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Get started with Timefold quickstarts here. Optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling and other planning problems.

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TimefoldAI/timefold-quickstarts

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Timefold Solver

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This repository contains quickstarts for Timefold Solver, an AI constraint solver for Java, Python, and Kotlin. It shows different use cases and basic implementations to get you started on your PlanningAI journey.

Overview

Use Case Notable Solver Concepts
🚚 Vehicle Routing Chained Through Time, Shadow Variables
🧑‍💼 Employee Scheduling Load Balancing
🛠️ Maintenance Scheduling TimeGrain, Shadow Variable, Variable Listener
📦 Food Packaging Chained Through Time, Shadow Variables, Pinning
🛒 Order Picking Chained Planning Variable, Shadow Variables
🏫 School Timetabling Timeslot
🏭 Facility Location Problem Shadow Variable
🎤 Conference Scheduling Timeslot, Justifications
🛏️ Bed Allocation Scheduling Allows Unassigned
🛫 Flight Crew Scheduling
👥 Meeting Scheduling TimeGrain
Task Assigning Bendable Score, Chained Through Time, Allows Unassigned
📆 Project Job Scheduling Shadow Variables, Variable Listener, Strenght Comparator
🏆 Sports League Scheduling Consecutive Sequences
🏅 Tournament Scheduling Pinning, Load Balancing

Note

The implementations in this repository serve as a starting point and/or inspiration when creating your own application. Timefold Solver is a library and does not include a UI. To illustrate these use cases a rudimentary UI is included in these quickstarts.

Use cases

🚚 Vehicle Routing

Find the most efficient routes for vehicles to reach visits, considering vehicle capacity and time windows when visits are available. Sometimes also called "CVRPTW".

Vehicle Routing Screenshot

Tip

Check out our off-the-shelf model for Field Service Routing. This model goes beyond basic Vehicle Routing and supports additional constraints such as priorities, skills, fairness and more.


🧑‍💼 Employee Scheduling

Schedule shifts to employees, accounting for employee availability and shift skill requirements.

Employee Scheduling Screenshot

Tip

Check out our off-the-shelf model for Employee Shift Scheduling. This model supports many additional constraints such as skills, pairing employees, fairness and more.


🛠️ Maintenance Scheduling

Schedule maintenance jobs to crews over time to reduce both premature and overdue maintenance.

Maintenance Scheduling Screenshot


📦 Food Packaging

Schedule food packaging orders to manufacturing lines to minimize downtime and fulfill all orders on time.

Food Packaging Screenshot


🛒 Order Picking

Generate an optimal picking plan for completing a set of orders.

Order Picking Screenshot


🏫 School Timetabling

Assign lessons to timeslots and rooms to produce a better schedule for teachers and students.

School Timetabling Screenshot

Without a UI:


🏭 Facility Location Problem

Pick the best geographical locations for new stores, distribution centers, COVID test centers, or telecom masts.

Facility Location Screenshot


🎤 Conference Scheduling

Assign conference talks to timeslots and rooms to produce a better schedule for speakers.

Conference Scheduling Screenshot


🛏️ Bed Allocation Scheduling

Assign beds to patient stays to produce a better schedule for hospitals.

Bed Scheduling Screenshot


🛫 Flight Crew Scheduling

Assign crew to flights to produce a better schedule for flight assignments.

Flight Crew Scheduling Screenshot


👥 Meeting Scheduling

Assign timeslots and rooms for meetings to produce a better schedule.

Meeting Scheduling Screenshot


✅ Task Assigning

Assign employees to tasks to produce a better plan for task assignments.

Task Assigning Screenshot


📆 Project Job Scheduling

Assign jobs for execution to produce a better schedule for project job allocations.

Project Job Scheduling Screenshot


🏆 Sports League Scheduling

Assign rounds to matches to produce a better schedule for league matches.

Sports League Scheduling Screenshot


🏅 Tournament Scheduling

Tournament Scheduling service assigning teams to tournament matches.

Tournament Scheduling Screenshot


Legal notice

Timefold Quickstarts was forked on 20 April 2023 from OptaPlanner Quickstarts, which was entirely Apache-2.0 licensed (a permissive license).

Timefold Quickstarts is a derivative work of OptaPlanner Quickstarts, which includes copyrights of the original creator, Red Hat Inc., affiliates, and contributors, that were all entirely licensed under the Apache-2.0 license. Every source file has been modified.

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