(40,500),
(30,400)
]
nbKids=300
# Indexes
busSize=0;
busCost=1;
for b in Buses:
print("buses with ",b[busSize]," seats cost ",b[busCost])
mdl = CpoModel(name='buses')
#decision variables
mdl.nbBus=mdl.integer_var_dict(Buses,0,1000,name="nbBus")
# Constraint
mdl.add(mdl.sum(mdl.nbBus[b]*b[busSize] for b in Buses) >= nbKids)
# Objective
mdl.minimize(mdl.sum(mdl.nbBus[b]*b[busCost] for b in Buses))
msol=mdl.solve()
# Dislay solution
for b in Buses:
print(msol[mdl.nbBus[b]]," buses with ",b[busSize]," seats");
busSize = 0
busCost = 1
for b in Buses:
print("buses with ", b[busSize], " seats cost ", b[busCost])
print()
for b in Buses:
print("buses with ", b[busSize], " seats cost ", b[busCost])
mdl = CpoModel(name='buses')
#decision variables
mdl.nbBus = mdl.integer_var_dict(Buses, 0, maxquantity, name="nbBus")
# Constraint
mdl.add(sum(mdl.nbBus[b] * b[busSize] for b in Buses) >= nbKids)
# Objective
mdl.minimize(sum(mdl.nbBus[b] * b[busCost] for b in Buses))
msol = mdl.solve()
# Dislay solution
for b in Buses:
print(msol[mdl.nbBus[b]], " buses with ", b[busSize], " seats")
#Add a constraint
# Number of different quantity should be less than 1
def LNS_VRPTW(visit_fixed, time_fixed, distance, custs, vehicles, capacity, speed, resources, service_time, time_win):
custs_w_depot = [0] + custs + [len(custs) + 1]
time_min = min(time_win.values(), key = lambda x: x[0])[0]
time_max = max(time_win.values(), key = lambda x: x[1])[1]
mdl = CpoModel("VRPTW")
# decision variables
visit = [(i,j,k) for i in custs_w_depot for j in custs_w_depot for k in vehicles]
time = [(i, k) for i in custs for k in vehicles]
visit_var = mdl.integer_var_dict(visit, name = "visit")
time_var = mdl.integer_var_dict(time, name = "time")
# add constraints
# Range
for key in visit_var:
mdl.add(visit_var[key]>=0)
mdl.add(visit_var[key]<=1)
# route constraints
for i in custs:
mdl.add(mdl.sum(visit_var[(i, j, k)] for j in custs_w_depot for k in vehicles) == 1)
# capacity
for k in vehicles:
mdl.add(mdl.sum(visit_var[(i, j, k)] * resources[i] for i in custs for j in custs_w_depot) <= capacity)
# from depot
for k in vehicles:
mdl.add(mdl.sum(visit_var[(0, j, k)] for j in custs) == 1)
mdl.add(mdl.sum(visit_var[(i, 0, k)] for i in custs) == 0)
# flow
for k in vehicles:
for p in custs:
mdl.add(mdl.sum(visit_var[(i, p, k)] for i in custs_w_depot) == mdl.sum(visit_var[p, j, k] for j in custs_w_depot))
# to depot
for k in vehicles:
mdl.add(mdl.sum(visit_var[(i, len(custs)+1, k)] for i in custs) == 1)
mdl.add(mdl.sum(visit_var[(len(custs)+1, j, k)] for j in custs) == 0)
# cannot start and end from self
for k in vehicles:
for i in custs:
mdl.add(visit_var[i, i, k] == 0)
# time constraint between 2 customers
for i in custs:
for j in custs:
for k in vehicles:
mdl.add(visit_var[(i, j, k)]*(time_var[i, k] + distance[i][j]/speed - time_var[j, k]) <= 0)
# time window constraint
for i in custs:
mdl.add(mdl.sum(visit_var[i, j, k] * time_var[i, k] for j in custs_w_depot for k in vehicles) >= time_win[i][0])
mdl.add(mdl.sum(visit_var[i, j, k] * time_var[i, k] for j in custs_w_depot for k in vehicles) <= time_win[i][1])
# time within range
for i in custs:
mdl.add(mdl.sum(visit_var[i, j, k] * time_var[i, k] for j in custs_w_depot for k in vehicles) >= time_min)
mdl.add(mdl.sum(visit_var[i, j, k] * time_var[i, k] for j in custs_w_depot for k in vehicles) <= time_max)
# known route
for key in visit_fixed:
mdl.add(visit_var[key] == visit_fixed[key])
for key in time_fixed:
mdl.add(time_var[key] == time_fixed[key])
# add objective
obj = mdl.minimize(mdl.sum(visit_var[(i, j, k)] * distance[i][j] for i in custs_w_depot for j in custs_w_depot for k in vehicles))
mdl.add(obj)
sol = mdl.solve(TimeLimit = 5, LogVerbosity = "Quiet", agent='local',
execfile='/Applications/CPLEX_Studio201/cpoptimizer/bin/x86-64_osx/cpoptimizer')
# sol = mdl.solve(TimeLimit = 5, LogVerbosity = "Verbose", agent='local',
# execfile='/Applications/CPLEX_Studio201/cpoptimizer/bin/x86-64_osx/cpoptimizer')
if not sol:
return None
# sol.write()
# new route info
# distance
distance_result = sol.get_objective_values()[0]
if (type(distance_result) == tuple):
distance_result = distance_result[0]
# print("distance:", distance_result)
# route
routes = []
for k in vehicles:
# route_seq = [(i, j) for i in custs_w_depot for j in custs_w_depot \
# if sol.get_var_solution(visit_var[i, j, k]).get_value() != 0]
# print("route", route_seq)
time = [(i, sol.get_var_solution(time_var[i, k]).get_value()) for i in custs \
if time_min <= sol.get_var_solution(time_var[i, k]).get_value() <= time_max]
time.sort(key = lambda x: x[1])
route = [{"id": x[0], "begin_time": x[1]} for x in time]
routes.append(route)
return routes, distance_result
