Planning Model Wizard

Review the most recently saved planning problem before submitting it to Timefold Solver.

Loading planning problem…

Submitting will send this payload to the solver and may take a few minutes to complete.

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Entity List

ID *	Name/Description	Duration	Priority	Required Skills	Deadline	Other Properties	Actions

Entity Dependencies (Optional)

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Value List

ID *	Name	Skills	Capacity *	Hourly Rate	Availability	Other Properties	Actions

Capacity Limits Description (Optional)

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Hard Constraints

Name *	Rule Type *	Weight (1-10) *	Description	Actions

Medium Constraints

Name *	Rule Type *	Weight (1-10) *	Description	Actions

Medium constraints help manage plans when resources are limited. They incentivize assigning as many entities (tasks, visits, shifts) as possible.

Soft Constraints

Name *	Rule Type *	Weight (1-10) *	Description	Actions

Note: For skill matching constraints to work, your Timefold backend solver must implement the skill matching logic. The constraint will be sent to the API, but the backend needs to enforce it by checking if assigned resources have the required skills for each task.

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Number of Entities Calculated from Step 2

Number of Values Calculated from Step 3

Problem Size Category

Expected Solving Time

Timeout Setting (seconds) * Recommended timeout based on problem size

Construction Heuristic Algorithm Algorithm used to build initial solution

Local Search Algorithm Algorithm used to improve the initial solution

Algorithm Selection: If you're unsure, use the defaults. They work well for most problems. Advanced users can experiment with different algorithms for better performance on specific problem types.

The construction heuristic phase builds an initial solution. Available options:

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

The local search phase improves the initial solution. Available options:

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

Describe Your Planning Problem

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Latest JSON Preview

Solver Output

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Assignment Summary

Raw Solution JSON

Operational Intelligence

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Insights JSON

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Assignment Summary

Raw Solution JSON

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Insights JSON

Step 1: Define Your Problem

Step 2: List Your Entities

Entity List

Step 3: List Your Values

Value List

Step 4: Define Your Constraints

Hard Constraints

Medium Constraints

Soft Constraints

Step 5: Define Your Objectives

Step 6: Estimate Problem Size

Step 7: Map Your Data to JSON Structure

Step 8: Complete JSON Template

Message

Confirm

Construction Heuristic Algorithms

Local Search Algorithms