Geoffrey De Smet
@geoffreydesmet.bsky.social
PlanningAI expert, Timefold co-founder, OptaPlanner creator, complex scheduling and routing, Operations Research, Java, Kotlin, open source, international speaker
The Sunk Cost Fallacy
After you've worked weeks on an improvement that turns out as not much of an improvement...
... this Pull Request appears ...
Mario Fusco @ #devoxx
After you've worked weeks on an improvement that turns out as not much of an improvement...
... this Pull Request appears ...
Mario Fusco @ #devoxx
October 12, 2025 at 10:24 AM
The Sunk Cost Fallacy
After you've worked weeks on an improvement that turns out as not much of an improvement...
... this Pull Request appears ...
Mario Fusco @ #devoxx
After you've worked weeks on an improvement that turns out as not much of an improvement...
... this Pull Request appears ...
Mario Fusco @ #devoxx
"Why haven't they fixed this yet?" @smarks.bsky.social and Maurice talking about ConcurrentException etc in #java at #devoxx
Great talk, but when they cover Vector and Hashtable, I can't stop and think:
"Why haven't they deprecated this yet?"
Great talk, but when they cover Vector and Hashtable, I can't stop and think:
"Why haven't they deprecated this yet?"
October 6, 2025 at 11:59 AM
"Why haven't they fixed this yet?" @smarks.bsky.social and Maurice talking about ConcurrentException etc in #java at #devoxx
Great talk, but when they cover Vector and Hashtable, I can't stop and think:
"Why haven't they deprecated this yet?"
Great talk, but when they cover Vector and Hashtable, I can't stop and think:
"Why haven't they deprecated this yet?"
An Open Source project needs a real-time chat for the community to come together.
So we created a Discord chat for Timefold Solerv.
To ask questions.
To discuss ideas.
Or to figure out how to hack the solver to run your crazy experiment.
Join us:
discord.gg/976RcEVVHW
So we created a Discord chat for Timefold Solerv.
To ask questions.
To discuss ideas.
Or to figure out how to hack the solver to run your crazy experiment.
Join us:
discord.gg/976RcEVVHW
October 3, 2025 at 11:52 AM
An Open Source project needs a real-time chat for the community to come together.
So we created a Discord chat for Timefold Solerv.
To ask questions.
To discuss ideas.
Or to figure out how to hack the solver to run your crazy experiment.
Join us:
discord.gg/976RcEVVHW
So we created a Discord chat for Timefold Solerv.
To ask questions.
To discuss ideas.
Or to figure out how to hack the solver to run your crazy experiment.
Join us:
discord.gg/976RcEVVHW
If it takes 10 000 hours to master a craft,
what do you get for 10 000 commits?
Timefold Solver, our Open Source solver for complex scheduling and routing problems, just reached 10 000 commits on GitHub:
github.com/TimefoldAI/t...
what do you get for 10 000 commits?
Timefold Solver, our Open Source solver for complex scheduling and routing problems, just reached 10 000 commits on GitHub:
github.com/TimefoldAI/t...
August 25, 2025 at 8:13 AM
If it takes 10 000 hours to master a craft,
what do you get for 10 000 commits?
Timefold Solver, our Open Source solver for complex scheduling and routing problems, just reached 10 000 commits on GitHub:
github.com/TimefoldAI/t...
what do you get for 10 000 commits?
Timefold Solver, our Open Source solver for complex scheduling and routing problems, just reached 10 000 commits on GitHub:
github.com/TimefoldAI/t...
Can the optimal solution for a Traveling Salesman Problem (TSP) have crossing paths?
No, it cannot.
Because of the Triangle Inequality principle.
But in reality, it can.
Because there's road infrastructure: highways, one-way streets, etc.
In the real-world, solution optimality is rarely obvious.
No, it cannot.
Because of the Triangle Inequality principle.
But in reality, it can.
