 1 (getting the first feasible solution)
model.Params.MIPFocus = 3 # Focusing on phase 2 (moving the best upper bound)
model.Params.ImproveStartTime = 120 # Give up proving optimality after 2 min
# Termination
model.Params.BestObjStop = 1.05 * self.original_route_cost(v_2_re_route) # The original route cost is a natural lower bound.
# A re-route is good if the cost is < 1.05 original route cost
model.Params.WorkLimit = 30 # Terminate the MILP solver
#TODO: Replacing AV cost with HDV cost naively is incorrect. We need to re-solve the TSP
model.Params.Cutoff = self.HDV_route_cost(v_2_re_route) # cutoff the solution with even worse cost than dispatching an HDV
# Create variables
#* The lower bounds for all variables are zero by default
x = model.addMVar(len(E_e), vtype=GRB.BINARY)
y = model.addMVar(len(V_e), vtype=GRB.BINARY)
t = model.addMVar(len(V_e), ub = np.repeat(self.t_max, len(V_e)), vtype=GRB.CONTINUOUS)
alpha = model.addMVar((len(E_e), len(Q)), vtype=GRB.BINARY)
beta = model.addMVar((len(E_e), len(Q)), vtype=GRB.BINARY)
#// makespan = model.addVar(ub = t_max, vtype=GRB.CONTINUOUS)
# Add constraints
## Flow conservation at all nodes (except the depot)
for i in list(set(V_e) - set([departure, arrival])):
out_no = [edge_to_no[e] for e in list(G_e.out_edges(i))]
in_no = [edge_to_no[e] for e in list(G_e.in_edges(i))]
model.addConstr(sum(x[out_no]) == sum(x[in_no]))
model.addConstr(sum(x[out_no]) <= 1)
## Flow conservation at the depot
departure_out = [edge_to_no[e] for e in list(G_e.out_edges(departure))]
arrival_in = [edge_to_no[e] for e in list(G_e.in_edges(arrival))]
model.addConstr(sum(x[departure_out]) == y[departure])
model.addConstr(sum(x[arrival_in]) == y[arrival])
model.addConstr(y[departure] == y[arrival])
## Prohibit vehicles from passing through the depot
departure_in = [edge_to_no[e] for e in list(G_e.in_edges(departure))]
model.addConstr(x[departure_in] == 0)
## Allow a vehicle to pass thru a customer without serving it
for i in D_e:
i_in = [edge_to_no[e] for e in list(G_e.in_edges(i))]
model.addConstr(sum(x[i_in]) >= y[i])
## Each customer is only served by one vehicle
model.addConstrs(sum(y[i] for i in D_e_2d[:, h]) == 1 for h in range(len(D)))
## Transition
for i in D_e:
model.addConstr(sum(x[edge_to_no[(i, j)]] for j in D_e if G_e.has_edge(i, j) and G_e[i][j]['is_artificial'] == 1) <= y[i])
## Capacity limit
model.addConstr(sum(demand_dict[i] * y[i] for i in D_e) <= self.capacity)
## Subtour elimination + time tracking
for i in V_e:
for j in V_e:
if G_e.has_edge(i, j):
model.addConstr(t[j] >= t[i] + self.gamma[0] * G_e[i][j]['travel_time'] + self.t_max * (x[edge_to_no[(i, j)]] - 1))
model.addConstr(t[j] <= t[i] + self.gamma[1] * G_e[i][j]['travel_time'] + self.t_max * (1 - x[edge_to_no[(i, j)]]))
# The following constraints can be captured by the previous constraint
#// for e in E_e:
#// i, j = e[:2]
#// # We need one more set of important constraints, which equals the time stamp of the start node to that of the end node in any aritificial edges.
#// if G_e[i][j]['is_artificial'] == 1: