Discrete Optimization using vector of positive integers

[dmaupin12]

I was wondering if the ompr package might be able to solve the following problem i have which I've posted on stackoverflow.
I'm essentially trying to find the best "route" to take in order to maximize value of a function. Any help would be greatly appreciated!

https://stackoverflow.com/questions/69497797/optimize-function-using-positive-parameters-inputs-in-r

Below is the problem which i'm trying to optimize the function (max value) by changing the input which is a vector of integers.

library(dplyr)
df<-data.frame(id=c(1:5),Revenue=sample(201:205))

#Start with random set of numbers to arrange dataframe
vec=c(1,2,5,3,4)

function_1<-function(vec){
  
  df$new_col <- vec 
  df<-df%>%arrange(new_col)
  df2 <-
    df %>% mutate(nearest = transform(., close_prev = id[apply(`diag<-`(m <-
                                                                          as.matrix(dist(id)), Inf) / upper.tri(m), 2, which.min)])) %>% as_tibble()
  df2 <- as_tibble(df2$nearest)
  df2 <-
    ##This is my penalty function which penalizes the revenue if any previous ##row id is less than 5 away from the row id.
    df2 %>% ungroup() %>% mutate(Penalty = ifelse(row_number() == 1, 1, if_else(abs(id- close_prev) > 1, 1, .6))) %>%
    mutate(Revenue_Penalized = Revenue * Penalty)
  df3<<-df2
  df2<- sum(df2$Revenue_Penalized)
  
print(df2)
  
}

function_1(df)