Because there's road infrastructure: highways, one-way streets, etc.
In the real-world, solution optimality is rarely obvious.
August 1, 2025 at 8:27 AM
Can the optimal solution for a Traveling Salesman Problem (TSP) have crossing paths?
No, it cannot.
Because of the Triangle Inequality principle.
But in reality, it can.
Because there's road infrastructure: highways, one-way streets, etc.
In the real-world, solution optimality is rarely obvious.
No, it cannot.
Because of the Triangle Inequality principle.
But in reality, it can.
Because there's road infrastructure: highways, one-way streets, etc.
In the real-world, solution optimality is rarely obvious.
That moment when your backend algorithms optimize a vehicle routing dataset with thousands of visits...
... and your frontend turns into a piece of modern art.
... and your frontend turns into a piece of modern art.
June 21, 2025 at 1:44 PM
That moment when your backend algorithms optimize a vehicle routing dataset with thousands of visits...
... and your frontend turns into a piece of modern art.
... and your frontend turns into a piece of modern art.
This is what happens if you use floating point numbers (int, double in #Java) for financial data.
Use decimal numbers (BigDecimal in Java) instead.
Use decimal numbers (BigDecimal in Java) instead.
June 15, 2025 at 10:25 AM
This is what happens if you use floating point numbers (int, double in #Java) for financial data.
Use decimal numbers (BigDecimal in Java) instead.
Use decimal numbers (BigDecimal in Java) instead.
Where does your enterprise stand on the scheduling maturity ladder?
Are your resources scheduled manually?
With or without constraint verification?
Or automatically?
With or without optimization?
Discover the 4 Levels of Scheduling:
timefold.ai/blog/the-4-l...
Are your resources scheduled manually?
With or without constraint verification?
Or automatically?
With or without optimization?
Discover the 4 Levels of Scheduling:
timefold.ai/blog/the-4-l...
June 6, 2025 at 6:46 AM
Where does your enterprise stand on the scheduling maturity ladder?
Are your resources scheduled manually?
With or without constraint verification?
Or automatically?
With or without optimization?
Discover the 4 Levels of Scheduling:
timefold.ai/blog/the-4-l...
Are your resources scheduled manually?
With or without constraint verification?
Or automatically?
With or without optimization?
Discover the 4 Levels of Scheduling:
timefold.ai/blog/the-4-l...
Most publications of the Vehicle Routing Problem only cover the tip of the iceberg.
Benchmarks and academic papers typically only handle vehicle capacity (CVRP) and time windows (VRPTW)
But production deployments also need to deal with many other requirements.
#OperationsResearch #PlanningAI #AI
Benchmarks and academic papers typically only handle vehicle capacity (CVRP) and time windows (VRPTW)
But production deployments also need to deal with many other requirements.
#OperationsResearch #PlanningAI #AI
April 21, 2025 at 11:36 AM
Most publications of the Vehicle Routing Problem only cover the tip of the iceberg.
Benchmarks and academic papers typically only handle vehicle capacity (CVRP) and time windows (VRPTW)
But production deployments also need to deal with many other requirements.
#OperationsResearch #PlanningAI #AI
Benchmarks and academic papers typically only handle vehicle capacity (CVRP) and time windows (VRPTW)
But production deployments also need to deal with many other requirements.
#OperationsResearch #PlanningAI #AI
Our Open Source solver now has a dedicated home: solver.timefold.ai
It's a place for the Timefold Solver community,
with everything you need to build a solution
for any planning, scheduling or routing optimization problem.
Is there anything missing that you would like to see on solver.timefold.ai?
It's a place for the Timefold Solver community,
with everything you need to build a solution
for any planning, scheduling or routing optimization problem.
Is there anything missing that you would like to see on solver.timefold.ai?
April 10, 2025 at 9:35 AM
Our Open Source solver now has a dedicated home: solver.timefold.ai
It's a place for the Timefold Solver community,
with everything you need to build a solution
for any planning, scheduling or routing optimization problem.
