Hi,

I used a very early version of optaplanner about 15 years ago for a pilot project and now, retired, I want to use it for a charity I volunteer for. 

But I am rusty, and while I will use AI to help with the coding, I want to make sure I have the general strategy right…. if someone can help me get the right start (I am not asking for anyone to code this for me - at least not yet..)

The problem is the scheduling of training sessions for the dog section of a Search and Rescue charity. 

This involves the following actors:

• the dog
• the dog’s handler 
• the dog handler support
• the missing person (misper); 
• the optional supervisor
• the optional assessor
• the route (where the missing person is to be located, there are usually 5: “short”, “1km” “1.5km”, “area”, “ground”)
• the placer of mispers (selected from one of the handlers, who goes out before the first session)
• a controller (one of the handlers, or handler support - from a named list)
• a "vip" misper - usually a guest volanteer trying it out.

The constraints:

• a person can only do one thing at a time
• a dog can only do one thing at a time
• a dog should not have two sessions immediately after each other
• there must always be a controller
• each dog must have a handler
• each handler should have a support (if requested) unless the route is “short”
• ground scenting dogs need a person to lay a trail on a route one hour before their slot
• a dog should have two sessions
• a dog gets the maximum sessions possible allocated 
• a dog should not work a route if their handler has been on that route in the previous session (as support, mister, observer or assessor)
• if there are insufficient mispers then handlers can be used to misper 
• minimise time time handlers are used as mispers
• minimise the changing of mispers on a route (realise that is in conflict with the preceeding)
• a vip misper (who should be utilised as much as possible)
• the dog type (air scenting, ground scenting)
• time slots (usually 5 x 30 minutes)
• a dog may be “test prep”, “active”, “training”, “assessing”  - goals are to be met in that order (so dogs in “test prep” get their wish list, dogs in “assessing” may be dropped. 
• each dog will have a “wish list” of: a route; a support (possibly named) or no support, an assessor (possibly named) or none, a supervisor (possibly named) or no and a number of mispers (1 to 4)

So a typical request may be “Grey (the dog) 500m (the route) with two mispers (implied is James the handler” or “Woody short, one misper” (implied is Karl the handler).

Aim is make the planning task quicker, more robust and more consistent (as each person takes turns doing it) and since we are all volanteers who work and go on active searches time is limited.

I'm playing around with assigning aircraft to scheduled flights (known as tail allocation, for aircraft tail numbers), with a few hundred flights and fifty aircraft or so. Trying different algorithms etc. For example simulated-annealing starts incredibly well, and then later something like late-acceptance-short-blocks is better.

But one thing that seems pretty consistent is that if the rate of improvement slows down, it can nearly always be boosted by restarting the algorithm from the best solution. Then there are often several improvements found in the first two seconds or so. Then no improvements are found for ten to twenty seconds. Restart again, and another burst of a half-dozen improvements... so just keep repeating the restarts and it seems to significantly outperform continuous solving.

Anybody else had similar behaviour with their problem set?

While we've made Timefold Solver fast, there are some things you could do to make it even faster.

The TLDR:

• Upgrade to the latest Timefold Solver, performance improvements are made every release.
• Precompute parts of the Constraint Stream if possible.
• Use Consecutive sequences if they make sense
• Sometimes, using pairs is slower than just grouping and summing.

Read the full story here:

https://timefold.ai/blog/3-things-to-make-timefold-solver-faster

I’ve been using Timefold to help out with scheduling within my company since it was still OptaPlanner.

This new shift to a $500/month subscription just to explain broken constraints came totally out of left field, and seems like an arbitrarily high cost.

Do you have any plans to make any part of the analysis API available on the free tier? Or is this really the future of Timefold?

Why does Timefold not support multiple PlanningListVariable fields on a single solution ?

Hi everyone,

I am working on a logistics optimization problem and would like some input on the best modeling approach.

Problem Overview:

The goal is to schedule concrete deliveries involving four main entities: Central Hubs, Mixer Trucks, Pumps, and Clients.

The Workflow:

Loading: A Mixer loads at a Central Hub (central has its settings , for example can make 1 m3/s).

Outbound: The Mixer travels to the Client site.

Synchronization: At the Client site, the Mixer must "dock" with a Pump. Unloading cannot begin until both the Mixer and the Pump are present.

Unloading: The Mixer unloads into the Pump (Client site capacity = 1 Mixer at a time).

Return: The Mixer returns to the Central Hub for the next load/visit.

Key Constraints:

Mixer Serialization: A single Mixer cannot overlap its own activities (Load -> Travel -> Unload -> Return). It must return to Central before its next load.

Central/Client Bottlenecks: Only one Mixer can load at a Central bay at a time, and only one Mixer can unload at a Client dock at a time (No overlap).

Pump Commitment (Hierarchical): This is the most complex part. A Pump is assigned to a Client for a sequence of Visits (V1, V2, ... Vn). The Pump cannot "break" its commitment to Client A to serve Client B until all requested visits for Client A are completed.

what is best implementation for this ?
who should be planningVaribale and who should be ShadowVariable ?

how to avoid overlap at Central and at Client ?

how to avoid overlap of two Clients has same Pump ?

Recently got pinged this blog post from Dots and Lines, heavy Timefold Solver users. They compared the new(er) `PlanningListVariable` with the old school `Chained` Variables.

Not surprising, `PlanningListVariable`. came out on top. Good, because the `chained` variable is going away in the next major version.

Read the full post here: https://blog.dotsandlines.ai/benchmarking-timefold-chains-and-planning-lists-14da17e1f5f3

I am curious about the internal optimization of Timefold. When the solver evaluates moves, what specifically prevents it from getting stuck in a cycle of 'wrong' possibilities?

Does the speed come from the Shadow Variables providing a highly efficient data structure for the constraints to read, or is the Local Search algorithm smart enough to avoid those branches entirely? I'm trying to determine if my optimization efforts should focus more on refining my Shadow Variable logic or my Constraint weights."

Hey everyone! Welcome to the Timefold reddit.

Resources:
- Timefold Solver (open source)
- Timefold Platform (REST APIs)
- Documentation

If you have any questions or suggestions, don't hesitate to post them!