hros

[tool.poetry]
name = "tproj"
version = "0.1.0"
description = ""
authors = ["Your Name <you@example.com>"]
readme = "README.md"

[tool.poetry.dependencies]
python = "^3.11"

hros

using Random: AbstractRNG, GLOBAL_RNG, MersenneTwister,shuffle!; import Future
using Base.Threads: nthreads, threadid
using BenchmarkTools

println("nthreads = $(nthreads())")

PAR_RNG = let m = MersenneTwister(1)
    [m; accumulate(Future.randjump, fill(big(10)^20, nthreads() - 1), init = m)]
end;

hros

using Cxx

cxx"""
class A{
private:
    int a;
public:
    A(int n) : a(n) {}
    friend int operator==(const A& x, const A& y);
    A operator+(const A& other) const {return A(a+other.a);}

hros

from typing import Dict, Tuple, List
from math import ceil
import gurobipy as grb
from gurobipy import GRB
from simulation import Scenario, \
    cost_of_preemption, risk_of_adverse_outcome, \
    Patient, SubVisit

# a helper dictionary to iterate over each treatment visit of patients
# a patient with two visit will result in two keys: (p,0), (p,1)

hros

from gurobipy import *
# inputs to code snippet:
# m - gurobi model object
# s - a scenario object containing the tasks' and agents' parameters (self explanatory)
N = s.num_tasks
C = s.num_agents
# add one time step to time range to simplify preemption constraints
t_range = range(s.t_range[0], s.t_range[-1] + 2)
	
# agent dependent task processing time ($j_p /\eta^i_{j_\tau}$)