def solve(dat):
assert input_schema.good_tic_dat_object(dat)
mdl = gu.Model("diet", env=gurobi_env())
nutrition = {c:mdl.addVar(lb=n["Min Nutrition"], ub=n["Max Nutrition"], name=c)
for c,n in dat.categories.items()}
# Create decision variables for the foods to buy
buy = {f:mdl.addVar(name=f) for f in dat.foods}
# Nutrition constraints
for c in dat.categories:
mdl.addConstr(gu.quicksum(dat.nutrition_quantities[f,c]["Quantity"] * buy[f]
for f in dat.foods) == nutrition[c],
name = c)
mdl.setObjective(gu.quicksum(buy[f] * c["Cost"] for f,c in dat.foods.items()),
sense=gu.GRB.MINIMIZE)
mdl.optimize()
if mdl.status == gu.GRB.OPTIMAL:
sln = solution_schema.TicDat()
for f,x in buy.items():
if x.x > 0:
sln.buy_food[f] = x.x
for c,x in nutrition.items():
sln.consume_nutrition[c] = x.x
sln.parameters['Total Cost'] = sum(dat.foods[f]["Cost"] * r["Quantity"]
for f,r in sln.buy_food.items())
return sln
def solve(dat):
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
assert not input_schema.find_data_row_failures(dat)
if len(dat.roster) < len(dat.positions):
print(
"\n****\nModel is infeasible due to shortage of players!\n****\n")
mdl = gu.Model("simpler little_league", env=gurobi_env())
lineup = {(i, p, pl): mdl.addVar(vtype=gu.GRB.BINARY,
name="lineup_%s_%s_%s" % (i, p, pl))
for i, p, pl in product(dat.innings, dat.positions, dat.roster)}
l_slicer = Slicer(lineup)
# 1 player assigned to each position per inning
for i, p in product(dat.innings, dat.positions):
mdl.addConstr(gu.quicksum(lineup[k]
for k in l_slicer.slice(i, p, '*')) == 1,
name="all_positions_filled_per_inning_%s_%s" % (i, p))
# each player can play at most one position per inning
for i, pl in product(dat.innings, dat.roster):
mdl.addConstr(gu.quicksum(lineup[k]
for k in l_slicer.slice(i, '*', pl)) <= 1,
name="at_most_one_position_per_inning_%s_%s" % (i, pl))
grade_slice = Slicer([(pl, r["Grade"]) for pl, r in dat.roster.items()])
position_slice = Slicer([(p, r["Position Group"])
for p, r in dat.positions.items()])
innings_slice = Slicer([(i, r["Inning Group"])
for i, r in dat.innings.items()])
# Position_Constraints satisfied
for (pg, ig, g), r in dat.position_constraints.items():
total_players = gu.quicksum(
lineup[i[0], p[0], pl[0]] for i, p, pl in product(
innings_slice.slice('*', ig), position_slice.slice('*', pg),
grade_slice.slice('*', g)))
mdl.addConstr(total_players >= r["Min Players"],
name="min_players_%s_%s_%s" % (pg, ig, g))
mdl.addConstr(total_players <= r["Max Players"],
name="max_players_%s_%s_%s" % (pg, ig, g))
mdl.setObjective(
gu.quicksum(dat.positions[p]["Position Importance"] *
dat.player_ratings[pl, pg]["Rating"] * v
for (i, p, pl), v in lineup.items()
for pg in [dat.positions[p]["Position Group"]]
if (pl, pg) in dat.player_ratings),
sense=gu.GRB.MAXIMIZE)
mdl.optimize()
if mdl.status == gu.GRB.OPTIMAL:
sln = solution_schema.TicDat()
for (i, p, pl), v in lineup.items():
if abs(v.x - 1) < 0.0001:
sln.lineup[i, p] = pl
return sln
def solve(dat):
