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import pandas
import pyomo.opt
import pyomo.environ as pe
import scipy
import itertools
import cplex
import logging
#DEFINE GLOBAL NAMES HERE
CREWDATA_CSV = 'CrewData.csv'
DEMANDDATA_CSV = 'DemandData.csv'
VACATIONDATA_CSV = 'VacationData.csv'
crew_df = pandas.read_csv(CREWDATA_CSV)
demand_df = pandas.read_csv(DEMANDDATA_CSV)
vacation_df = pandas.read_csv(VACATIONDATA_CSV)
def get_demand(rank, fleet, base, week):
# example: base = "B1", fleet = "A330", rank = "FO", week = 0
# return the demand at B1, A330, FO of week 0
return demand_df['B'+ str(base) + '_' + fleet[1:] + rank][week]
def get_nonfix_pilots():
return set(crew_df[(crew_df.Bid_BaseChange.notnull()) | (crew_df.Bid_FleetChange.notnull())| (crew_df.Bid_RankChange.notnull())]['Crew_ID'])
def get_all_pilots():
return set(crew_df[crew_df.Rank != "SIM_INS"]['Crew_ID'])
####trainer Pilots
trainers = set(crew_df[(crew_df.Instructor == "TR3233_1")]['Crew_ID'])
#### Seniority set[1,2,3,4]
se_1 = set(crew_df[(crew_df.Seniority == 1)]['Crew_ID'])
se_2 = set(crew_df[(crew_df.Seniority == 2)]['Crew_ID'])
se_3 = set(crew_df[(crew_df.Seniority == 3)]['Crew_ID'])
se_4 = set(crew_df[(crew_df.Seniority == 4)]['Crew_ID'])
####fixedPos
def print_duplicate(a):
print [item for item, count in collections.Counter(a).items() if count > 1]
nonfixed_df = crew_df[(crew_df.Bid_BaseChange.notnull()) | (crew_df.Bid_FleetChange.notnull())| (crew_df.Bid_RankChange.notnull())]
fixed_df = crew_df[(~crew_df.Bid_BaseChange.notnull()) & (~crew_df.Bid_FleetChange.notnull()) & (~crew_df.Bid_RankChange.notnull())]
#### toPos
topos_list = []
rank_change = set(crew_df[(crew_df.Bid_RankChange.notnull())]['Crew_ID'])
fleet_change = set(crew_df[(crew_df.Bid_FleetChange.notnull())]['Crew_ID'])
base_change = set(crew_df[(crew_df.Bid_BaseChange.notnull())]['Crew_ID'])
for pilot in set(nonfixed_df['Crew_ID']):
cur = [pilot]
pilot_item = crew_df[crew_df.Crew_ID == pilot]
if pilot in rank_change:
print pilot_item
cur.append('CPT')
cur.append(pilot_item.Cur_Fleet.values[0])
cur.append(pilot_item.Current_Base.values[0])
elif pilot in fleet_change :
cur.append(pilot_item.Rank.values[0])
cur.append(pilot_item.Bid_FleetChange.values[0])
cur.append(pilot_item.Current_Base.values[0])
elif pilot in base_change :
cur.append(pilot_item.Rank.values[0])
cur.append(pilot_item.Cur_Fleet.values[0])
cur.append(pilot_item.Bid_BaseChange.values[0])
topos_list.append(cur)
toPos = pandas.DataFrame(topos_list)
toPos.columns =['ID','RANK','FLEET','BASE']
#### fromPos
frompos_list = []
for pilot in set(nonfixed_df['Crew_ID']):
cur = [pilot]
pilot_item = crew_df[crew_df.Crew_ID == pilot]
cur.append(pilot_item.Rank.values[0])
cur.append(pilot_item.Cur_Fleet.values[0])
cur.append(pilot_item.Current_Base.values[0])
frompos_list.append(cur)
fromPos = pandas.DataFrame(frompos_list)
fromPos.columns =['ID','RANK','FLEET','BASE']
# ALL debuged before this point
model = pe.ConcreteModel()
model.pilots = pe.Set(initialize=get_all_pilots())
nonfix_var_set=[]
fix_var_set = []
all_var_set = []
