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### workforce4.py

#!/usr/bin/python

# Copyright 2016, Gurobi Optimization, Inc.

# Assign workers to shifts; each worker may or may not be available on a
# particular day. We use lexicographic optimization to solve the model:
# first, we minimize the linear sum of the slacks. Then, we constrain
# the sum of the slacks, and we minimize a quadratic objective that
# tries to balance the workload among the workers.

from gurobipy import *

# Number of workers required for each shift
shifts, shiftRequirements = multidict({
"Mon1":  3,
"Tue2":  2,
"Wed3":  4,
"Thu4":  4,
"Fri5":  5,
"Sat6":  6,
"Sun7":  5,
"Mon8":  2,
"Tue9":  2,
"Wed10": 3,
"Thu11": 4,
"Fri12": 6,
"Sat13": 7,
"Sun14": 5 })

# Amount each worker is paid to work one shift
workers, pay = multidict({
"Amy":   10,
"Bob":   12,
"Cathy": 10,
"Dan":   8,
"Ed":    8,
"Fred":  9,
"Gu":    11 })

# Worker availability
availability = tuplelist([
('Amy', 'Tue2'), ('Amy', 'Wed3'), ('Amy', 'Fri5'), ('Amy', 'Sun7'),
('Amy', 'Tue9'), ('Amy', 'Wed10'), ('Amy', 'Thu11'), ('Amy', 'Fri12'),
('Amy', 'Sat13'), ('Amy', 'Sun14'), ('Bob', 'Mon1'), ('Bob', 'Tue2'),
('Bob', 'Fri5'), ('Bob', 'Sat6'), ('Bob', 'Mon8'), ('Bob', 'Thu11'),
('Bob', 'Sat13'), ('Cathy', 'Wed3'), ('Cathy', 'Thu4'), ('Cathy', 'Fri5'),
('Cathy', 'Sun7'), ('Cathy', 'Mon8'), ('Cathy', 'Tue9'), ('Cathy', 'Wed10'),
('Cathy', 'Thu11'), ('Cathy', 'Fri12'), ('Cathy', 'Sat13'),
('Cathy', 'Sun14'), ('Dan', 'Tue2'), ('Dan', 'Wed3'), ('Dan', 'Fri5'),
('Dan', 'Sat6'), ('Dan', 'Mon8'), ('Dan', 'Tue9'), ('Dan', 'Wed10'),
('Dan', 'Thu11'), ('Dan', 'Fri12'), ('Dan', 'Sat13'), ('Dan', 'Sun14'),
('Ed', 'Mon1'), ('Ed', 'Tue2'), ('Ed', 'Wed3'), ('Ed', 'Thu4'),
('Ed', 'Fri5'), ('Ed', 'Sun7'), ('Ed', 'Mon8'), ('Ed', 'Tue9'),
('Ed', 'Thu11'), ('Ed', 'Sat13'), ('Ed', 'Sun14'), ('Fred', 'Mon1'),
('Fred', 'Tue2'), ('Fred', 'Wed3'), ('Fred', 'Sat6'), ('Fred', 'Mon8'),
('Fred', 'Tue9'), ('Fred', 'Fri12'), ('Fred', 'Sat13'), ('Fred', 'Sun14'),
('Gu', 'Mon1'), ('Gu', 'Tue2'), ('Gu', 'Wed3'), ('Gu', 'Fri5'),
('Gu', 'Sat6'), ('Gu', 'Sun7'), ('Gu', 'Mon8'), ('Gu', 'Tue9'),
('Gu', 'Wed10'), ('Gu', 'Thu11'), ('Gu', 'Fri12'), ('Gu', 'Sat13'),
('Gu', 'Sun14')
])

# Model
m = Model("assignment")

# Assignment variables: x[w,s] == 1 if worker w is assigned to shift s.
# This is no longer a pure assignment model, so we must use binary variables.
x = {}
for w,s in availability:

# Slack variables for each shift constraint so that the shifts can
# be satisfied
slacks = {}
for s in shifts:

# Variable to represent the total slack

# Variables to count the total shifts worked by each worker
totShifts = {}
for w in workers:

# Update model to integrate new variables
m.update()

# Constraint: assign exactly shiftRequirements[s] workers to each shift s,
# plus the slack
for s in shifts:
quicksum(x[w,s] for w,s in availability.select('*', s)) ==
shiftRequirements[s], s)

# Constraint: set totSlack equal to the total slack
m.addConstr(totSlack == quicksum(slacks[s] for s in shifts), "totSlack")

# Constraint: compute the total number of shifts for each worker
for w in workers:
quicksum(x[w,s] for w,s in availability.select(w, '*')),
"totShifts" + w)

# Objective: minimize the total slack
# Note that this replaces the previous 'pay' objective coefficients
m.setObjective(totSlack)

# Optimize
def solveAndPrint():
m.optimize()
status = m.status
if status == GRB.Status.INF_OR_UNBD or status == GRB.Status.INFEASIBLE \
or status == GRB.Status.UNBOUNDED:
print('The model cannot be solved because it is infeasible or \
unbounded')
exit(1)

if status != GRB.Status.OPTIMAL:
print('Optimization was stopped with status %d' % status)
exit(0)

# Print total slack and the number of shifts worked for each worker
print('')
print('Total slack required: %g' % totSlack.x)
for w in workers:
print('%s worked %g shifts' % (w, totShifts[w].x))
print('')

solveAndPrint()

# Constrain the slack by setting its upper and lower bounds
totSlack.ub = totSlack.x
totSlack.lb = totSlack.x

# Variable to count the average number of shifts worked

# Variables to count the difference from average for each worker;
# note that these variables can take negative values.
diffShifts = {}
for w in workers:
diffShifts[w] = \

# Update model to integrate new variables
m.update()

# Constraint: compute the average number of shifts worked
quicksum(totShifts[w] for w in workers),
"avgShifts")

# Constraint: compute the difference from the average number of shifts
for w in workers:
m.addConstr(diffShifts[w] == totShifts[w] - avgShifts, w + "Diff")

# Objective: minimize the sum of the square of the difference from the
# average number of shifts worked
m.setObjective(quicksum(diffShifts[w]*diffShifts[w] for w in workers))

# Optimize
solveAndPrint()


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