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
normal_price = {pdct:0 for pdct in dat.products}
for pdct, price in dat.forecast_sales:
normal_price[pdct] = max(normal_price[pdct], price)
def revenue(pdct, price):
return dat.forecast_sales[pdct, price]["Sales"] * price
def investment(pdct, price):
return max(0, dat.forecast_sales[pdct, price]["Sales"] * normal_price[pdct] -
revenue(pdct, normal_price[pdct]))
mdl = gu.Model("soda promotion", env=gurobi_env())
pdct_price = mdl.addVars(dat.forecast_sales, vtype=gu.GRB.BINARY,name='pdct_price')
mdl.addConstrs((pdct_price.sum(pdct,'*') == 1 for pdct in dat.products), name = "pick_one_price")
total_qty = mdl.addVar(name="total_qty")
total_revenue = mdl.addVar(name="total_revenue")
total_investment = mdl.addVar(name="total_investment", ub=dat.parameters["Maximum Total Investment"]["Value"]
if "Maximum Total Investment" in dat.parameters else float("inf"))
mdl.addConstr(total_qty == pdct_price.prod({_:dat.forecast_sales[_]["Sales"] for _ in pdct_price}))
mdl.addConstr(total_revenue == pdct_price.prod({_:revenue(*_) for _ in pdct_price}))
mdl.addConstr(total_investment == pdct_price.prod({_:investment(*_) for _ in pdct_price}))
pf_slice = Slicer((dat.products[pdct]["Family"], pdct, price) for pdct, price in pdct_price)
for pdct_family, r in dat.max_promotions.items():
mdl.addConstr(gu.quicksum(pdct_price[_pdct, _price]
for _pdct_family, _pdct, _price in pf_slice.slice(pdct_family, '*', '*')
if _price != normal_price[_pdct]) <= r["Max Promotions"],
name = "max_promotions_%s"%pdct_family)
mdl.setObjective(total_qty, sense=gu.GRB.MAXIMIZE)
mdl.optimize()
if mdl.status == gu.GRB.OPTIMAL:
sln = solution_schema.TicDat()
for (pdct, price), var in pdct_price.items():
if abs(var.X -1) < 0.0001: # i.e. almost one
sln.product_pricing[pdct] = [price, dat.products[pdct]["Family"],
"Normal Price" if price == normal_price[pdct] else "Discounted"]
sln.parameters["Total Quantity Sold"] = total_qty.X
sln.parameters["Total Revenue"] = total_revenue.X
sln.parameters["Total Investment"] = total_investment.X
number_meaningful_discounts = 0
for (pdct, price), r in dat.forecast_sales.items():
if (price < normal_price[pdct] and
r["Sales"] > dat.forecast_sales[pdct,normal_price[pdct]]["Sales"]):
number_meaningful_discounts += 1
sln.parameters["Number of Meaningful Discounts"] = number_meaningful_discounts
return sln
def solve(dat):
"""
core solving routine
:param dat: a good ticdat for the input_schema
:return: a good ticdat for the solution_schema, or None
"""
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
mdl = gu.Model("netflow", env=gurobi_env())
flow = {(h, i, j): mdl.addVar(name='flow_%s_%s_%s' % (h, i, j))
for h, i, j in dat.cost if (i, j) in dat.arcs}
flowslice = Slicer(flow)
# Arc Capacity constraints
for i, j in dat.arcs:
mdl.addConstr(
gu.quicksum(flow[_h, _i, _j] * dat.commodities[_h]["Volume"]
for _h, _i, _j in flowslice.slice('*', i, j)) <=
dat.arcs[i, j]["Capacity"],
name='cap_%s_%s' % (i, j))