from_set = []
to_set = []
for pilot in nonfixed_df['Crew_ID'].values:
for fleet in ['A320','A330']:
for base in [1,2]:
for rank in ['CPT','FO']:
in_from = pilot in fromPos[(fromPos.RANK==rank)&(fromPos.FLEET==fleet)&(fromPos.BASE==base)]['ID'].values
in_to = pilot in toPos[(toPos.RANK==rank)&(toPos.FLEET==fleet)&(toPos.BASE==base)]['ID'].values
if(in_from or in_to):
nonfix_var_set.append((pilot,rank,fleet,base))
all_var_set.append((pilot,rank,fleet,base))
if in_from :
from_set.append((pilot,rank,fleet,base))
if in_to :
to_set.append((pilot,rank,fleet,base))
df_fixnew = fixed_df.set_index(['Crew_ID','Rank','Cur_Fleet','Current_Base'])
for pilot in fixed_df['Crew_ID'].values:
for fleet in ['A320','A330']:
for base in [1,2]:
for rank in ['CPT','FO']:
if (pilot, rank, fleet, base) in df_fixnew.index:
fix_var_set.append((pilot, rank, fleet, base))
all_var_set.append((pilot, rank, fleet, base))
model.nonfix_pilots = pe.Set(initialize = nonfixed_df['Crew_ID'].values)
model.nonfix_var_set = pe.Set(initialize = nonfix_var_set)
model.fix_var_set = pe.Set(initialize = fix_var_set)
model.all_var_set = pe.Set(initialize = all_var_set)
model.fix_pilots = model.pilots - model.nonfix_pilots
print "Number of nonfix_pilots is " + str(len(nonfixed_df['Crew_ID'].values))
model.trainer_pilots = pe.Set(initialize = trainers)
model.rank_pilots = pe.Set(initialize = rank_change)
model.fleet_pilots = pe.Set(initialize = fleet_change)
model.base_pilots = pe.Set(initialize = base_change)
model.from_pos = pe.Set(initialize = from_set)
model.to_pos = pe.Set(initialize = to_set)
model.all_pos = pe.Set(initialize = nonfix_var_set)
#new set
nonfixed_trainer=[]
for pilot in nonfixed_df['Crew_ID'].values:
if pilot in model.trainer_pilots:
nonfixed_trainer.append(pilot)
model.trainer_nonfix_pilots = pe.Set(initialize = nonfixed_trainer)
#end new set
model.se_1 = pe.Set(initialize = se_1)
model.se_2 = pe.Set(initialize = se_2)
model.se_3 = pe.Set(initialize = se_3)
model.se_4 = pe.Set(initialize = se_4)
model.fix_pilots = model.pilots - model.nonfix_pilots
model.rank = pe.Set(initialize=['CPT','FO'])
model.fleet = pe.Set(initialize=['A330','A320'])
model.base = pe.Set(initialize=[1,2])
model.time = pe.Set(initialize=range(26))
model.timestart = pe.Set(initialize=range(25))
model.quarterstart = pe.Set(initialize = [0,13])
model.Y = pe.Var(model.nonfix_var_set*model.time, domain=pe.Binary)
# this variable contained all pilots
model.Yall = pe.Var(model.all_var_set*model.time, domain=pe.Binary)
model.shortage = pe.Var(model.rank*model.fleet*model.base*model.time, domain = pe.NonNegativeIntegers)
model.surplus = pe.Var(model.rank*model.fleet*model.base*model.time, domain = pe.NonNegativeIntegers)
model.T = pe.Var(model.trainer_pilots*model.base*model.time, domain=pe.Binary)
model.Trainee = pe.Var(model.fleet_pilots*model.base*model.time, domain=pe.Binary)
model.V = pe.Var(model.pilots*model.time, domain=pe.Binary)
model.VP = pe.Var(model.pilots*model.quarterstart, domain=pe.NonNegativeIntegers)
#only nonfix pilots can take vacation or training?
