main.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import os, glob
from solver import *
from problem_instance import *
from solution import *
from utils import find_step
import gurobipy as gp
import time

if __name__ == '__main__':
    #verobse
    verbose = True
    #total time available to gurobi
    T_avail = 595
    #NOTICE THAT INSIDE THE TWO SOLVER A 4 THREAD CONSTRAINT IS ACTIVE FOR COMPETITION REQUIREMENTS.
    # Hidden/Public
    inType = 'p' #'l' 'h' , 'p'
    #check for gurobi version. if not 9.5.1 or .2, iterative model building too expensive. 
    # Get the Gurobi version
    version = gp.gurobi.version()

    # Check if version is 9.5.1. or .2. If not, the model building is so slow that the decomposition method makes no sense. I don't know why. we tried several machines and pre_solve options. it is always 10 times slower than 9.5.
    if version[0] != 9 or version[1] != 5:
        raise ValueError(f"Incompatible Gurobi version. Expected 9.5.1 or .2, but found {version[0]}.{version[1]}.{version[2]}")
    else:
        print("Gurobi version 9.5.2 confirmed. Proceeding...")

    
    ###########
    ## Selection of the dataset
    ###########
    if inType == 'p':
        foldername      = 'ihtc2024_competition_instances'
        filenames       = [f'i{i:02}' for i in range(1, 31)]         # i01 … i30
    elif inType == 'h':
        foldername      = 'ihtc2024_final_instances'
        filenames       = [f'm{i:02}' for i in range(1, 31)]         # m01 … m30
    elif inType == 'l':
        foldername      = 'large_instances'
        pattern         = os.path.join('./datasets/', foldername, 'l*.json')
        # strip extension → l35_1, l35_2, …
        filenames       = [os.path.splitext(os.path.basename(p))[0]
                           for p in glob.glob(pattern)]
        filenames.sort()
    else:
        raise ValueError(f"Unknown inType '{inType}'")

    ########
    ## Iteration on the instances
    ########

    #uncomment for specfic instances (check that the foldername fits)
    # filenames = ['i16']

    for filename in filenames:
        
        print(f"\n|--- {filename} ---|")

        t_start = time.time()

        inst = InstanceGlobal(os.path.join('.', 'datasets', foldername, f'{filename}.json'))
        sol_two = Solution(inst)

        #step generation and actual solve
        step, H = find_step(inst.N_PATIENTS,inst.N_DAYS)
        #
        if verbose:
            print('inst: ',filename,'initial step: ', step, 'initial H: ', H)
        # 

        # Initialize sub solvers
        #NOTICE: CURRENTLY 4 THREADS CONSTRAINT IS HARDCODED INSIDE BOTH THE SOLVER.
        #
        sub_sSCPPAS_two = HPT_SCPPAS(**{'v': inst.N_DAYS, 'H': H, 'step': step, 'verbose': verbose})
        sub_sNRA_two = NRA(**{'verbose': verbose})
        # Initialized sub solvers manager
        seq_solver_two = Seq_solver_two(sol_two, sub_sSCPPAS_two, sub_sNRA_two, percTimeNRA=0.1) #percTime is how much of the total time is given minimum to the NRA, here 10%
        seq_solver_two.populate(inst) #populate the subsolvers with the specific instances. To be fair, when the solver is the same only a populate should be called within the loop. But the code is not do optimized rn, sorry.

        #actual remaining time for the solver 
        remaining_time = int(T_avail - (time.time() - t_start))
        ###run the solver
        try:
            seq_solver_two.solve(res_time=remaining_time)
        except ValueError as e:
            print(f"Failed to solve: {str(e)}")

        #########
        ##Solution visualization and json dump  (notice that as worst case a empty solution is dumped even if unfeasible)
        #########
        # #This can be launched only if cpp precompiled on the validator
        violations, of = sol_two.eval(light_verbose=verbose, verbose = False) #when light verbose, costs per category are printed
        # print performance
        print(f"{violations},{of}")

        print(f"TIME: {'{:.2f}'.format(time.time() - t_start)}\n")
        #uncomment to save
        sol_two.json_dump(f'./results/sol_{filename}.json')