# Flow conservation constraints. Constraints are generated only for relevant pairs.
# So we generate a conservation of flow constraint if there is negative or positive inflow
# quantity, or at least one inbound flow variable, or at least one outbound flow variable.
for h,j in set((h,i) for (h,i), v in dat.inflow.items() if abs(v["Quantity"]) > 0)\
.union({(h,i) for h,i,j in flow}, {(h,j) for h,i,j in flow}):
mdl.addConstr(gu.quicksum(flow[h_i_j]
for h_i_j in flowslice.slice(h, '*', j)) +
dat.inflow.get(
(h, j), {"Quantity": 0})["Quantity"] == gu.quicksum(
flow[h_j_i]
for h_j_i in flowslice.slice(h, j, '*')),
name='node_%s_%s' % (h, j))
mdl.setObjective(gu.quicksum(flow * dat.cost[h, i, j]["Cost"]
for (h, i, j), flow in flow.items()),
sense=gu.GRB.MINIMIZE)
mdl.optimize()
if mdl.status == gu.GRB.OPTIMAL:
rtn = solution_schema.TicDat()
for (h, i, j), var in flow.items():
if var.x > 0:
rtn.flow[h, i, j] = var.x
rtn.parameters["Total Cost"] = sum(
dat.cost[h, i, j]["Cost"] * r["Quantity"]
for (h, i, j), r in rtn.flow.items())
return rtn
def solve(dat):
"""
core solving routine
:param dat: a good ticdat for the input_schema
:return: a good ticdat for the solution_schema, or None
"""
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
m = gu.Model("assignment", env=gurobi_env())
# Assignment variables: x[w,s] == 1 if worker w is assigned to shift s.
x = m.addVars(dat.availability, vtype=gu.GRB.BINARY, name="x")
# Slack variables for each shift constraint
slacks = m.addVars(dat.shifts, name="Slack")
totSlack = m.addVar(name="totSlack")
# Variables to count the total shifts worked by each worker
totShifts = m.addVars(dat.workers, name="TotShifts")
# Constraint: assign exactly shiftRequirements[s] workers to each shift s,
# plus the slack
m.addConstrs((slacks[s] + x.sum('*', s) == dat.shifts[s]["Requirement"]
for s in dat.shifts), "_")
m.addConstr(totSlack == slacks.sum(), "totSlack")
m.addConstrs((totShifts[w] == x.sum(w) for w in dat.workers), "totShifts")
# Objective: minimize the total slack
m.setObjective(totSlack)
def _solve():
m.optimize()
if m.status in [gu.GRB.Status.INF_OR_UNBD, gu.GRB.Status.INFEASIBLE, gu.GRB.Status.UNBOUNDED]:
print('The model cannot be solved because it is infeasible or unbounded')
elif m.status != gu.GRB.Status.OPTIMAL:
print('Optimization was stopped with status %d' % m.status)
else:
return True
if _solve():
# Constrain the slack by setting its upper and lower bounds
totSlack.ub = totSlack.lb = totSlack.x
totalPayments = m.addVar(name="totalPayments")
m.addConstr(totalPayments == gu.quicksum(dat.workers[w]["Payment"]*x[w,s]
for w,s in dat.availability))
m.setObjective(totalPayments)
if _solve():
totalPayments.ub = totalPayments.lb = totalPayments.x
# Variable to count the average number of shifts worked
avgShifts = m.addVar(name="avgShifts")
