Gfrktyl

I have one machine, which produces parts. In machine_failure_rate% it produces faulty parts which need to be produced again. Thus, we end up with a simple queuing problem. Can the following code be futher functionalized? I have the feeling, I can get rid of time_parts, but all I have in mind deteriorates the code as I need further lookups in the production_df data frame to look for "what was produced / what needs to be produced now?". The following script is running:

input_rate <- 1/60 # input rate [1/min, 1/input_rate corresponds to interarrival time in min]

n <- 1000 # number of parts

dt <- 1 # timestep = time to transfer faulty parts back to production. [min]



machine_production_rate <- 1/40 # production rate [1/min]

machine_failure_rate <- 0.2 # machine failure rate





# Sum all interarrival times

set.seed(123456)

t_event <- cumsum(rpois(n, 1/input_rate))



# Create initial list of tasks. Produces parts will be cut off.

time_parts <- data.frame(id = c(1:n),

                         t = t_event,

                         stringsAsFactors = FALSE)





# ========= Functions ==========================================================

create_machine <- function(failure_rate, production_rate) {

  machine <- list()

  machine$failure_rate <- failure_rate

  machine$production_rate <- production_rate

  machine$is_occupied <- FALSE

  return(machine);

}



update_machine <- function(ind_production_df, machine, production_df) {

  if (machine$is_occupied) {

    if (production_df$po_start[ind_production_df] + 1/machine$production_rate <= t) {

      machine$is_occupied <- FALSE

    }

  }

  return(machine)

}



production_summary <- function(production_df, machine, input_rate) {

  no_of_failures <- sum(production_df$no_failures)

  total_production_time <- max(production_df$po_start) + 1/machine$production_rate

  uptime <- (no_of_failures + n)/machine$production_rate

  print(paste0("Estimated machine$failure_rate ", 

               round(no_of_failures/(no_of_failures + n), 2),

               " [theory ", round(machine$failure_rate, 2), "]"))

  print(paste0("Up-time ", uptime, 

               ", of total time ", total_production_time, ". Auslastung ",

               round(uptime/total_production_time, 2),

               " [theory ", round(input_rate/machine$production_rate*1/(1 - machine$failure_rate), 2), "]"))

}





# ========= DE simulation ======================================================

machine <- create_machine(machine_failure_rate, machine_production_rate)

production_df <- data.frame(id = time_parts$id,

                            time = time_parts$t,

                            po_start = rep(0, nrow(time_parts)),

                            no_failures = rep(0, nrow(time_parts)),

                            stringsAsFactors = FALSE)



t <- 0

while (length(time_parts$t) > 0) {

  ind_production_df <- which(production_df$id == time_parts$id[1])



  machine <- update_machine(ind_production_df, machine, production_df)



  if (!machine$is_occupied & time_parts$t[1] <= t) {

    # A machine is available and a part needs to be produced

    machine$is_occupied <- TRUE

    production_df$po_start[ind_production_df] <- t

    if (runif(1) < machine$failure_rate) {

      # bad part

      time_parts$t[1] <- time_parts$t[1] + dt

      time_parts <- time_parts[sort(time_parts$t, index.return = TRUE)$ix, ]



      production_df$no_failures[ind_production_df] <- 

        production_df$no_failures[ind_production_df] + 1

      t <- t + min(time_parts$t[1], dt)

    } else {

      # good part

      if (production_df$po_start[ind_production_df] + 1/machine$production_rate >= t &&

          nrow(time_parts) >= 2) {

        time_parts <- time_parts[2:(nrow(time_parts)), ]

      } else {

        time_parts <- time_parts[FALSE, ]

      }

      t <- t + 1/machine$production_rate

      machine$is_occupied <- FALSE

    }

  } else {

    # machine is occupied or no part needs to be produced

    t <- t + min(time_parts$t[1], dt)

  }

}





# ========= Results ============================================================

production_summary(production_df, machine, input_rate)

Backround: I think about a generalisation (more machines, more input-sources, more complex rules how/when/... parts a produces). I fear that I will end up with tons of unreadable and unmaintainable code-lines if I proceed like this.