"""
## GAMSSOURCE: https://www.gams.com/latest/gamslib_ml/libhtml/gamslib_thai.html
## LICENSETYPE: Demo
## MODELTYPE: MIP
## DATAFILES: thai.gdx
## KEYWORDS: mixed integer linear programming, routing, naval recruitment, scheduling
Thai Navy Problem (THAI)
This model is used to allocate ships to transport personnel from
different port to a training center.
Choypeng, P, Puakpong, P, and Rosenthal, R E, Optimal Ship Routing
and Personnel Assignment for Naval Recruitment in Thailand.
Interfaces 16, 4 (1986), 356-366.
"""
from __future__ import annotations
import os
from pathlib import Path
from gamspy import Container, Domain, Model, Sense, Sum
def main():
m = Container(
system_directory=os.getenv("SYSTEM_DIRECTORY", None),
load_from=str(Path(__file__).parent.absolute()) + "/thai.gdx",
)
# Sets
i, j, k, sc, vc = m.getSymbols(["i", "j", "k", "sc", "vc"])
# Parameters
(d, shipcap, n, a, w1, w2, w3) = m.getSymbols(
["d", "shipcap", "n", "a", "w1", "w2", "w3"]
)
# Variables
z, y, obj = m.getSymbols(["z", "y", "obj"])
# Equations
(
objdef,
demand,
voycap,
shiplim,
) = m.getSymbols(
[
"objdef",
"demand",
"voycap",
"shiplim",
]
)
objdef[...] = obj == w1 * Sum(
Domain(j, k).where[vc[j, k]], z[j, k]
) + w2 * Sum(
Domain(j, k).where[vc[j, k]], a[j, "dist"] * z[j, k]
) + w3 * Sum(
Domain(j, k, i).where[a[j, i].where[vc[j, k]]],
a[j, "dist"] * y[j, k, i],
)
demand[i] = (
Sum(Domain(j, k).where[a[j, i].where[vc[j, k]]], y[j, k, i]) >= d[i]
)
voycap[j, k].where[vc[j, k]] = (
Sum(i.where[a[j, i]], y[j, k, i]) <= shipcap[k] * z[j, k]
)
shiplim[k] = Sum(j.where[vc[j, k]], z[j, k]) <= n[k]
thainavy = Model(
m,
name="thainavy",
equations=m.getEquations(),
problem="MIP",
sense=Sense.MIN,
objective=obj,
)
z.up[j, k].where[vc[j, k]] = n[k]
thainavy.solve()
print("Objective Function: ", round(thainavy.objective_value, 3))
if __name__ == "__main__":
main()