# Variables to count the difference from average for each worker;
# note that these variables can take negative values.
diffShifts = m.addVars(dat.workers, lb=-gu.GRB.INFINITY, name="Diff")
# Constraint: compute the average number of shifts worked
m.addConstr(len(dat.workers) * avgShifts == totShifts.sum(), "avgShifts")
# Constraint: compute the difference from the average number of shifts
m.addConstrs((diffShifts[w] == totShifts[w] - avgShifts for w in dat.workers),
"Diff")
# Objective: minimize the sum of the square of the difference from the
# average number of shifts worked
m.setObjective(gu.quicksum(diffShifts[w]*diffShifts[w] for w in dat.workers))
if _solve():
sln = solution_schema.TicDat()
for (w,s),x_var in x.items():
if abs(x_var.x - 1) < 1e-5:
sln.assignments[w,s] = {}
for s,x_var in slacks.items():
if x_var.x > 0:
sln.slacks[s] = x_var.x
for w,x_var in totShifts.items():
if x_var.x > 0:
sln.total_shifts[w] = x_var.x
sln.parameters["Total Slack"] = totSlack.x
sln.parameters["Total Payments"] = totalPayments.x
sln.parameters["Variance of Total Shifts"] = float(m.objVal) / len(dat.workers)
return sln
def solve(dat):
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
assert not input_schema.find_data_row_failures(dat)
expected_draft_position = {}
# for our purposes, its fine to assume all those drafted by someone else are drafted
# prior to any players drafted by me
for player_name in sorted(
dat.players,
key=lambda _p: {
"Un-drafted": dat.players[_p]["Average Draft Position"],
"Drafted By Me": -1,
"Drafted By Someone Else": -2
}[dat.players[_p]["Draft Status"]]):
expected_draft_position[player_name] = len(expected_draft_position) + 1
assert max(expected_draft_position.values()) == len(
set(expected_draft_position.values())) == len(dat.players)
assert min(expected_draft_position.values()) == 1
already_drafted_by_me = {
player_name
for player_name, row in dat.players.items()
if row["Draft Status"] == "Drafted By Me"
}
can_be_drafted_by_me = {
player_name
for player_name, row in dat.players.items()
if row["Draft Status"] != "Drafted By Someone Else"
}
m = gu.Model('fantop', env=gurobi_env())
my_starters = {
player_name: m.addVar(vtype=gu.GRB.BINARY,
name="starter_%s" % player_name)
for player_name in can_be_drafted_by_me
}
my_reserves = {
player_name: m.addVar(vtype=gu.GRB.BINARY,
name="reserve_%s" % player_name)
for player_name in can_be_drafted_by_me
}
for player_name in can_be_drafted_by_me:
if player_name in already_drafted_by_me:
m.addConstr(my_starters[player_name] +
my_reserves[player_name] == 1,
name="already_drafted_%s" % player_name)
else:
m.addConstr(
my_starters[player_name] + my_reserves[player_name] <= 1,
name="cant_draft_twice_%s" % player_name)
for i, draft_position in enumerate(sorted(dat.my_draft_positions)):
m.addConstr(gu.quicksum(
my_starters[player_name] + my_reserves[player_name]
for player_name in can_be_drafted_by_me
if expected_draft_position[player_name] < draft_position) <= i,
name="at_most_%s_can_be_ahead_of_%s" % (i, draft_position))
my_draft_size = gu.quicksum(my_starters[player_name] +
my_reserves[player_name]
for player_name in can_be_drafted_by_me)
m.addConstr(my_draft_size >= len(already_drafted_by_me) + 1,
name="need_to_extend_by_at_least_one")
m.addConstr(my_draft_size <= len(dat.my_draft_positions),
name="cant_exceed_draft_total")
for position, row in dat.roster_requirements.items():
players = {
player_name
for player_name in can_be_drafted_by_me
if dat.players[player_name]["Position"] == position
}
starters = gu.quicksum(my_starters[player_name]
for player_name in players)
reserves = gu.quicksum(my_reserves[player_name]
for player_name in players)
m.addConstr(starters >= row["Min Num Starters"],
name="min_starters_%s" % position)
m.addConstr(starters <= row["Max Num Starters"],
name="max_starters_%s" % position)
m.addConstr(reserves >= row["Min Num Reserve"],
name="min_reserve_%s" % position)
m.addConstr(reserves <= row["Max Num Reserve"],
name="max_reserve_%s" % position)
if "Maximum Number of Flex Starters" in dat.parameters:
players = {
player_name
for player_name in can_be_drafted_by_me
if dat.roster_requirements[dat.players[player_name]["Position"]]
["Flex Status"] == "Flex Eligible"
}
m.addConstr(gu.quicksum(my_starters[player_name]
for player_name in players) <=
dat.parameters["Maximum Number of Flex Starters"]["Value"],
name="max_flex")
starter_weight = dat.parameters["Starter Weight"][
"Value"] if "Starter Weight" in dat.parameters else 1
reserve_weight = dat.parameters["Reserve Weight"][
"Value"] if "Reserve Weight" in dat.parameters else 1
m.setObjective(gu.quicksum(dat.players[player_name]["Expected Points"] *
(my_starters[player_name] * starter_weight +
my_reserves[player_name] * reserve_weight)
for player_name in can_be_drafted_by_me),
sense=gu.GRB.MAXIMIZE)
m.optimize()
if m.status != gu.GRB.OPTIMAL:
print("No draft at all is possible!")
