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Optimization Solver as a Service

quicopt.com

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Re: Optimization Solver as a Service

#21

Post by akoboldfrying »

DannyBee wrote: post #13

NEOS will let you run this stuff on cplex/gurobi/etc (IE much faster than the backends behind quicopt), for free, is integrated with pyomo/etc, and has like an 8 hour time limit. Often, the difference on "harder" problems is 10x or more. I have problems that gurobi solves in 30 seconds that take 15 minutes or more for ~every non-commercial solver (or-tools, HIGHS, ipopt, etc). But right now, this wouldn't even be int…

> But right now, this wouldn't even be interesting to me to use even if they actually were fronting commercial solvers, because they can't actually run it any faster

So you use NEOS, but another service offering the same thing as NEOS would not be useful?

Re: Optimization Solver as a Service

#22

Post by uoaei »

cchianel wrote: post #12
uoaei wrote: post #10

Earlier quoted context omitted.

No free lunch theorem has nothing to say about approximate solutions, so I'm really not sure what you're going on about. OR-tools is almost exclusively linear programming which according to its strict assumptions converges more or less trivially, assuming a correctly composed program. Which means if you're paying for it "as a service" you all but deserve to lose that money. > Different algorithms are better for diffe…

Look at it this way: I am arguing against "No Free Lunch theorem says an optimization algorithm cannot solve all problems because for some problems it performs worse than other algorithms"; I am arguing approximate solutions are good enough, and in practice a wide variety of optimization algorithms find good enough solutions despite being worse than others algorithms for the problem class. Moreover, some algorithms/s…

Approximate solutions are good enough, but approximate solutions are not part of the solution space for the kinds of problems covered by OP.

Re: Optimization Solver as a Service

#23

Post by ge0ffrey »

TrueDuality wrote: post #6

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…

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.

Re: Optimization Solver as a Service

#24

Post by ge0ffrey »

greatony wrote: post #4

looks interesting, how large problem can it solve?

Our version of a Solver as a Service deals with cases of up to 390'000 shifts in a single dataset for shift scheduling and 30'000 visits for vehicle routing problems.

Some our customers want to go even higher, and we're working on that.

Re: Optimization Solver as a Service

#25

Post by throwaway832040 »

ge0ffrey wrote: post #23
TrueDuality wrote: post #6

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…

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,…

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.

Re: Optimization Solver as a Service

#26

Post by ge0ffrey »

throwaway832040 wrote: post #25
ge0ffrey wrote: post #23

Earlier quoted context omitted.

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,…

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-r…

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.

Re: Optimization Solver as a Service

#27

Post by genxy »

DannyBee wrote: post #13

NEOS will let you run this stuff on cplex/gurobi/etc (IE much faster than the backends behind quicopt), for free, is integrated with pyomo/etc, and has like an 8 hour time limit. Often, the difference on "harder" problems is 10x or more. I have problems that gurobi solves in 30 seconds that take 15 minutes or more for ~every non-commercial solver (or-tools, HIGHS, ipopt, etc). But right now, this wouldn't even be int…

I worked at a place that basically never bought software, and they used gurobi for scheduling. It is apparently best in class for lots of problems.

Re: Optimization Solver as a Service

#28

Post by ge0ffrey »

ge0ffrey wrote: post #26
throwaway832040 wrote: post #25

Earlier quoted context omitted.

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-r…

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 ge…

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.

Re: Optimization Solver as a Service

#30

Post by raverbashing »

DannyBee wrote: post #13

NEOS will let you run this stuff on cplex/gurobi/etc (IE much faster than the backends behind quicopt), for free, is integrated with pyomo/etc, and has like an 8 hour time limit. Often, the difference on "harder" problems is 10x or more. I have problems that gurobi solves in 30 seconds that take 15 minutes or more for ~every non-commercial solver (or-tools, HIGHS, ipopt, etc). But right now, this wouldn't even be int…

Gurobi probably has good heuristics and gives you a good enough answer instead of gnawing at a bone like every other MIP solver
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