10 ms·

Optimization Solver as a Service

51 points by paddi91 2mo ago

paddi91 2mo ago
Keep it simple, just one call to solve every model.
- uoaei 2mo ago
  *sigh* We really need to teach this new crop the term "no free lunch". Again.
  - cchianel 2mo ago
    I personally disagree with "no free lunch"; (for the uninitiated, "no free lunch" refer to the fact for any deterministic algorithm, there exist a problem that will force the algorithm to go through the entire solution space to find the optimal solution, with every single other possible algorithm beating it (https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem)). For many planning problems, finding a good enough solution is sufficient, and there are many optimization algorithms that work for a wide variety of problems and provide a good enough solution in reasonable time. Different algorithms are better for different problems (ex: Metaheuristic (ex: Late Acceptance) Solvers beats MIP Solvers on vehicle routing, whereas MIP Solvers beat Metaheuristic Solvers on Employee Scheduling and Bin Packing. But both Metaheuristic and MIP Solvers provider good enough solutions for both vehicle routing and bin packing.
    - uoaei 2mo ago
      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 different problems So... why does your rhetorical style have such oppositional tone if you're just going to reaffirm the no free lunch theorem?
      - cchianel 2mo ago
        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/solvers can be configured, which fundamentally change the direction the solving takes (for example, a custom phase that uses your domain knowledge of the particular problem to get a good enough initial solution to be improved upon) (Side note: I am NOT affiliated with this post/project; from the website I don't really see a value add for it, especially since the site is lacking so many details).
        uoaei 2mo ago
        Approximate solutions are good enough, but approximate solutions are not part of the solution space for the kinds of problems covered by OP.
greatony 2mo ago
looks interesting, how large problem can it solve?
- cchianel 2mo ago
  It seems to just be a wrapper over or-tools and other solvers from their landing page, with the difference being it run on their servers versus your hardware. Their website does not mention what hardware is allocated per model (which determine speed of solving) nor any limit on model size.
- ge0ffrey 2mo ago
  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.
TrueDuality 2mo ago
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.
- ge0ffrey 2mo ago
  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 2mo ago
    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.
quantum_state 2mo ago
This may be useful for small demos. For large scale MIP with millions of variables, one needs to have the solver at hand to support custom algos with techniques such as column generation, etc. to achieve time to solution and economics of compute resources. A remote API will not fit.
shoo 2mo ago
I'm not a potential customer for this, but i have worked on a few commercial projects involving combinatorial optimisation. Misc thoughts: - I'm not familiar with the LABS problem, but the LABS benchmark page is interesting & compares against Gurobi. I'd be curious to see how an existing commercial non-mip approximate solver such as Hexaly (formerly LocalSolver) compares here. - the other two benchmarks aren't very convincing as they don't compare against other methods or show running times - the front page mentions peer reviewed methodology - consider linking to the publications - good idea to have case studies of applications. I was a bit confused to see this listed under 'References' but on comparison the Gurobi & Hexaly marketing websites also do this (references -> case studies & references -> customer stories, respectively) - re the client API, you may want to make the server URL have a default, so your trial users / customers don't have to specify it. It may be easier for you to roll out changes to your server URL in future if you can do it by changing the default server URL in a new version of your client library rather than requiring your customers to update their source code. All the best!
- LPisGood 2mo ago
  Does anyone use Hexaly for any serious work? If so, why? There seem to be many better alternatives out there.
  - luiwammus 2mo ago
    Amazon has been quite vocal about using Hexaly.
    - whatever1 2mo ago
      All vendors just need 1 engineer in a BigCorp to have used their software to claim "Trusted by BigCorpName"
      - LPisGood 2mo ago
        This is essentially my understanding of the situation. The OR applications are extremely diverse there; they use most wvery solver and technique under the sun for something.
DannyBee 2mo ago
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 interesting to me to use even if they actually were fronting commercial solvers, because they can't actually run it any faster and having this ".solve" API does nothing - pyomo already does that for me in practice.