return
sln = solution_schema.TicDat()
def almostone(x):
return abs(x.x - 1) < 0.0001
picked = sorted([
player_name for player_name in can_be_drafted_by_me
if almostone(my_starters[player_name])
or almostone(my_reserves[player_name])
],
key=lambda _p: expected_draft_position[_p])
assert len(picked) <= len(dat.my_draft_positions)
if len(picked) < len(dat.my_draft_positions):
print(
"Your model is over-constrained, and thus only a partial draft was possible"
)
draft_yield = 0
for player_name, draft_position in zip(picked,
sorted(dat.my_draft_positions)):
draft_yield += dat.players[player_name]["Expected Points"] * \
(starter_weight if almostone(my_starters[player_name]) else reserve_weight)
assert draft_position <= expected_draft_position[player_name]
sln.my_draft[player_name]["Draft Position"] = draft_position
sln.my_draft[player_name]["Position"] = dat.players[player_name][
"Position"]
sln.my_draft[player_name][
"Planned Or Actual"] = "Actual" if player_name in already_drafted_by_me else "Planned"
sln.my_draft[player_name]["Starter Or Reserve"] = \
"Starter" if almostone(my_starters[player_name]) else "Reserve"
sln.parameters["Total Yield"] = draft_yield
sln.parameters["Draft Performed"] = "Complete" if len(sln.my_draft) == len(dat.my_draft_positions) \
else "Partial"
return sln
def solve(dat, out, err, progress):
assert isinstance(progress, Progress)
assert isinstance(out, LogFile) and isinstance(err, LogFile)
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
out.write("COG output log\n%s\n\n" % time_stamp())
err.write("COG error log\n%s\n\n" % time_stamp())
def get_distance(x, y):
if (x, y) in dat.distance:
return dat.distance[x, y]["Distance"]
if (y, x) in dat.distance:
return dat.distance[y, x]["Distance"]
return float("inf")
def can_assign(x, y):
return dat.sites[y]["Center Status"] == "Can Be Center" \
and get_distance(x,y)<float("inf")
unassignables = [
n for n in dat.sites
if not any(can_assign(n, y)
for y in dat.sites) and dat.sites[n]["Demand"] > 0
]
if unassignables:
# Infeasibility detected. Generate an error table and return None
err.write("The following sites have demand, but can't be " +
"assigned to anything.\n")
err.log_table("Un-assignable Demand Points",
[["Site"]] + [[_] for _ in unassignables])
return
useless = [
n for n in dat.sites
if not any(can_assign(y, n)
for y in dat.sites) and dat.sites[n]["Demand"] == 0
]
if useless:
# Log in the error table as a warning, but can still try optimization.
err.write(
"The following sites have no demand, and can't serve as the " +
"center point for any assignments.\n")
err.log_table("Useless Sites", [["Site"]] + [[_] for _ in useless])
progress.numerical_progress("Feasibility Analysis", 100)
m = gu.Model("cog", env=gurobi_env())
assign_vars = {
(n, assigned_to):
m.addVar(vtype=gu.GRB.BINARY,
name="%s_%s" % (n, assigned_to),
obj=get_distance(n, assigned_to) * dat.sites[n]["Demand"])
for n in dat.sites for assigned_to in dat.sites
if can_assign(n, assigned_to)
}
open_vars = {
n: m.addVar(vtype=gu.GRB.BINARY, name="open_%s" % n)
for n in dat.sites if dat.sites[n]["Center Status"] == "Can Be Center"
}
if not open_vars:
err.write("Nothing can be a center!\n") # Infeasibility detected.
