Define

Describe Your Planning Problem

Capture entities, values, constraints, and solver settings in a guided experience that matches the main PlanningPowerTools platform.

Problem Type

Not set

Entities

0

Tasks, jobs, or shifts defined in Step 2

Values

0

Resources or people defined in Step 3

Constraints

0

Hard / soft rules shaping the solver

Timeout (sec)

120

Latest JSON Preview AI Improved

Start the wizard to generate your planning problem JSON.

Visualize

Solver Output

Review the latest job returned by Timefold Solver. Launch a new solve whenever you are ready.

Waiting for job

Last Job ID

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Completed At

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Assignment Summary

No solution data yet. Run Solve to populate this section.

Raw Solution JSON

Awaiting solver results.

Insights

Operational Intelligence

Dive into KPIs, constraint hits, and feasibility analysis for the latest solve.

Total Violations

—

Hard / medium / soft constraint breaches

Key Metrics

No insights available.

Insights JSON

Run Solve to generate insight data.

Algorithm	Description	Best For
FIRST_FIT	Assigns entities to first available value	Simple problems, fast initial solution
FIRST_FIT_DECREASING	Sorts entities by difficulty, then assigns	Default - Good balance for most problems
WEAKEST_FIT	Assigns to value with least remaining capacity	Capacity-constrained problems
WEAKEST_FIT_DECREASING	Sorts entities, assigns to weakest fit	Capacity-constrained with entity ordering
STRONGEST_FIT	Assigns to value with most remaining capacity	Problems where strong fits are preferred
STRONGEST_FIT_DECREASING	Sorts entities, assigns to strongest fit	Problems with entity ordering needs
CHEAPEST_INSERTION	Finds cheapest entity-value combination	Cost-minimization problems
ALLOCATE_ENTITY_FROM_QUEUE	Generic queue-based allocation	Custom entity ordering
ALLOCATE_TO_VALUE_FROM_QUEUE	Round-robin value assignment	Value-constrained problems
ALLOCATE_FROM_POOL	Pool-based best-fit allocation	Complex multi-criteria problems

Algorithm

Description

Best For

FIRST_FIT

Assigns entities to first available value

Simple problems, fast initial solution

FIRST_FIT_DECREASING

Sorts entities by difficulty, then assigns

Default - Good balance for most problems

WEAKEST_FIT

Assigns to value with least remaining capacity

Capacity-constrained problems

WEAKEST_FIT_DECREASING

Sorts entities, assigns to weakest fit

Capacity-constrained with entity ordering

STRONGEST_FIT

Assigns to value with most remaining capacity

Problems where strong fits are preferred

STRONGEST_FIT_DECREASING

Sorts entities, assigns to strongest fit

Problems with entity ordering needs

CHEAPEST_INSERTION

Finds cheapest entity-value combination

Cost-minimization problems

ALLOCATE_ENTITY_FROM_QUEUE

Generic queue-based allocation

Custom entity ordering

ALLOCATE_TO_VALUE_FROM_QUEUE

Round-robin value assignment

Value-constrained problems

ALLOCATE_FROM_POOL

Pool-based best-fit allocation

Complex multi-criteria problems

Algorithm	Description	Best For
TABU_SEARCH	Prevents revisiting recent solutions	Default - Most problems - good balance
HILL_CLIMBING	Accepts only improving moves	Simple problems, fast convergence
SIMULATED_ANNEALING	Probabilistic acceptance of worse moves	Escaping local optima
LATE_ACCEPTANCE	Accepts moves based on historical threshold	Large problems, good exploration
DIVERSIFIED_LATE_ACCEPTANCE	Enhanced late acceptance with diversification	Complex problems needing exploration
GREAT_DELUGE	Accepts moves above a decreasing threshold	Problems with many local optima
VARIABLE_NEIGHBORHOOD_DESCENT	Switches between neighborhood types	Problems with multiple move types

Algorithm

Description

Best For

TABU_SEARCH

Prevents revisiting recent solutions

Default - Most problems - good balance

HILL_CLIMBING

Accepts only improving moves

Simple problems, fast convergence

SIMULATED_ANNEALING

Probabilistic acceptance of worse moves

Escaping local optima

LATE_ACCEPTANCE

Accepts moves based on historical threshold

Large problems, good exploration

DIVERSIFIED_LATE_ACCEPTANCE

Enhanced late acceptance with diversification

Complex problems needing exploration

GREAT_DELUGE

Accepts moves above a decreasing threshold

Problems with many local optima

VARIABLE_NEIGHBORHOOD_DESCENT

Switches between neighborhood types

Problems with multiple move types

Planning Wizard

Not set

0

0

0

120

Latest JSON Preview AI Improved

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Assignment Summary

Raw Solution JSON

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Insights JSON

Step 1: Define Your Problem

Step 2: What Needs Scheduling?

Step 3: Who or What Can Do It?

Step 4: What Are Your Rules?

Step 5: Any Pre-Decisions?

Step 6: How Long Should We Search?

Step 7: Review & Validate JSON

Message

Confirm

Construction Heuristic Algorithms

Local Search Algorithms