- akoboldfrying 2mo ago
  > 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?
  - DannyBee 2mo ago
    Not if i have to pay for it and use a different API? Remember, NEOS is both free, and pyomo already supports it natively without me changing anything - i can use both neos and local without any issue. Why would i move to something with a different API that i have to pay for?
    - akoboldfrying 2mo ago
      > Not if i have to pay for it and use a different API? TFA says Quicopt supports Pyomo. The example uses the "free tier", so you're not paying for anything (yet -- though I'm confused by the phrase "one-time entry point"). NEOS has generous limits, but the fact that Gurobi are still in business tells me that NEOS by itself can't satisfy everyone. Were Quicopt to offer access to Gurobi, etc., per your hypothetical, I think this would clearly be valuable. More strongly I don't think it's crazy to offer "just" publicly available solvers, despite how much weaker they are than commercial ones. If I had to solve a stream of optimisation problems that were individually pretty easy but the rate of arrival was unpredictable, using such an "Optimisation as a Service" would make sense in much the same way that it makes more sense to serve spiky web traffic by spinning up cloud VMs on demand than by buying a bunch of on-prem boxes, even though those cloud VMs might be very weak.
whatever1 2mo ago
We do have standard benchmarks in the field. Hans Mittelman maintains a library. No idea why they did not bother to run them. https://plato.asu.edu/guide.html https://plato.asu.edu/guide.html Their website has just 3 cherry picked instances and claim complete dominance.
- ge0ffrey 2mo ago
  We run the Mittelman VRPLib benchmarks at Timefold (and beat other open source solvers like or-tools in 95%+ of the X datasets). But they are not representive of the real world, at all. The Mittelman VRPLib benchmarks have only 1-2 constraints. Skills? No need. Working hours? Unlimited. Maps integretion? Cars can fly and the earth is a flat Euclidean space. Any VRP algorithm optimized for the vrplib datasets is overfitted and not the best one in reality. Take HGS for instance. Brilliant for CVRPTW. Crumbles to dust in field service routing for telco operations etc.
  - whatever1 2mo ago
    That is fair, which is why you should not over-index on academic benchmarks. But a frontier solver should be within a small margin from the competitors in these benchmarks. If you are 10x slower, there is no way you can catch up (at least not without a ton of work from the practitioner side).
deanalyzer 2mo ago
This could be interesting, but it badly needs systematic benchmarking results. It is not difficult to get Claude Code or Codex to install and run a solver locally, so the tool’s current value proposition is fairly muddled. If there were evidence that it offered better performance, I might consider running larger workloads on it.
- akoboldfrying 2mo ago
  As I understand it, the value proposition is that it can deal with a spiky stream of problems to solve better than buying a bunch of on-prem hardware and running local solvers on them would -- similar to why it often makes sense to spin up cloud VMs on demand to handle spiky web traffic. Performance on an individual problem is still interesting of course, but maybe not the main focus.
ge0ffrey 2mo ago
Sounds similar to Timefold Platform: app.timefold.ai That's our Solver as a Service for scheduling problems (vehicle routing problem, shift scheduling, job scheduling, etc). It runs scheduling problems implemented with our open source solver: solver.timefold.ai But this post is such a service for formula problems instead (think master capacity planning, portfolio optimization, etc), due to the choice of MILP solvers underneath. Similar to NextMv, Neos, etc.
mulmboy 2mo ago
Very difficult to take seriously when the entire site appears to be AI-written.
amelius 2mo ago
Do you still pay if the solver cannot find a solution?
amelius 2mo ago
What are the open source equivalents, and how far are they behind/ahead?
jwally 2mo ago
For whatever its worth I built this about a decade ago because I am a non academic who can't think in tableaus, but still wanted to solve optimization problems. I created a json like schema/struct/whatever to describe the problem. Maybe adopt something like this and more people will be able to see how they could use your tool: https://github.com/JWally/jsLPSolver/blob/master/API.md https://github.com/JWally/jsLPSolver/blob/master/API.md I need to re go through the docs, but you get the gist. Here is the Berlin Airlift problem for example: const model = { optimize: "capacity", opType: "max", constraints: { plane: { max: 44 }, person: { max: 512 }, cost: { max: 300000 }, }, variables: { brit: { capacity: 20000, plane: 1, person: 8, cost: 5000 }, yank: { capacity: 30000, plane: 1, person: 16, cost: 9000 }, }, };

