# Type alias: WorkerParameters

WorkerParameters: {`"cumulPropagationLevel"`

:`number`

;`"fdsAdditionalStepRatio"`

:`number`

;`"fdsBothFailRewardFactor"`

:`number`

;`"fdsBranchOnObjective"`

:`boolean`

;`"fdsEpsilon"`

:`number`

;`"fdsEventTimeInfluence"`

:`number`

;`"fdsFixedAlpha"`

:`number`

;`"fdsInitialRating"`

:`number`

;`"fdsInitialRestartLimit"`

:`number`

;`"fdsLBResetRatings"`

:`boolean`

;`"fdsLBStrategy"`

:`"Minimum"`

|`"Random"`

|`"Split"`

;`"fdsLengthStepRatio"`

:`number`

;`"fdsMaxCounterAfterRestart"`

:`number`

;`"fdsMaxCounterAfterSolution"`

:`number`

;`"fdsMaxInitialChoicesPerVariable"`

:`number`

;`"fdsMaxInitialIntVarChoiceStep"`

:`number`

;`"fdsPresenceStatusChoices"`

:`boolean`

;`"fdsRatingAverageComparison"`

:`"Off"`

|`"Global"`

|`"Depth"`

;`"fdsRatingAverageLength"`

:`number`

;`"fdsReductionFactor"`

:`"Normal"`

|`"Zero"`

|`"Random"`

;`"fdsReductionWeight"`

:`number`

;`"fdsResetRestartsAfterSolution"`

:`boolean`

;`"fdsRestartGrowthFactor"`

:`number`

;`"fdsRestartStrategy"`

:`"Geometric"`

|`"Nested"`

|`"Luby"`

;`"fdsReuseClosing"`

:`boolean`

;`"fdsStrongBranchingCriterion"`

:`"Both"`

|`"Left"`

|`"Right"`

;`"fdsStrongBranchingDepth"`

:`number`

;`"fdsStrongBranchingSize"`

:`number`

;`"fdsUniformChoiceStep"`

:`boolean`

;`"fdsUseNogoods"`

:`boolean`

;`"noOverlapPropagationLevel"`

:`number`

;`"propagationTraceLevel"`

:`number`

;`"randomSeed"`

:`number`

;`"reservoirPropagationLevel"`

:`number`

;`"searchTraceLevel"`

:`number`

;`"searchType"`

:`"LNS"`

|`"FDS"`

|`"FDSLB"`

|`"SetTimes"`

;`"simpleLBMaxIterations"`

:`number`

;`"simpleLBShavingRounds"`

:`number`

;`"simpleLBWorker"`

:`number`

;`"stepFunctionSumPropagationLevel"`

:`number`

; }

WorkerParameters allow to specify how each individual worker of the solver should behave. It is part of the Parameters object.

If a parameter is not listed here then it can be set only globally (in Parameters}), not per worker. For example *timeLimit* or *logPeriod* are
global parameters.

## Type declaration

### cumulPropagationLevel?

`optional`

cumulPropagationLevel:`number`

How much to propagate constraints on cumul functions.

This parameter controls the amount of propagation done for Model.cumulLe constraint when used with a sum of pulses. The bigger the value, the more algorithms are used for propagation. It means that more time is spent by the propagation, and possibly more values are removed from domains. More propagation doesn't necessarily mean better performance. FDS search (see searchType) usually benefits from higher propagation levels.

The parameter takes an unsigned integer value in range `1..3`

.
The default value is `1`

.

### fdsAdditionalStepRatio?

`optional`

fdsAdditionalStepRatio:`number`

Domain split ratio when run out of choices.

When all choices are decided, and a greedy algorithm cannot find a solution, then more choices are generated by splitting domains into the specified number of pieces.

The parameter takes a floating point value in range `2.000000..Infinity`

.
The default value is `7`

.

### fdsBothFailRewardFactor?

`optional`

fdsBothFailRewardFactor:`number`

How much to improve rating when both branches fail immediately.

This parameter sets a bonus reward given to a choice when both left and right branches fail immediately. Current rating of both branches is multiplied by the specified value.

The parameter takes a floating point value in range `0..1`

.
The default value is `0.98`

.

### fdsBranchOnObjective?

`optional`

fdsBranchOnObjective:`boolean`

Whether to generate choices for objective expression/variable.

This option controls the generation of choices on the objective. It works regardless of the objective is given by an expression or a variable.

The default value is `false`

.

### fdsEpsilon?

`optional`

fdsEpsilon:`number`

How often to chose a choice randomly.

