Description Usage Arguments Details Value See Also Examples

`Optimize_WSO`

creates and optimizes the Weighted Sum Optimization (WSO) in Julia environment.
It requires two objective functions and their respective positive weights.

(For detailed information refer to the paper)

1 2 | ```
Optimize_WSO(First_Objective=0, Second_Objective=0, Risk_Objective=0,
Time_limit=1e7, Solver="SCIP", Silence= FALSE, Env= .GlobalEnv)
``` |

`First_Objective` |
A float parameter: defining the weight of the first objective function ( |

`Second_Objective` |
A float parameter: defining the weight of the second objective function ( |

`Risk_Objective` |
A float parameter: defining the weight of the Risk objective function ( |

`Time_limit` |
A double: the total time limit in seconds |

`Solver` |
A string: defining the solver to be used to solve the problem. (Default: "SCIP") |

`Silence` |
A binary parameter: if |

`Env` |
the environment where the package should create or access variables. By default the package works in the R's Global environment. (Default: .GlobalEnv) |

In the list that `Optimize_WSO`

returns, `[["Status"]]`

defines the status of the returned WSO solution.

If `[["Status"]]="OPTIMAL"`

, the WSO was solved to optimality and the Parcels' optimal status are stored in `[["Result"]]`

.
Also, the WSO optimal values of objectives will be stored in either `[["First_Objective"]]`

, `[["Second_Objective"]]`

, or `[["Risk_Objective"]]`

, based on the input objectives.

If `[["Status"]]="TIME_LIMIT"`

, the solver was terminated because the time limit was reached.
If any feasible solution was reported by the solver, it would be stored in `[["Result"]]`

. Otherwise, `[["Result"]]`

and objective values would be
all equal to "N/A".

If `[["Status"]]="INFEASIBLE"`

, the problem is infeasible. So, no solution would be reported.

In any other case, the `[["Status"]]`

will be the solution status that the solver has reported.

**Note 1:** The weights should be non negative, and two objectives should have positive weights.

A list with 8 members:

`[["Solution_time"]]`

: a numeric value; defining the time spent in the solver in seconds.

`[["Status"]]`

: a character; defining the status of the solution.

`[["Gap"]]`

: a numeric value; defining the relative optimality gap of the solution.

`[["Result"]]`

: a vector of binaries; its `i`

'th element defines if the parcel named `Parcels[i]`

is protected or not. Comparing these values with the values of `Status`

defined when creating the problem,
the user can find which parcels are to be invested, divested, or remain the same.

`[["Firs_Objective"]]`

: a numveric value; (if applicable) returns the WSO value of first objective.

`[["Second_Objective"]]`

: a numveric value; (if applicable) returns the WSO value of second objective.

`[["Risk_Objective"]]`

: a numveric value; (if applicable) returns the WSO value of risk objective.

Other Optimizer Functions:
`Load_Problem()`

,
`Optimize_First_Objective()`

,
`Optimize_NBP()`

,
`Optimize_Second_Objective()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## Not run:
## to find WSO solution for First Objective and Second Objective with weights equal to 1 and 2: ##
Optimize_WSO(First_Objective=1, Second_Objective=2, Time_limit=1e7, Solver="SCIP", Silence= FALSE)
## to find WSO solution for First Objective and Risk Objective with weights equal to 1 and 2: ##
Optimize_WSO(First_Objective=1, Risk_Objective=2, Time_limit=1e7, Solver="SCIP", Silence= FALSE)
## to find WSO solution for Second Objective and Risk Objective with weights equal to 1 and 2: ##
Optimize_WSO(Second_Objective=1, Risk_Objective=2, Time_limit=1e7, Solver="SCIP", Silence= FALSE)
## End(Not run)
``` |

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