Decision Optimization

Once the solver research is launched, its strategy cannot change.
However, a common practice is to start a new search, with its own phases, using a previously reached solution as a Starting Point.
This might suit your situation

Once the solver research is launched, its strategy cannot change.
However, a common practice is to start a new search, with its own phases, using a previously reached solution as a Starting Point.
This might suit your situation

If a CpoSolveResult contains a solution (is_solution()), you can get its CpoModelSolution representation directly (get_solution()).
Note that the methods implemented in the class CpoModelSolution to access solution elements are available directly CpoSolveResult,
so you can use them as condition to end the current search (end_search())

The general idea is:
- start a first search, examine each new solution until it meet your criteria; then store a first solution and end the search
- start a second search using the first solution as a starting point.
Each search might use its proper search phases.

A starting point accepts partial CpoModelSolution . Indeed  you can create_empty_solution() then populate it with external values such as values extracted from previous solutions. (e.g. plant_location_with_starting_point)

Once the solver research is launched, its strategy cannot change.
However, a common practice is to start a new search, with its own phases, using a previously reached solution as a Starting Point.
This might suit your situation

If a CpoSolveResult contains a solution (is_solution()), you can get its CpoModelSolution representation directly (get_solution()).
Note that the methods implemented in the class CpoModelSolution to access solution elements are available directly CpoSolveResult,
so you can use them as condition to end the current search (end_search())

The general idea is:
- start a first search, examine each new solution until it meet your criteria; then store a first solution and end the search
- start a second search using the first solution as a starting point.
Each search might use its proper search phases.

A starting point accepts partial CpoModelSolution . Indeed  you can create_empty_solution() then populate it with external values such as values extracted from previous solutions. (e.g. plant_location_with_starting_point)

Once the solver research is launched, its strategy cannot change.
However, a common practice is to start a new search, with its own phases, using a previously reached solution as a Starting Point.
This might suit your situation

If a CpoSolveResult contains a solution (is_solution()), you can get its CpoModelSolution representation directly (get_solution()).
Note that the methods implemented in the class CpoModelSolution to access solution elements are available directly CpoSolveResult,
so you can use them as condition to end the current search (end_search())

The general idea is:
- start a first search, examine each new solution until it meet your criteria; then store a first solution and end the search
- start a second search using the first solution as a starting point.
Each search might use its proper search phases.

A starting point accepts partial CpoModelSolution . Indeed  you can create_empty_solution() then populate it with external values such as values extracted from previous solutions. (e.g. plant_location_with_starting_point)

Once the solver research is launched, its strategy cannot change.
However, a common practice is to start a new search, with its own phases, using a previously reached solution as a Starting Point.
This might suit your situation

If a CpoSolveResult contains a solution (is_solution()), you can get its CpoModelSolution representation directly (get_solution()).
Note that the methods implemented in the class CpoModelSolution to access solution elements are available directly CpoSolveResult,
so you can use them as condition to end the current search (end_search())

The general idea is:
- start a first search, examine each new solution until it meet your criteria; then store a first solution and end the search
- start a second search using the first solution as a starting point.
Each search might use its proper search phases.

A starting point accepts partial CpoModelSolution . Indeed  you can create_empty_solution() then populate it with external values such as values extracted from previous solutions. (e.g. plant_location_with_starting_point)

Once the solver research is launched, its strategy cannot change.
However, a common practice is to start a new search, with its own phases, using a previously reached solution as a Starting Point.
This might suit your situation

On this page you can find the documentation about search phases and variable/value selectors:

https://www.ibm.com/docs/en/icos/22.1.0?topic=search-ordering-variables-values

You have several level of selection:
1. Order  groups of variables
2. Use already-defined variable and value selectors on a group
3. Write your own selector for variables and values. 

Regards

Philippe

If a CpoSolveResult contains a solution (is_solution()), you can get its CpoModelSolution representation directly (get_solution()).
Note that the methods implemented in the class CpoModelSolution to access solution elements are available directly CpoSolveResult,
so you can use them as condition to end the current search (end_search())

The general idea is:
- start a first search, examine each new solution until it meet your criteria; then store a first solution and end the search
- start a second search using the first solution as a starting point.
Each search might use its proper search phases.

A starting point accepts partial CpoModelSolution . Indeed  you can create_empty_solution() then populate it with external values such as values extracted from previous solutions. (e.g. plant_location_with_starting_point)

Once the solver research is launched, its strategy cannot change.
However, a common practice is to start a new search, with its own phases, using a previously reached solution as a Starting Point.
This might suit your situation