[TrueDuality]

Really not trying to be cheeky... but why? Who is the audience here? I can see maybe academics with small grants and want to do the absolute minimum spend on compute... But that is an audience you will have to fight for every cent.

This doesn't solve or provide guidance for the subtle problems in these otherwise opensource solvers... The first example requires the client to manually disambiguate equivalent variables to get a stable solution... Sure that's a pretty common problem everyone working with optimizers should be familiar with but they're also one of the hardest things to track down in a complex derived model.

[6r17]

Hei i'd argue the opposite ; the target you named are actually able to formalize this and spend more time on this because they have the mathematical background - it is not the case for many amateur programmer who would now be exposed to such problematic with a tool that can give them somewhat of an insight - being exposed to the tool it-self alone is huge because it allows an operator to experience and learn - this is all of course almost hyperbolic, reality is that most people won't be doing that - but it allows it, and it's cool !

[ge0ffrey]

There is an audience for such platforms - Timefold Platform optimizes 1,000,000 visits and 2,000,000 shifts per week - but only if it's more than just orchestration.

If it handles explainabily, what-if scenarios and insights to fulfill business needs.

And that's where supporting many solvers becomes the blocker.

A lowest common denominator design.

Those solvers are a black box. They don't expose what they're running, why they made certain decisions or how they can scale to large datasets or complex business requirements.

We've picked our poison: one solver, which we've built in the open, in the last 20 years, versatile enough to handle any scheduling problem. That delivers.

[throwaway832040]

None of these solvers genuinely focuses on the quality of the features that matter in real-world operations.

Many of them, including Timefold, lack a realistic, financially grounded model of the world. They do not adequately account for traffic, driver preferences, or other factors that require a continuous feedback loop between what actually happened in practice and what the optimizer expected to happen.

A vehicle-routing problem without real-world feedback is little more than a gimmick. Even assuming the world could be modelled perfectly, what happens when an unpredictable event disrupts the plan? Is the supposedly “globally optimal” solution robust enough to adapt, or will it create a backlog that forces the business to hire additional workers because the system failed to build in sufficient redundancy?

Using MILP makes the system even less flexible.

[ge0ffrey]

I beg to differ. Timefold is used daily in real-world operations.

Neither our clusters in the US or EU can afford to go down for a minute, or business operations in logistics, retail and healthcare are impacted.

> They do not adequately account for traffic, driver preferences, or other factors that require a continuous feedback

- Traffic: supported

- Driver preferences: the APIs support

-- Area affinity (soft) and geo fencing (hard)

-- Fairness and load balancing of work (soft)

-- Overtime (soft and hard)

-- Seniors not doing boring junior tasks (soft)

- Continuous feedback: the APIs support

-- Real-time rescheduling (warm starts) as actual execution data comes in.

-- Recommend assignment: scheduling in survival mode

-- Pinning: user stays in control through overriding assignments

-- Explainability: why a certain decision was made

-- Insights: what are the bottlenecks in my schedule - what type of employees should I hire or upskill.

[ge0ffrey]

That being said: I fully agree that the solver industry as a whole has focused for far too long on global optima for academic requirements, instead of real-world use for the actual business requirements, and how to deal with business objective changes each quarter.

[tirutiru]

I work on vehicle routing for a living. Our routing engine is mostly original heuristics, no MIP.

The thrust of your remarks is correct IMO. We are forced to support a large number of granular constraints, too large for my comfort.

But no solver will deal with large 'global events'. That's just not its job. Different levels of decision making altogether.

My email is in my profile. Would be happy to hear more.