model.Vposition = pe.Var(model.nonfix_var_set*model.time, domain=pe.Binary)
model.Vfix_position = pe.Var(model.fix_var_set*model.time, domain=pe.Binary)
model.Tposition = pe.Var(model.trainer_pilots*model.rank*model.fleet*model.base*model.time, domain=pe.Binary)
model.Trainee_po = pe.Var(model.fleet_pilots*model.rank*model.fleet*model.base*model.time, domain=pe.Binary)
model.VS = pe.Var(model.pilots*model.time, domain = pe.NonNegativeIntegers)
model.short_cost = pe.Param(model.rank*model.fleet*model.base*model.time, initialize = 70000)
model.normal_cost = pe.Param(model.nonfix_var_set*model.time, initialize = 3500)
model.base_transition_cost = pe.Param(model.nonfix_var_set*model.time, initialize = 15000)
model.fleet_transition_cost = pe.Param(model.nonfix_var_set*model.time, initialize = 5000)
model.vacation_penalty = pe.Param(model.pilots*model.quarterstart, initialize = 3000)
model.seniority_reward = pe.Param(model.pilots*model.time, initialize = 50)
#new constraints 1-5
#non-fixed only
#def trainer_rule(model,p,b,t):
# rhs = 0
# for f in model.fleet:
# for r in model.rank:
# rhs=rhs+model.Y[p,r,f,b,t]
# return model.T[p,b,t] <= rhs
#model.trainer_constraint = pe.Constraint(model.trainer_nonfix_pilots*model.base*model.time,rule=trainer_rule)
#include fixed
def trainer_rule(model,p,b,t):
rhs = 0
for f in model.fleet:
for r in model.rank:
if (p,r,f,b) in model.all_var_set:
rhs=rhs+model.Yall[p,r,f,b,t]
return model.T[p,b,t] <= rhs
model.trainer_constraint = pe.Constraint(model.trainer_pilots*model.base*model.time,rule=trainer_rule)
def trainee_rule(model,p,b,t):
rhs=0
for f in model.fleet:
for r in model.rank:
if (p,r,f,b) in model.nonfix_var_set:
rhs=rhs+model.Y[p,r,f,b,t]
return model.Trainee[p,b,t] <= rhs
model.trainee_constraint = pe.Constraint(model.fleet_pilots*model.base*model.time,rule=trainee_rule)
def vacation_rule1(model,p,b,t):
return model.V[p,t] <= 1- model.T[p,b,t]
model.vacation_constraint1 = pe.Constraint(model.trainer_pilots*model.base*model.time,rule=vacation_rule1)
def vacation_rule2(model,p,b,t):
return model.V[p,t] <= 1- model.Trainee[p,b,t]
model.vacation_constraint2 = pe.Constraint(model.fleet_pilots*model.base*model.time,rule=vacation_rule2)
#include fixed
def training_rule(model,p,r,f,b,t):
if (p,r,f,b) in model.all_var_set:
return model.Tposition[p,r,f,b,t] >= model.T[p,b,t] + model.Yall[p,r,f,b,t]-1
else:
return pe.Constraint.Skip
model.training_constraint = pe.Constraint(model.trainer_pilots*model.rank*model.fleet*model.base*model.time,rule = training_rule)
#non-fixed only
#def training_rule(model,p,r,f,b,t):
# return model.Tposition[p,r,f,b,t] >= model.T[p,b,t] + model.Y[p,r,f,b,t]-1
#model.training_constraint = pe.Constraint(model.trainer_nonfix_pilots*model.rank*model.fleet*model.base*model.time)
def trainee_rule2(model,p,r,f,b,t):
if (p,r,f,b) in model.all_var_set:
return model.Trainee_po[p,r,f,b,t] >= model.Trainee[p,b,t] +model.Y[p,r,f,b,t] -1
else:
return pe.Constraint.Skip
model.trainee_constraint2 = pe.Constraint(model.fleet_pilots*model.rank*model.fleet*model.base*model.time, rule = trainee_rule2)
def demand_rule(model,r,f,b,t):
vp=0
for p in model.nonfix_pilots :
if (p, r, f, b) in model.nonfix_var_set:
vp +=model.Vposition[p, r, f, b, t]
tp=0
for p in model.trainer_pilots :
if (p, r, f, b) in model.all_var_set:
vp +=model.Tposition[p, r, f, b, t]
traineep=0
for p in model.fleet_pilots :
if (p, r, f, b) in model.nonfix_var_set:
vp +=model.Trainee_po[p, r, f, b, t]
vfixp=0
for p in model.fix_pilots :
if (p, r, f, b) in model.fix_var_set:
vp +=model.Vfix_position[p, r, f, b, t]
curr_fixed = fixed_df[(fixed_df.Rank==r)&(fixed_df.Cur_Fleet==f)&(fixed_df.Current_Base==b)]['Crew_ID'].values
pilot = len(curr_fixed)
nonfix_pilot = 0
for p in model.nonfix_pilots :
if (p, r, f, b) in model.nonfix_var_set:
nonfix_pilot +=model.Y[p, r, f, b, t]
rhs = pilot + nonfix_pilot - vp - tp - vfixp - traineep + model.shortage[r,f,b,t] - model.surplus[r,f,b,t]
demand = get_demand(r,f,b,t)
return rhs == demand
model.demand_constraint = pe.Constraint(model.rank*model.fleet*model.base*model.time, rule = demand_rule)
'''
# demand rule
#TODO: need to figure out vacation, trainer cases.