Probability that a choice is taken randomly. A randomly selected choice is not added into the search tree automatically. Instead, the choice is tried, its rating is updated, but it is added into the search tree only if one of the branches fails. The mechanism is similar to strong branching.

The parameter takes a floating point value in range `0.000000..0.999990`

.
The default value is `0.1`

.

### fdsEventTimeInfluence?

`optional`

fdsEventTimeInfluence:`number`

Influence of event time to initial choice rating.

When non-zero, then the initial choice rating is influenced by the date of the choice. This way, very first choices in the search should be taken chronologically.

The parameter takes a floating point value in range `0..1`

.
The default value is `0`

.

### fdsFixedAlpha?

`optional`

fdsFixedAlpha:`number`

When non-zero, alpha factor for rating updates.

When this parameter is set to a non-zero, then parameter FDSRatingAverageLength is ignored. Instead, the rating of a branch is computed as an exponential moving average with the given parameter alpha.

The parameter takes a floating point value in range `0..1`

.
The default value is `0`

.

### fdsInitialRating?

`optional`

fdsInitialRating:`number`

Initial rating for newly created choices.

Default rating for newly created choices. Both left and right branches get the same rating. Choice is initially permuted so that bigger domain change is the left branch.

The parameter takes a floating point value in range `0.000000..2.000000`

.
The default value is `0.5`

.

### fdsInitialRestartLimit?

`optional`

fdsInitialRestartLimit:`number`

Fail limit for the first restart.

Failure-directed search is periodically restarted: explored part of the current search tree is turned into a no-good constraint, and the search starts again in the root node. This parameter specifies the size of the very first search tree (measured in number of failures).

The parameter takes an unsigned integer value in range `1..9223372036854775807`

.
The default value is `100`

.

### fdsLBResetRatings?

`optional`

fdsLBResetRatings:`boolean`

Whether to reset ratings when a new LB is proved.

When this parameter is on, and FDSLB proves a new lower bound then all ratings are reset to default values.

The default value is `false`

.

### fdsLBStrategy?

`optional`

fdsLBStrategy:`"Minimum"`

|`"Random"`

|`"Split"`

A strategy to choose objective cuts during FDSLB search..

Possible values are:

`Minimum`

: Always change the cut by the minimum amount. The default value.`Random`

: At each restart, randomly choose a value in range LB..UB.`Split`

: Always split the current range LB..UB in half.

The default value is `Minimum`

.

### fdsLengthStepRatio?

`optional`

fdsLengthStepRatio:`number`

Choice step relative to average length.

Ratio of initial choice step size to the minimum length of interval variable.When FDSUniformChoiceStep is set, this ratio is used to compute global choice step using the average of interval var length.When FDSUniformChoiceStep is not set, this ratio is used to compute the choice step for every interval var individually.

The parameter takes a floating point value in range `0.000000..Infinity`

.
The default value is `0.699999988079071`

.

### fdsMaxCounterAfterRestart?

`optional`

fdsMaxCounterAfterRestart:`number`

Truncate choice use counts after a restart to this value.

The idea is that ratings learned in the previous restart are less valid in the new restart. Using this parameter, it is possible to truncate use counts on choices so that new local ratings will have bigger weights (when FDSFixedAlpha is not used).

The parameter takes an unsigned integer value.
The default value is `255`

.

### fdsMaxCounterAfterSolution?

`optional`

fdsMaxCounterAfterSolution:`number`

Truncate choice use counts after a solution is found.

Similar to `FDSMaxCounterAfterRestart`

, this parameter allows truncating use counts on choices when a solution is found.

The parameter takes an unsigned integer value.
The default value is `255`

.

### fdsMaxInitialChoicesPerVariable?

`optional`

fdsMaxInitialChoicesPerVariable:`number`

Maximum number of choices generated initially per a variable.

Initial domains are often very large (e.g., `0..IntervalMax`

). Therefore initial
number of generated choices is limited: only choices near startMin are kept.

The parameter takes an unsigned integer value in range `2..2147483647`

.
The default value is `90`

.

### fdsMaxInitialIntVarChoiceStep?

`optional`

fdsMaxInitialIntVarChoiceStep:`number`

Maximum step when generating initial choices for integer variables..

For a variable with big initial domains, choices are generated only around min value considering the given step.

The parameter takes an unsigned integer value in range `1..1073741823`

.
The default value is `107374182`

.

### fdsPresenceStatusChoices?

`optional`

fdsPresenceStatusChoices:`boolean`

Whether to generate choices on presence status.

Choices on start time also include a choice on presence status. Therefore, dedicated choices on presence status only are not mandatory.