return
m.update()
progress.numerical_progress("Core Model Creation", 50)
# using ticdat.Slicer instead of tuplelist simply as a matter of taste/vanity
assign_slicer = Slicer(assign_vars)
for n, r in dat.sites.items():
if r["Demand"] > 0:
m.addConstr(gu.quicksum(
assign_vars[n, assign_to]
for _, assign_to in assign_slicer.slice(n, "*")) == 1,
name="must_assign_%s" % n)
crippledfordemo = "Formulation" in dat.parameters and \
dat.parameters["Formulation"]["Value"] == "Weak"
for assigned_to, r in dat.sites.items():
if r["Center Status"] == "Can Be Center":
_assign_vars = [
assign_vars[n, assigned_to]
for n, _ in assign_slicer.slice("*", assigned_to)
]
if crippledfordemo:
m.addConstr(gu.quicksum(_assign_vars) <=
len(_assign_vars) * open_vars[assigned_to],
name="weak_force_open%s" % assigned_to)
else:
for var in _assign_vars:
m.addConstr(var <= open_vars[assigned_to],
name="strong_force_open_%s" % assigned_to)
number_of_centroids = dat.parameters["Number of Centroids"]["Value"] \
if "Number of Centroids" in dat.parameters else 1
if number_of_centroids <= 0:
err.write("Need to specify a positive number of centroids\n"
) # Infeasibility detected.
return
m.addConstr(gu.quicksum(
v for v in open_vars.values()) == number_of_centroids,
name="numCentroids")
if "MIP Gap" in dat.parameters:
m.Params.MIPGap = dat.parameters["MIP Gap"]["Value"]
m.update()
progress.numerical_progress("Core Model Creation", 100)
m.optimize(progress.gurobi_call_back_factory("COG Optimization", m))
progress.numerical_progress("Core Optimization", 100)
if not hasattr(m, "status"):
print "missing status - likely premature termination"
return
for failStr, grbkey in (("inf_or_unbd", gu.GRB.INF_OR_UNBD),
("infeasible", gu.GRB.INFEASIBLE),
("unbounded", gu.GRB.UNBOUNDED)):
if m.status == grbkey:
print "Optimization failed due to model status of %s" % failStr
return
if m.status == gu.GRB.INTERRUPTED:
err.write("Solve process interrupted by user feedback\n")
if not all(hasattr(var, "x") for var in open_vars.values()):
err.write("No solution was found\n")
return
elif m.status != gu.GRB.OPTIMAL:
err.write("unexpected status %s\n" % m.status)
return
sln = solution_schema.TicDat()
sln.parameters["Lower Bound"] = getattr(m, "objBound", m.objVal)
sln.parameters["Upper Bound"] = m.objVal
out.write('Upper Bound: %g\n' % sln.parameters["Upper Bound"]["Value"])
out.write('Lower Bound: %g\n' % sln.parameters["Lower Bound"]["Value"])
def almostone(x):
return abs(x - 1) < 0.0001
for (n, assigned_to), var in assign_vars.items():
if almostone(var.x):
sln.assignments[n, assigned_to] = {}
for n, var in open_vars.items():
if almostone(var.x):
sln.openings[n] = {}
out.write('Number Centroids: %s\n' % len(sln.openings))
progress.numerical_progress("Full Cog Solve", 100)
return sln
def solve(dat):
assert input_schema.good_tic_dat_object(dat)
assert not input_schema.find_foreign_key_failures(dat)
assert not input_schema.find_data_type_failures(dat)
assert not input_schema.find_data_row_failures(dat)
# use default parameters, unless they are overridden by user-supplied parameters
full_parameters = dict(
default_parameters,
**{k: v["Value"]
for k, v in dat.parameters.items()})
bench = full_parameters["Bench Roster Label"]
verify(bench in dat.positions,
"%s needs to be one of the positions" % bench)
verify(
len(dat.roster) >= len(dat.positions) - 1,
"Model is infeasible due to shortage of players!")