def demand_rule(model,r,f,b,t):
curr_fixed = fixed_df[(fixed_df.Rank==r)&(fixed_df.Cur_Fleet==f)&(fixed_df.Current_Base==b)]['Crew_ID'].values
rhs = len(curr_fixed)
for p in curr_fixed:
if(p in model.trainer_pilots):
rhs -= model.T[p, b ,t]
for p in curr_fixed:
# if p is in vacation
rhs -= model.V[p,t]
for p in toPos[(toPos.RANK==r)&(toPos.FLEET==f)&(toPos.BASE==b)]['ID'].values:
rhs += model.Y[p,r,f,b,t]
# if p come to the position by a fleet change, it takes 5 weeks training
if(p in fleet_change and t >= 5) :
rhs = rhs - 1 + model.Y[p,r,f,b,t-5]
elif(p in fleet_change and t < 5) :
rhs = rhs - model.Y[p,r,f,b,t]
for p in fromPos[(fromPos.RANK==r)&(fromPos.FLEET==f)&(fromPos.BASE==b)]['ID'].values:
rhs = rhs - model.Y[p,r,f,b,t]
rhs = rhs + model.shortage[r,f,b,t] - model.surplus[r,f,b,t]
return rhs == get_demand(r,f,b,t)
model.Demand = pe.Constraint(model.rank*model.fleet*model.base*model.time, rule=demand_rule)
'''
# model.Demand.pprint()
#at time t, a pilot should occupy one and only one position
#checked
def pilot_pos_rule(model, p, t):
summ=0
for r in model.rank:
for f in model.fleet:
for b in model.base:
if (p,r,f,b) in model.nonfix_var_set:
summ += model.Y[p, r, f, b, t]
lhs = summ
return lhs == 1
model.PositionConst = pe.Constraint(model.nonfix_pilots*model.time, rule = pilot_pos_rule)
# model.PositionConst.pprint()
# all nonfix_pilots should start being at their "from" position
def pilot_transit_rule0(model, p, r, f, b):
return model.Y[p,r,f,b,0] == 1
model.Transition0 = pe.Constraint(model.from_pos, rule = pilot_transit_rule0)
# model.Transition0.pprint()
# all nonfix_pilots should transit only once--"from" postion should be decreasing
def pilot_transit_rule1(model, p, r, f, b, t):
return model.Y[p,r,f,b,t] - model.Y[p,r,f,b,t+1] >= 0
model.Transition1 = pe.Constraint(model.from_pos*model.timestart, rule = pilot_transit_rule1)
# model.Transition1.pprint()
# "to" postion should be increasing
def pilot_transit_rule2(model, p, r, f, b, t):
return model.Y[p,r,f,b,t] - model.Y[p,r,f,b,t+1] <= 0
model.Transition2 = pe.Constraint(model.to_pos*model.timestart, rule = pilot_transit_rule2)
# model.Transition2.pprint()
def get_slot(t):
return vacation_df["Available_Vacation_Slots"][t]
#vacation constraint. -vacation. pilot <= slot.
