# tries to balance the workload among the workers.
#
from ticdat import PanDatFactory, standard_main
try: # if you don't have amplpy installed, the code will still load and then fail on solve
from amplpy import AMPL
except:
AMPL = None
# ------------------------ define the input schema --------------------------------
input_schema = PanDatFactory(
workers=[["Name"], ["Payment"]],
shifts=[["Name"], ["Requirement"]],
availability=[["Worker", "Shift"], []]
)
# Define the foreign key relationships
input_schema.add_foreign_key("availability", "workers", ['Worker', 'Name'])
input_schema.add_foreign_key("availability", "shifts", ['Shift', 'Name'])
# Define the data types
input_schema.set_data_type("workers", "Payment", min=0, max=float("inf"),
inclusive_min=True, inclusive_max=True)
input_schema.set_data_type("shifts", "Requirement", min=0, max=float("inf"),
inclusive_min=True, inclusive_max=True)
# ---------------------------------------------------------------------------------
# ------------------------ define the output schema -------------------------------
solution_schema = PanDatFactory(
assignments=[["Worker", "Shift"], []],
slacks = [["Shift"], ["Slack"]],
total_shifts=[["Worker"], ["Total Number Of Shifts"]],
parameters=[["Parameter"], ["Value"]]
import gurobipy as gp
except:
gp = None
from ticdat import PanDatFactory, Slicer
# ------------------------ define the input schema --------------------------------
input_schema = PanDatFactory(commodities=[["Name"], ["Volume"]],
nodes=[["Name"], []],
arcs=[["Source", "Destination"], ["Capacity"]],
cost=[["Commodity", "Source", "Destination"],
["Cost"]],
supply=[["Commodity", "Node"], ["Quantity"]],
demand=[["Commodity", "Node"], ["Quantity"]])
# Define the foreign key relationships
input_schema.add_foreign_key("arcs", "nodes", ['Source', 'Name'])
input_schema.add_foreign_key("arcs", "nodes", ['Destination', 'Name'])
input_schema.add_foreign_key(
"cost", "arcs", [['Source', 'Source'], ['Destination', 'Destination']])
input_schema.add_foreign_key("cost", "commodities", ['Commodity', 'Name'])
input_schema.add_foreign_key("demand", "commodities", ['Commodity', 'Name'])
input_schema.add_foreign_key("demand", "nodes", ['Node', 'Name'])
input_schema.add_foreign_key("supply", "commodities", ['Commodity', 'Name'])
input_schema.add_foreign_key("supply", "nodes", ['Node', 'Name'])
# Define the data types
input_schema.set_data_type("commodities",
"Volume",
min=0,
max=float("inf"),
inclusive_min=False,
from ticdat import PanDatFactory, standard_main
try: # if you don't have amplpy installed, the code will still load and then fail on solve
from amplpy import AMPL
except:
AMPL = None
# ------------------------ define the input schema --------------------------------
input_schema = PanDatFactory(commodities=[["Name"], ["Volume"]],
nodes=[["Name"], []],
arcs=[["Source", "Destination"], ["Capacity"]],
cost=[["Commodity", "Source", "Destination"],
["Cost"]],
inflow=[["Commodity", "Node"], ["Quantity"]])
# Define the foreign key relationships
input_schema.add_foreign_key("arcs", "nodes", ['Source', 'Name'])
input_schema.add_foreign_key("arcs", "nodes", ['Destination', 'Name'])
input_schema.add_foreign_key("cost", "nodes", ['Source', 'Name'])
input_schema.add_foreign_key("cost", "nodes", ['Destination', 'Name'])
input_schema.add_foreign_key("cost", "commodities", ['Commodity', 'Name'])
input_schema.add_foreign_key("inflow", "commodities", ['Commodity', 'Name'])
input_schema.add_foreign_key("inflow", "nodes", ['Node', 'Name'])
# Define the data types
input_schema.set_data_type("commodities",
"Volume",
min=0,
max=float("inf"),
inclusive_min=False,
inclusive_max=False)
input_schema.set_data_type("arcs",
max=float("inf"),
inclusive_min=True,
inclusive_max=False)
input_schema.set_data_type("warehouse_to_customer_costs",
"Cost",
min=0,
max=float("inf"),
inclusive_min=True,
inclusive_max=False)
input_schema.set_data_type("warehouse_to_customer_distances",
"Distance",
min=0,
max=float("inf"),
inclusive_min=True,
inclusive_max=False)
input_schema.add_foreign_key("demand", "customers", ["Customer", "Name"])
input_schema.add_foreign_key("demand", "products", ["Product", "Name"])
input_schema.add_foreign_key("supply", "plants", ["Plant", "Name"])
