SolutionSet class

The SolutionSet class stores the result of solving a Problem object when multiple runs are produced.

A SolutionSet object is the multi-run counterpart of solution-class. It contains the original problem, run-level design and outcome tables, the list of individual Solution objects, diagnostics summarizing the full set of runs, method metadata, and additional metadata.

Objects of this class are typically created by solve in workflows that generate more than one optimization run, such as weighted-sum scans, \(\epsilon\)-constraint designs, or AUGMECON grids.

Value

No return value. This page documents the SolutionSet class.

Details

Conceptual role

The SolutionSet class represents the result of a multi-run solving workflow:

Problem -> solve() -> SolutionSet

Each run corresponds to one specific optimization subproblem, parameter setting, or trade-off configuration. The SolutionSet object keeps all these runs together in a structured form.

Thus, a SolutionSet is not a single solution with multiple labels, but a collection of distinct Solution objects linked to a shared problem and a shared multi-run design.

Typical use cases

A SolutionSet is typically returned when the chosen solution method generates multiple runs, for example:

• several weighted-sum configurations,

• a grid of \(\epsilon\)-constraint runs,

• an AUGMECON exploration of multiple \(\epsilon\)-combinations,

• or any other workflow producing more than one optimizer call.

Internal structure

A SolutionSet object separates information into several layers:

problem

The original Problem object shared by all runs.

solution

A named list containing the core multi-run optimization outputs. This typically includes the run design, the run summary table, and the list of individual Solution objects.

summary

A named list containing user-facing summaries aggregated across runs when such summaries are available.

diagnostics

A named list containing diagnostics about the solution set as a whole.

method

A named list describing the multi-run optimization method used.

meta

A named list containing additional metadata.

name

A character(1) identifier for the solution set.

Run-level content

The solution field is the main entry point for run-level information. Typical entries include:

design

A data.frame describing the experimental or optimization design, for example weights or \(\epsilon\)-levels.

runs

A data.frame summarizing the outcome of each run. This typically includes run identifiers, solver status, runtime, gap, and objective values.

solutions

A list of individual Solution objects, one per run.

In many workflows, the runs table is the most important compact representation of the solution set, while solutions[[i]] provides the full detailed output for run \(i\).

Objective values and run tables

In multi-objective workflows, the run table often stores objective values in columns named value_<alias>, where <alias> is the alias of a registered objective. For example:

• value_cost,

• value_frag,

• value_benefit.

This naming convention is used by downstream functions such as plot_tradeoff.

Depending on the solving method, the run table may also contain design columns such as:

• weight_<alias> for weighted-sum runs,

• eps_<alias> for \(\epsilon\)-constraint or AUGMECON runs.

Relationship with Solution

A SolutionSet is conceptually a collection of Solution objects. The individual runs are typically stored in: $$ \code{x$solution$solutions[[i]]} $$

where each element is itself a full Solution object.

Therefore:

• use SolutionSet when working with the full set of runs,

• use an individual Solution when inspecting one particular run in detail.

Diagnostics

The diagnostics field stores metadata about the multi-run solve process. Depending on the implementation, it may summarize:

• number of runs,

• status frequencies,

• runtime ranges,

• gap ranges,

• and other aggregate information about the set of runs.

Printing

The print() method provides a concise summary of the full solution set. It reports:

• the optimization method name,

• the participating objective aliases,

• the number of design rows, runs, and stored solutions,

• run-level status summaries,

• runtime and gap ranges,

• and the names of design and objective-value columns when available.

This printed output is intended as a quick overview. Detailed inspection should use:

• x$solution$runs,

• x$solution$design,

• x$solution$solutions[[i]],

• or the accessor methods documented below.

Fields

problem

The Problem object used to generate the full solution set.

solution

A named list containing the core multi-run outputs, typically including design, runs, and solutions.

summary

A named list containing user-facing summaries associated with the solution set.

diagnostics

A named list containing diagnostics about the solution set as a whole.

method

A named list describing the optimization method used.

meta

A named list of additional metadata.

name

A character(1) name for the solution set object.

Methods

print()

Print a concise summary of the solution set, including method name, number of runs, and run-level diagnostics.

show()

Alias of print().

repr()

Return a short one-line representation of the solution set.

getMethod()

Return the method specification stored in self$method.

getDesign()

Return the design table stored in self$solution$design.

getRuns()

Return the run summary table stored in self$solution$runs.

getSolutions()

Return the list of individual Solution objects stored in self$solution$solutions.

See also

solution-class, plot_tradeoff, get_pu, get_actions, get_features, get_targets, solve