def max_vacation_slot_rule(model, t):
lhs = 0
for pilot in model.pilots :
lhs += model.V[pilot,t]
return lhs <= get_slot(t)
model.pilot_vacation_slot_exceed = pe.Constraint(model.time, rule = max_vacation_slot_rule)
### at least one vacation per quarter
def min_vacation_rule(model, p, t):
lhs = 0
for i in range(13):
lhs += model.V[p,t+i]
lhs += model.VP[p,t]
return lhs >= 1
model.Vacation = pe.Constraint(model.pilots*model.quarterstart, rule = min_vacation_rule)
### Seniority rule: get reward if we give vacation to more senior employee first
def seniority_rule(model,p,t):
lhs = 0
if (p in model.se_1):
lhs = model.V[p,t]*1
elif (p in model.se_2):
lhs = model.V[p, t] * 2
elif (p in model.se_3):
lhs = model.V[p, t] * 3
elif (p in model.se_4):
lhs = model.V[p, t] * 4
lhs -= model.VS[p, t]
return lhs == 0
model.seniority = pe.Constraint(model.pilots*model.time, rule = seniority_rule)
### if the pilot p is not at position [b,f,r]at week t, even if he is on vacation, then Vposition[p,b,f,r,t] = 0
def vacation_position_rule(model,p,r,f,b,t):
lhs = 0
lhs = model.V[p,t] + model.Y[p,r,f,b,t] - 1 - model.Vposition[p,r,f,b,t]
return lhs <= 0
model.Vacation_position = pe.Constraint(model.nonfix_var_set*model.time, rule = vacation_position_rule)
def vacation_position_rule2(model,p,r,f,b,t):
lhs = 0
lhs = model.V[p,t] + model.Yall[p,r,f,b,t] - 1 - model.Vfix_position[p,r,f,b,t]
return lhs <= 0
model.Vacation_position2 = pe.Constraint(model.fix_var_set*model.time, rule = vacation_position_rule2)
# def trainer_location_rule(model, p, r, f, b, t):
# if p in model.trainer_pilots:
# return model.T[p, b, t] <= model.Y[p,r,f,b,t]
# else:
# return pe.Constraint.Skip
# model.trainer_location = pe.Constraint(model.all_pos*model.time, rule=trainer_location_rule)
# def trainee_location_rule(model, p, r, f, b, t):
# if p in model.fleet_pilots:
# return model.Trainee[p, b, t] <= model.Y[p,r,f,b,t]
# else:
# return pe.Constraint.Skip
# model.trainee_location = pe.Constraint(model.all_pos*model.time, rule=trainee_location_rule)
def trainee_var_binding_rule(model, p, r, f, b, t):
if(p in fleet_change):
return model.Y[p,r,f,b,t] - model.Y[p,r,f,b,t+1] - model.Trainee[p, b, t] == 0
else:
return pe.Constraint.Skip
model.trainee_var_binding = pe.Constraint(model.from_pos*model.timestart, rule=trainee_var_binding_rule)
def trainee_trainer_rule(model, b, t):
total_trainer = 0
for p in model.trainer_pilots:
total_trainer += model.T[p, b, t]
total_trainee = 0
for p in fleet_change:
total_trainee += model.Trainee[p, b, t]
return total_trainer == total_trainee
model.trainee_trainer = pe.Constraint(model.base*model.time, rule = trainee_trainer_rule)
###Yall and Y binding rule (for non-fix pilot part)
def yall_y_binding_rule(model, p, r, f, b, t):
return model.Yall[p,r,f,b,t] == model.Y[p,r,f,b,t]
model.yall_y_binding = pe.Constraint(model.nonfix_var_set*model.time, rule = yall_y_binding_rule)
###Yall setting rule(for fix-pilot part)
def yall_setting_rule(model, p, r, f, b, t):
df_new = fixed_df.set_index(['Crew_ID','Rank','Cur_Fleet','Current_Base'])
if (p, r, f, b) in df_new.index:
return model.Yall[p,r,f,b,t] == 1
else:
return model.Yall[p,r,f,b,t] == 0
model.yall_setting = pe.Constraint(model.fix_var_set*model.time, rule = yall_setting_rule)
###OBJ###
###Normal Operation:
model.total_normal_cost = pe.summation(model.normal_cost, model.Y)
###Transitions:
model.total_fleet_trans_cost = pe.summation(model.fleet_transition_cost, model.Y, index = [(p, r, f, b, 25) for(p, r, f, b) in model.to_pos if p in model.fleet_pilots ])
model.total_base_trans_cost = pe.summation(model.base_transition_cost, model.Y, index = [(p, r, f, b, 25) for(p, r, f, b) in model.to_pos if p in model.base_pilots ])
model.total_trans_cost = model.total_fleet_trans_cost + model.total_base_trans_cost
###Shortages:
model.total_shortage_cost = pe.summation(model.short_cost, model.shortage)
###Vacation Penalty:
model.total_vacation_penalty = pe.summation(model.vacation_penalty, model.VP)
model.total_seniority_reward = pe.summation(model.seniority_reward, model.VS)
model.OBJ = pe.Objective(expr = model.total_shortage_cost + model.total_trans_cost + model.total_normal_cost + model.total_vacation_penalty - model.total_seniority_reward, sense=pe.minimize)
solver = pyomo.opt.SolverFactory('cplex')
results = solver.solve(model, tee=True, keepfiles=False)
if (results.solver.status != pyomo.opt.SolverStatus.ok):
logging.warning('Check solver not ok?')