input_schema.add_foreign_key("supply", "products", ["Product", "Name"])
input_schema.add_foreign_key("plant_to_warehouse_costs", "plants",
["Plant", "Name"])
input_schema.add_foreign_key("plant_to_warehouse_costs", "warehouses",
["Warehouse", "Name"])
input_schema.add_foreign_key("plant_to_warehouse_costs", "products",
["Product", "Name"])
input_schema.add_foreign_key("warehouse_to_customer_distances", "customers",
["Customer", "Name"])
input_schema.add_foreign_key("warehouse_to_customer_distances", "warehouses",
["Warehouse", "Name"])
input_schema.add_foreign_key("warehouse_to_customer_costs", "products",
["Product", "Name"])
from ticdat import PanDatFactory
input_schema = PanDatFactory(cities=[["Name"],["Demand"]],
distances=[["Source", "Destination"], ["Distance"]],
parameters=[["Parameter"], ["Value"]])
input_schema.add_parameter("Number of Centroids", default_value=4, inclusive_min=False, inclusive_max=False, min=0,
max=float("inf"), must_be_int=True)
input_schema.set_data_type("cities", "Demand", min=0, max=float("inf"), inclusive_min=True, inclusive_max=False)
input_schema.set_data_type("distances", "Distance", min=0, max=float("inf"), inclusive_min=True, inclusive_max=False)
input_schema.add_foreign_key("distances", "cities", ['Source', 'Name'])
input_schema.add_foreign_key("distances", "cities", ['Destination', 'Name'])
# The distance matrix is bi-directionally safe. I.e. if the same source/dest and dest/source exist then the
# distances must match. If only one is present, it can fall back to the other in the code.
def _distance_matrix(dat):
return {"distance_matrix": {tuple(row[:2]): row[2] for row in dat.distances.itertuples(index=False)}}
input_schema.add_data_row_predicate("distances", predicate_name="Check Bi-Directionally Safe",
predicate=lambda row, distance_matrix: ((row["Destination"], row["Source"]) not in distance_matrix) or
(row["Distance"] == distance_matrix[row["Destination"], row["Source"]]),
predicate_kwargs_maker=_distance_matrix)
solution_schema = PanDatFactory(openings=[['City'],[]], assignments=[['City', 'Assigned To'],[]],
parameters=[["Parameter"], ["Value"]])
def solve(dat):
assert input_schema.good_pan_dat_object(dat), "bad dat check"
assert not input_schema.find_duplicates(dat), "duplicate row check"
assert not input_schema.find_foreign_key_failures(dat), "foreign key check"
assert not input_schema.find_data_type_failures(dat), "data type value check"
assert not input_schema.find_data_row_failures(dat), "data row check"
[
'Position', 'Average Draft Position',
'Expected Points', 'Draft Status'
]],
roster_requirements=[['Position'],
[
'Min Num Starters',
'Max Num Starters',
'Min Num Reserve',
'Max Num Reserve',
'Flex Status'
]],
my_draft_positions=[['Draft Position'], []])
# add foreign key constraints (optional, but helps with preventing garbage-in, garbage-out)
input_schema.add_foreign_key("players", "roster_requirements",
['Position', 'Position'])
# set data types (optional, but helps with preventing garbage-in, garbage-out)
input_schema.set_data_type("parameters",
"Parameter",
number_allowed=False,
strings_allowed=[
"Starter Weight", "Reserve Weight",
"Maximum Number of Flex Starters"
])
input_schema.set_data_type("parameters",
"Value",
min=0,
max=float("inf"),
inclusive_min=True,
inclusive_max=False)
# to solution_data.xlsx.
#
from amplpy import AMPL
from ticdat import PanDatFactory, standard_main
# ------------------------ define the input schema --------------------------------
# There are three input tables, with 4 primary key fields and 4 data fields.
input_schema = PanDatFactory(categories=[["Name"],
["Min Nutrition", "Max Nutrition"]],
foods=[["Name"], ["Cost"]],
nutrition_quantities=[["Food", "Category"],
["Quantity"]])
# Define the foreign key relationships
input_schema.add_foreign_key("nutrition_quantities", "foods", ["Food", "Name"])
input_schema.add_foreign_key("nutrition_quantities", "categories",
["Category", "Name"])
# Define the data types
input_schema.set_data_type("categories",
"Min Nutrition",
min=0,
max=float("inf"),
inclusive_min=True,
inclusive_max=False)
input_schema.set_data_type("categories",
"Max Nutrition",
min=0,
max=float("inf"),
inclusive_min=True,