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master.py
167 lines (148 loc) · 7.03 KB
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master.py
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from gurobipy import *
import gurobipy as gu
class MasterProblem:
def __init__(self, df, Demand, max_iteration, current_iteration, last, nr, start, timeLim):
self.iteration = current_iteration
self.max_iteration = max_iteration
self.nurses = df['I'].dropna().astype(int).unique().tolist()
self.days = df['T'].dropna().astype(int).unique().tolist()
self.shifts = df['K'].dropna().astype(int).unique().tolist()
self._current_iteration = current_iteration
self.roster = [i for i in range(1, self.max_iteration + 2)]
self.rosterinitial = [i for i in range(1, 2)]
self.demand = Demand
self.model = gu.Model("MasterProblem")
self.cons_demand = {}
self.newvar = {}
self.last_itr = last
self.max_itr = max_iteration
self.cons_lmbda = {}
self.output_len = nr
self.start = start
self.timeLim = timeLim
def buildModel(self):
self.generateVariables()
self.generateConstraints()
self.model.update()
self.generateObjective()
self.model.update()
def generateVariables(self):
self.slack = self.model.addVars(self.days, self.shifts, vtype=gu.GRB.CONTINUOUS, lb=0, name='slack')
self.motivation_i = self.model.addVars(self.nurses, self.days, self.shifts, self.roster,
vtype=gu.GRB.CONTINUOUS, lb=0, ub=1, name='motivation_i')
self.lmbda = self.model.addVars(self.nurses, self.roster, vtype=gu.GRB.BINARY, lb=0, name='lmbda')
self.model.update()
def generateConstraints(self):
for i in self.nurses:
self.cons_lmbda[i] = self.model.addLConstr(1 == gu.quicksum(self.lmbda[i, r] for r in self.rosterinitial), name = "lmb("+str(i)+")")
for t in self.days:
for s in self.shifts:
self.cons_demand[t, s] = self.model.addConstr(
gu.quicksum(self.motivation_i[i, t, s, r]*self.lmbda[i, r] for i in self.nurses for r in self.rosterinitial) +
self.slack[t, s] >= self.demand[t, s], "demand("+str(t)+","+str(s)+")")
self.model.update()
return self.cons_lmbda, self.cons_demand
def generateObjective(self):
self.model.setObjective(gu.quicksum(self.slack[t, s] for t in self.days for s in self.shifts),
sense=gu.GRB.MINIMIZE)
def solveRelaxModel(self):
try:
self.model.Params.OutputFlag = 0
self.model.Params.QCPDual = 1
self.model.Params.TimeLimit = self.timeLim
self.model.Params.Method = 2
self.model.Params.LogToConsole = 0
self.model.Params.Crossover = 0
for v in self.model.getVars():
v.setAttr('vtype', 'C')
self.model.optimize()
except gu.GurobiError as e:
print('Error code ' + str(e.errno) + ': ' + str(e))
def getDuals_i(self):
Pi_cons_lmbda = self.model.getAttr("Pi", self.cons_lmbda)
return Pi_cons_lmbda
def getDuals_ts(self):
Pi_cons_demand = self.model.getAttr("QCPi", self.cons_demand)
return Pi_cons_demand
def updateModel(self):
self.model.update()
def setStartSolution(self):
for i in self.nurses:
for t in self.days:
for s in self.shifts:
if (i, t, s) in self.start:
self.model.addLConstr(self.motivation_i[i, t, s, 1] == self.start[i, t, s])
def solveModel(self):
try:
self.model.Params.QCPDual = 1
self.model.Params.TimeLimit = self.timeLim
self.model.Params.IntegralityFocus = 1
self.model.Params.FeasibilityTol = 1e-9
self.model.Params.BarConvTol = 0.0
self.model.Params.MIPGap = 1e-4
self.model.Params.OutputFlag = 0
self.model.Params.LogToConsole = 0
self.model.optimize()
except gu.GurobiError as e:
print('Error code ' + str(e.errno) + ': ' + str(e))
def File2Log(self):
self.model.Params.LogToConsole = 1
self.model.Params.LogFile = "./log_file_cg.log"
def addColumn(self, index, itr, schedule):
self.nurseIndex = index
self.rosterIndex = itr + 1
for t in self.days:
for s in self.shifts:
qexpr = self.model.getQCRow(self.cons_demand[t, s])
qexpr.add(schedule[self.nurseIndex, t, s, self.rosterIndex] * self.lmbda[self.nurseIndex, self.rosterIndex], 1)
rhs = self.cons_demand[t, s].getAttr('QCRHS')
sense = self.cons_demand[t, s].getAttr('QCSense')
name = self.cons_demand[t, s].getAttr('QCName')
newcon = self.model.addQConstr(qexpr, sense, rhs, name)
self.model.remove(self.cons_demand[t, s])
self.cons_demand[t, s] = newcon
self.model.update()
def addLambda(self, index, itr):
self.nurseIndex = index
self.rosterIndex = itr + 1
self.newlmbcoef = 1.0
current_lmb_cons = self.cons_lmbda[self.nurseIndex]
expr = self.model.getRow(current_lmb_cons)
new_lmbcoef = self.newlmbcoef
expr.add(self.lmbda[self.nurseIndex, self.rosterIndex], new_lmbcoef)
rhs_lmb = current_lmb_cons.getAttr('RHS')
sense_lmb = current_lmb_cons.getAttr('Sense')
name_lmb = current_lmb_cons.getAttr('ConstrName')
newconlmb = self.model.addLConstr(expr, sense_lmb, rhs_lmb, name_lmb)
self.model.remove(current_lmb_cons)
self.cons_lmbda[self.nurseIndex] = newconlmb
def checkForQuadraticCons(self):
self.qconstrs = self.model.getQConstrs()
print("*{:^{output_len}}*".format(f"Check for quadratic constraints {self.qconstrs}", output_len=self.output_len))
def finalObj(self):
obj = self.model.objval
return obj
def printLambdas(self):
return self.model.getAttr("X", self.lmbda)
def finalSolve(self):
try:
self.model.Params.TimeLimit = self.timeLim
self.model.Params.IntegralityFocus = 1
self.model.Params.FeasibilityTol = 1e-9
self.model.Params.BarConvTol = 0.0
self.model.Params.MIPGap = 1e-4
self.model.Params.OutputFlag = 0
self.model.Params.LogToConsole = 0
self.model.setAttr("vType", self.lmbda, gu.GRB.BINARY)
self.model.update()
self.model.optimize()
if self.model.status == gu.GRB.OPTIMAL:
print("*" * (self.output_len + 2))
print("*{:^{output_len}}*".format("***** Optimal solution found *****", output_len=self.output_len))
print("*" * (self.output_len + 2))
else:
print("*" * (self.output_len + 2))
print("*{:^{output_len}}*".format("***** No optimal solution found *****", output_len=self.output_len))
print("*" * (self.output_len + 2))
except gu.GurobiError as e:
print('Error code ' + str(e.errno) + ': ' + str(e))