Is it possible to further functionalize this code for descrete event simulation












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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 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
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.









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    $begingroup$


    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 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
    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.









    share







    New contributor




    Christoph is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







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      0





      $begingroup$


      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 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
      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.









      share







      New contributor




      Christoph is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      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 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
      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.







      r





      share







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      Christoph is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.










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      Check out our Code of Conduct.








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      asked 5 mins ago









      ChristophChristoph

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      Check out our Code of Conduct.






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