The default value is `true`

.

### fdsRatingAverageComparison?

`optional`

fdsRatingAverageComparison:`"Off"`

|`"Global"`

|`"Depth"`

Whether to compare the local rating with the average.

Possible values are:

`Off`

(the default): No comparison is done.`Global`

: Compare with the global average.`Depth`

: Compare with the average on the current search depth

Arithmetic average is used for global and depth averages.

The default value is `Off`

.

### fdsRatingAverageLength?

`optional`

fdsRatingAverageLength:`number`

Length of average rating computed for choices.

For the computation of rating of a branch. Arithmetic average is used until the branch is taken at least FDSRatingAverageLength times. After that exponential moving average is used with parameter alpha = 1 - 1 / FDSRatingAverageLength.

The parameter takes an integer value in range `0..254`

.
The default value is `25`

.

### fdsReductionFactor?

`optional`

fdsReductionFactor:`"Normal"`

|`"Zero"`

|`"Random"`

Reduction factor R for rating computation.

Possible values are:

`Normal`

(the default): Normal reduction factor.`Zero`

: Factor is not used (it is 0 all the time).`Random`

: A random number in the range [0,1] is used instead.

The default value is `Normal`

.

### fdsReductionWeight?

`optional`

fdsReductionWeight:`number`

Weight of the reduction factor in rating computation.

When computing the local rating of a branch, multiply reduction factor by the given weight.

The parameter takes a floating point value in range `0.000000..Infinity`

.
The default value is `1`

.

### fdsResetRestartsAfterSolution?

`optional`

fdsResetRestartsAfterSolution:`boolean`

Reset restart size after a solution is found (ignored in Luby).

When this parameter is set (the default), then restart limit is set back to `FDSInitialRestartLimit`

when a solution is found.

The default value is `true`

.

### fdsRestartGrowthFactor?

`optional`

fdsRestartGrowthFactor:`number`

Growth factor for fail limit after each restart.

After each restart, the fail limit for the restart is multiplied by the specified factor.
This parameter is ignored when fdsRestartStrategy is `Luby`

.

The parameter takes a floating point value in range `1.000000..Infinity`

.
The default value is `1.15`

.

### fdsRestartStrategy?

`optional`

fdsRestartStrategy:`"Geometric"`

|`"Nested"`

|`"Luby"`

Restart strategy to use.

This parameter specifies how the restart limit (maximum number of failures) changes from restart to restart. Possible values are:

`Geometric`

(the default): After each restart, restart limit is multiplied by fdsRestartGrowthFactor.`Nested`

: Similar to`Geometric`

but the limit is changed back to fdsInitialRestartLimit each time a new maximum limit is reached.`Luby`

: Luby restart strategy is used. Parameter fdsRestartGrowthFactor is ignored.

The default value is `Geometric`

.

### fdsReuseClosing?

`optional`

fdsReuseClosing:`boolean`

Whether always reuse closing choice.

Most of the time, FDS reuses closing choice automatically. This parameter enforces it all the time.

The default value is `false`

.

### fdsStrongBranchingCriterion?

`optional`

fdsStrongBranchingCriterion:`"Both"`

|`"Left"`

|`"Right"`

How to choose the best choice in strong branching.

Possible values are:

`Both`

: Choose the the choice with best combined rating.`Left`

(the default): Choose the choice with the best rating of the left branch.`Right`

: Choose the choice with the best rating of the right branch.

The default value is `Left`

.

### fdsStrongBranchingDepth?

`optional`

fdsStrongBranchingDepth:`number`

Up-to what search depth apply strong branching.

Strong branching is typically used in the root node. This parameter controls the maximum search depth when strong branching is used.

The parameter takes an unsigned integer value.
The default value is `6`

.

### fdsStrongBranchingSize?

`optional`

fdsStrongBranchingSize:`number`

Number of choices to try in strong branching.

Strong branching means that instead of taking a choice with the best rating, we take the specified number (FDSStrongBranchingSize) of best choices, try them in dry-run mode, measure their local rating, and then chose the one with the best local rating.

The parameter takes an unsigned integer value.
The default value is `10`

.

### fdsUniformChoiceStep?

`optional`

fdsUniformChoiceStep:`boolean`

Whether all initial choices have the same step length.

When set then initial choices generated on interval variables have all the same step size.

The default value is `true`

.

### fdsUseNogoods?

`optional`

fdsUseNogoods:`boolean`

Whether to use or not nogood constraints.

By default, no-good constraint is generated after each restart. This parameter allows to turn no-good constraints off.

