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import random  ## to generate the items
from pulp import * ## import pulp-or functions


	
class MasterProblem:
	def __init__(self, maxValue, itemLengths, itemDemands, initialPatterns, problemname):
		
		self.maxValue=maxValue
		self.itemLengths=itemLengths

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# Back-Propagation Neural Networks
#
# Written in Python.  See http://www.python.org/
# Placed in the public domain.
# Neil Schemenauer <nas@arctrix.com>

# Adapted for instructional purposes by Bart Smeets <bartsmeets86@gmail.com>

import math
import random

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library(R6)
library(simmer)

BatchGate <- R6Class("BatchGate",
                     public = list(
                       queue_length = 0,
                       opens_at_length = NA,
                       open = FALSE,
                       allowed_to_pass = 0,
                       initialize = function(opens_at_length){

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library(lpSolveAPI)

#used for result visualization
library(ggplot2)
library(reshape)
library(gridExtra)

#define the datasets
 
train<-data.frame(wagon=c('w1','w2','w3'), weightcapacity=c(10,8,12), spacecapacity=c(5000,4000,8000))

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{
  "env": {
    "resources": {
      "nurse": {
        "capacity": 1
      },
      "doctor": {
        "capacity": 1
      }
    },

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{
  "env": {
    "resources": {
      "nurse": {
        "capacity": 1
      },
      "doctor": {
        "capacity": 1
      }
    },

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Evolution hospital beds, resources and consumption
========================================================
```{r setup, echo=FALSE, message=FALSE, warning=FALSE}
imgur_insecure<-function (file, key = "75ace4c8e871b86") 
{
    if (!is.character(key)) 
        stop("The Imgur API Key must be a character string!")
    res = RCurl::postForm("https://api.imgur.com/3/image.xml", 
        image = RCurl::fileUpload(file), .opts = RCurl::curlOptions(httpheader = c(Authorization = paste("Client-ID", 
            key)),ssl.verifypeer = FALSE))

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