Is there anything missing that you would like to see on solver.timefold.ai?
It's a place for the Timefold Solver community,
with everything you need to build a solution
for any planning, scheduling or routing optimization problem.
Is there anything missing that you would like to see on solver.timefold.ai?
The quickstarts readme for our open source solver had a make-over!
It's now easier to find the right example and start solving your scheduling or routing problem with #PlanningAI.
Try it out:
github.com/TimefoldAI/t...
It's now easier to find the right example and start solving your scheduling or routing problem with #PlanningAI.
Try it out:
github.com/TimefoldAI/t...
March 25, 2025 at 3:26 PM
The quickstarts readme for our open source solver had a make-over!
It's now easier to find the right example and start solving your scheduling or routing problem with #PlanningAI.
Try it out:
github.com/TimefoldAI/t...
It's now easier to find the right example and start solving your scheduling or routing problem with #PlanningAI.
Try it out:
github.com/TimefoldAI/t...
The new overview of our REST API for Field Service Routing is online.
Take a look:
app.timefold.ai/models/field...
It covers almost every constraint.
From time windows, fairness and technician costs to real-time planning and many other #PlanningAI requirements.
Just pick and choose what you need.
Take a look:
app.timefold.ai/models/field...
It covers almost every constraint.
From time windows, fairness and technician costs to real-time planning and many other #PlanningAI requirements.
Just pick and choose what you need.
January 29, 2025 at 1:17 PM
The new overview of our REST API for Field Service Routing is online.
Take a look:
app.timefold.ai/models/field...
It covers almost every constraint.
From time windows, fairness and technician costs to real-time planning and many other #PlanningAI requirements.
Just pick and choose what you need.
Take a look:
app.timefold.ai/models/field...
It covers almost every constraint.
From time windows, fairness and technician costs to real-time planning and many other #PlanningAI requirements.
Just pick and choose what you need.
Sorry...
But it always works!
But it always works!
December 22, 2024 at 10:56 AM
Sorry...
But it always works!
But it always works!
Generative #AI versus #PlanningAI.
Use the right tool for the job.
For field service routing, shift scheduling, last mile routing, machine job scheduling, maintenance scheduling, ... take a look at our open source solver in #java, #python or #kotlin:
timefold.ai/open-source-...
#GenAI
Use the right tool for the job.
For field service routing, shift scheduling, last mile routing, machine job scheduling, maintenance scheduling, ... take a look at our open source solver in #java, #python or #kotlin:
timefold.ai/open-source-...
#GenAI
November 10, 2024 at 8:29 AM
Generative #AI versus #PlanningAI.
Use the right tool for the job.
For field service routing, shift scheduling, last mile routing, machine job scheduling, maintenance scheduling, ... take a look at our open source solver in #java, #python or #kotlin:
timefold.ai/open-source-...
#GenAI
Use the right tool for the job.
For field service routing, shift scheduling, last mile routing, machine job scheduling, maintenance scheduling, ... take a look at our open source solver in #java, #python or #kotlin:
timefold.ai/open-source-...
#GenAI
New Timefold docs website:
Same high-quality PlanningAI content, with a more user-friendly design!
Give it a try:
docs.timefold.ai
What do you think?
What documentation do you want us to improve or expand?
Same high-quality PlanningAI content, with a more user-friendly design!
Give it a try:
docs.timefold.ai
What do you think?
What documentation do you want us to improve or expand?
November 7, 2024 at 11:28 AM
New Timefold docs website:
Same high-quality PlanningAI content, with a more user-friendly design!
Give it a try:
docs.timefold.ai
What do you think?
What documentation do you want us to improve or expand?
Same high-quality PlanningAI content, with a more user-friendly design!
Give it a try:
docs.timefold.ai
What do you think?
What documentation do you want us to improve or expand?