The default value is `true`

.

### noOverlapPropagationLevel?

`optional`

noOverlapPropagationLevel:`number`

How much to propagate noOverlap constraints.

This parameter controls the amount of propagation done for noOverlap constraints. The bigger the value, the more algorithms are used for propagation. It means that more time is spent by the propagation, and possibly more values are removed from domains. More propagation doesn't necessarily mean better performance. FDS search (see searchType) usually benefits from higher propagation levels.

The parameter takes an unsigned integer value in range `1..4`

.
The default value is `2`

.

### propagationTraceLevel?

`optional`

propagationTraceLevel:`number`

Level of propagation trace.

This parameter is available only in the development edition of the solver.

When set to a value bigger than zero, the solver prints a trace of the propagation, that is a line for every domain change. The higher the value, the more information is printed.

The parameter takes an unsigned integer value in range `0..5`

.
The default value is `0`

.

### randomSeed?

`optional`

randomSeed:`number`

Random seed.

The solver breaks ties randomly using a pseudorandom number generator. This parameter sets the seed of the generator.

Note that when Parameters.nbWorkers is more than 1 then there is also another source of randomness: the time it takes for a message to pass from one worker to another. Therefore with Parameters.nbWorkers=1 the solver is deterministic (random behavior depends only on random seed). With Parameters.nbWorkers>1 the solver is not deterministic.

Even with NbSeeds=1 and the same random seed, the solver may behave differently on different platforms. This can be due to different implementations of certain functions such as `std::sort`

.

The parameter takes an unsigned integer value.
The default value is `1`

.

### reservoirPropagationLevel?

`optional`

reservoirPropagationLevel:`number`

How much to propagate constraints on cumul functions.

This parameter controls the amount of propagation done for Model.cumulLe and Model.cumulGe when used together with steps (Model.stepAtStart, Model.stepAtEnd, Model.stepAt). The bigger the value, the more algorithms are used for propagation. It means that more time is spent by the propagation, and possibly more values are removed from domains. More propagation doesn't necessarily mean better performance. FDS search (see searchType) usually benefits from higher propagation levels.

The parameter takes an unsigned integer value in range `1..2`

.
The default value is `1`

.

### searchTraceLevel?

`optional`

searchTraceLevel:`number`

Level of search trace.

This parameter is available only in the development edition of the solver.

When set to a value bigger than zero, the solver prints a trace of the search. The trace contains information about every choice taken by the solver. The higher the value, the more information is printed.

The parameter takes an unsigned integer value in range `0..5`

.
The default value is `0`

.

### searchType?

`optional`

searchType:`"LNS"`

|`"FDS"`

|`"FDSLB"`

|`"SetTimes"`

Type of search to use.

Possible values are:

`LNS`

: Large Neighborhood Search`FDS`

: Failure-Directed Search`FDSLB`

: Failure-Directed Searching working on lower bound`SetTimes`

: Depth-first set-times search (not restarted)

The default value is `LNS`

.

### simpleLBMaxIterations?

`optional`

simpleLBMaxIterations:`number`

Maximum number of feasibility checks.

Simple lower bound is computed by binary search for the best objective value that is not infeasible by propagation. This parameter limits the maximum number of iterations of the binary search.

The parameter takes an unsigned integer value in range `1..2147483647`

.
The default value is `2147483647`

.

### simpleLBShavingRounds?

`optional`

simpleLBShavingRounds:`number`

Number of shaving rounds.

When non-zero, the solver will use shaving on variable domains to improve the lower bound. This parameter controls the number of shaving rounds.

The parameter takes an unsigned integer value in range `0..2147483647`

.
The default value is `0`

.

### simpleLBWorker?

`optional`

simpleLBWorker:`number`

Which worker computes simple lower bound.

Simple lower bound is a bound such that infeasibility of a better objective can be proved by propagation only (without the search). Simple lower bound is computed by the given worker before its start normal search. If worker with the given number doesn't exist then the lower bound is not computed.

The parameter takes an integer value in range `-1..2147483647`

.
The default value is `0`

.

### stepFunctionSumPropagationLevel?

`optional`

stepFunctionSumPropagationLevel:`number`

How much to propagate stepFunctionSum expression.

This parameter controls the amount of propagation done for Model.stepFunctionSum expressions. In particular, it controls whether the propagation also affects the minimum and the maximum length of the associated interval variable:

`1`

: The length is updated only once during initial constraint propagation.`2`

: The length is updated every time the expression is propagated.

The parameter takes an unsigned integer value in range `1..2`

.
The default value is `1`

.