mdl = gu.Model("little_league", env=gurobi_env())
lineup = {(i, p, pl): mdl.addVar(vtype=gu.GRB.BINARY,
name="lineup_%s_%s_%s" % (i, p, pl))
for i, p, pl in product(dat.innings, dat.positions, dat.roster)}
l_slicer = Slicer(lineup)
# 1 player assigned to each active position per inning
for i, p in product(dat.innings, dat.positions):
if p != bench:
mdl.addConstr(
gu.quicksum(lineup[k] for k in l_slicer.slice(i, p, '*')) == 1,
name="all_positions_filled_per_inning_%s_%s" % (i, p))
# each player must be assigned to one position (including the bench) per inning
for i, pl in product(dat.innings, dat.roster):
mdl.addConstr(gu.quicksum(lineup[k]
for k in l_slicer.slice(i, '*', pl)) == 1,
name="at_most_one_position_per_inning_%s_%s" % (i, pl))
grade_slice = Slicer([(pl, r["Grade"]) for pl, r in dat.roster.items()])
position_slice = Slicer([(p, r["Position Group"])
for p, r in dat.positions.items()])
innings_slice = Slicer([(i, r["Inning Group"])
for i, r in dat.innings.items()])
# Position_Constraints satisfied
for (pg, ig, g), r in dat.position_constraints.items():
total_players = gu.quicksum(
lineup[i[0], p[0], pl[0]] for i, p, pl in product(
innings_slice.slice('*', ig), position_slice.slice('*', pg),
grade_slice.slice('*', g)))
mdl.addConstr(total_players >= r["Min Players"],
name="min_players_%s_%s_%s" % (pg, ig, g))
mdl.addConstr(total_players <= r["Max Players"],
name="max_players_%s_%s_%s" % (pg, ig, g))
# Enforce consecutive innings constraints
sorted_innings = list(sorted(dat.innings))
for p, r in dat.positions.items():
if r["Consecutive Innings Only"] == "True":
for pl in dat.roster:
for pos1, pos2 in combinations(range(len(sorted_innings)), 2):
if pos2 < pos1:
pos1, pos2 = pos2, pos1
if pos2 - pos1 > 1:
pass
# need Derek's determination of what it means
# Balanced Playing time = a min playing time for each player
if full_parameters["Balanced Playing Time"] == "True":
for pl, r in dat.roster.items():
mdl.addConstr(gu.quicksum(
lineup[k] for k in l_slicer.slice('*', '*', pl)) >= floor(
(r["Departure Inning"] - r["Arrival Inning"] + 1) /
float(len(dat.positions) * len(dat.innings))),
name="balanced_pt_%s" % pl)
if full_parameters["Limit Outfield Play"] == "True":
of = full_parameters["Outfield Group Label"]
for pl in dat.roster:
mdl.addConstr(
gu.quicksum(lineup[i, p[0], pl]
for p in position_slice.slice('*', of)
for i in dat.innings) <=
gu.quicksum(lineup[i, p, pl]
for i, p in product(dat.innings, dat.positions)) *
0.5 + 0.5,
name="limit_OF_play_%s" % pl)
max_bench = full_parameters["Max Consecutive Bench"]
for pos, i in enumerate(sorted_innings):
if max_bench + 1 + pos <= len(sorted_innings):
for pl in dat.roster:
mdl.addConstr(gu.quicksum(
lineup[k] for i_ in sorted_innings[pos:pos + max_bench + 1]
for k in l_slicer.slice(i_, '*', pl)) >= 1,
name="max_consecutive_bench_%s_%s" % (i, pl))
mdl.setObjective(
gu.quicksum(dat.positions[p]["Position Importance"] *
dat.player_ratings[pl, pg]["Rating"] * v
for (i, p, pl), v in lineup.items()
for pg in [dat.positions[p]["Position Group"]]
if (pl, pg) in dat.player_ratings),
sense=gu.GRB.MAXIMIZE)
mdl.optimize()
if mdl.status == gu.GRB.OPTIMAL:
sln = solution_schema.TicDat()
for (i, p, pl), v in lineup.items():
if abs(v.x - 1) < 0.0001:
sln.lineup[i, p] = pl
return sln