if (results.solver.termination_condition != pyomo.opt.TerminationCondition.optimal):
logging.warning('Check solver optimality?')
model.solutions.load_from(results)
#model.load(results)
print "\nTotal number of non-fix pilots is " + str(len(model.nonfix_pilots))
for (p, r, f, b) in model.from_pos:
for t in model.timestart:
if model.Y[p, r, f, b, t].value != model.Y[p, r, f, b, t+1].value:
print "\nPilot " + str(p) + " changed at week " + str(t)
if str(p) in model.fleet_pilots :
print "This is a fleet change from " + str(f)
if str(p) in model.rank_pilots :
print "This is a rank change from " + str(r)
if str(p) in model.base_pilots :
print "This is a base change from " + str(b)
print "\nTotal number of TR3233_1 qualified trainers is " + str(len(model.trainer_pilots))
for p in model.trainer_pilots:
for t in model.timestart:
for b in model.base:
if model.T[p, b, t].value == 1 :
print "trainer " + p + " is training at week " + str(t) + " at base " + str(b)
print "\nTotal number of pilot applies for fleet change is " + str(len(model.fleet_pilots))
for p in model.fleet_pilots:
for t in model.timestart:
for b in model.base:
if model.Trainee[p, b, t].value == 1 :
print "pilot " + p + " receives fleet training at week " + str(t) + " at base " + str(b)
# record the transition in each week
for (p, r, f, b) in model.fix_var_set:
for t in model.time:
if(model.Vfix_position[p, r, f, b, t].value == 1):
print p +" "+str(t) + " Vacation"
if(p in model.trainer_pilots):
if(model.T[p,b,t].value == 1):
print p +" "+str(t) + " Giving Training"
for (p, r, f, b) in model.nonfix_var_set:
for t in model.time:
if(model.Vposition[p, r, f, b, t].value == 1):
print p +" "+str(t) + " Vacation"
if(p in model.trainer_pilots):
if(model.T[p,b,t].value == 1):
print p +" "+str(t) + " Giving Training"
if(p in model.fleet_pilots):
if(model.Trainee[p,b,t].value == 1):
print p +" "+str(t) + " Receive Training"
if((p in model.base_pilots) & (t in model.timestart) & ((p,r,f,b) in model.from_pos)):
if((model.Y[p, r, f, b, t].value == 1) & (model.Y[p, r, f, b, t+1].value == 0)):
print p +" "+str(t) + " Base change from " + str(b)
if((p in model.rank_pilots) & (t in model.timestart) & ((p,r,f,b) in model.from_pos)):
if((model.Y[p, r, f, b, t].value == 1) & (model.Y[p, r, f, b, t+1].value == 0)):
print p +" "+str(t) + " Rank change from " + str(r)
print '\nTotal cost = ', model.OBJ()
print 'Shortage cost is = ', model.total_shortage_cost()
print 'Transition cost is = ', model.total_trans_cost()
print 'Normal Operation cost is = ', model.total_normal_cost()
print 'Vacation Penalty is = ', model.total_vacation_penalty()
print 'Seniority Reward is =', model.total_seniority_reward()
#instance.solutions.load_from(results)
#model.solutions.load_from(results)