R/max_cov_relocation.R
max_coverage_relocation.Rd
This function adds a relocation step
max_coverage_relocation(existing_facility = NULL, proposed_facility, user, distance_cutoff, cost_install, cost_removal, cost_total, solver = "lpSolve", return_early = FALSE)
existing_facility | data.frame containing the facilities that are already in existing, with columns names lat, and long. |
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proposed_facility | data.frame containing the facilities that are being proposed, with column names lat, and long. |
user | data.frame containing the users of the facilities, along with column names lat, and long. |
distance_cutoff | numeric indicating the distance cutoff (in metres) you are interested in. If a number is less than distance_cutoff, it will be 1, if it is greater than it, it will be 0. |
cost_install | integer the cost of installing a new facility |
cost_removal | integer the cost of removing a facility |
cost_total | integer the total cost allocated to the project |
solver | character "glpk" (default) or "lpSolve". "gurobi" is currently in development, see https://github.com/njtierney/maxcovr/issues/25 |
return_early | logical TRUE if I do not want to run the extraction process, FALSE if I want to just return the lpsolve model etc. |
dataframe of results
# NOT RUN { library(dplyr) # subset to be the places with towers built on them. york_selected <- york %>% filter(grade == "I") york_unselected <- york %>% filter(grade != "I") # OK, what if I just use some really crazy small data to optimise over. # mc_relocate <- max_coverage_relocation(existing_facility = york_selected, proposed_facility = york_unselected, user = york_crime, distance_cutoff = 100, cost_install = 5000, cost_removal = 200, cost_total = 600000) mc_relocate summary(mc_relocate) # }