51 points by paddi91 2mo ago

paddi91 2mo ago

Keep it simple, just one call to solve every model.

uoaei 2mo ago
*sigh* We really need to teach this new crop the term "no free lunch". Again.
- cchianel 2mo ago
  I personally disagree with "no free lunch"; (for the uninitiated, "no free lunch" refer to the fact for any deterministic algorithm, there exist a problem that will force the algorithm to go through the entire solution space to find the optimal solution, with every single other possible algorithm beating it (https://en.wikipedia.org/wiki/No_free_lunch_theorem https://en.wikipedia.org/wiki/No_free_lunch_theorem)). For many planning problems, finding a good enough solution is sufficient, and there are many optimization algorithms that work for a wide variety of problems and provide a good enough solution in reasonable time. Different algorithms are better for different problems (ex: Metaheuristic (ex: Late Acceptance) Solvers beats MIP Solvers on vehicle routing, whereas MIP Solvers beat Metaheuristic Solvers on Employee Scheduling and Bin Packing. But both Metaheuristic and MIP Solvers provider good enough solutions for both vehicle routing and bin packing.
  - uoaei 2mo ago
    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 different problems So... why does your rhetorical style have such oppositional tone if you're just going to reaffirm the no free lunch theorem?
    - cchianel 2mo ago
      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/solvers can be configured, which fundamentally change the direction the solving takes (for example, a custom phase that uses your domain knowledge of the particular problem to get a good enough initial solution to be improved upon) (Side note: I am NOT affiliated with this post/project; from the website I don't really see a value add for it, especially since the site is lacking so many details).
      - uoaei 2mo ago
        Approximate solutions are good enough, but approximate solutions are not part of the solution space for the kinds of problems covered by OP.

greatony 2mo ago

looks interesting, how large problem can it solve?

cchianel 2mo ago
It seems to just be a wrapper over or-tools and other solvers from their landing page, with the difference being it run on their servers versus your hardware. Their website does not mention what hardware is allocated per model (which determine speed of solving) nor any limit on model size.
ge0ffrey 2mo ago
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.

TrueDuality 2mo ago

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.

ge0ffrey 2mo ago
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 2mo ago
  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.

quantum_state 2mo ago

This may be useful for small demos. For large scale MIP with millions of variables, one needs to have the solver at hand to support custom algos with techniques such as column generation, etc. to achieve time to solution and economics of compute resources. A remote API will not fit.

shoo 2mo ago

I'm not a potential customer for this, but i have worked on a few commercial projects involving combinatorial optimisation. Misc thoughts: - I'm not familiar with the LABS problem, but the LABS benchmark page is interesting & compares against Gurobi. I'd be curious to see how an existing commercial non-mip approximate solver such as Hexaly (formerly LocalSolver) compares here. - the other two benchmarks aren't very convincing as they don't compare against other methods or show running times - the front page mentions peer reviewed methodology - consider linking to the publications - good idea to have case studies of applications. I was a bit confused to see this listed under 'References' but on comparison the Gurobi & Hexaly marketing websites also do this (references -> case studies & references -> customer stories, respectively) - re the client API, you may want to make the server URL have a default, so your trial users / customers don't have to specify it. It may be easier for you to roll out changes to your server URL in future if you can do it by changing the default server URL in a new version of your client library rather than requiring your customers to update their source code. All the best!

LPisGood 2mo ago
Does anyone use Hexaly for any serious work? If so, why? There seem to be many better alternatives out there.
- luiwammus 2mo ago
  Amazon has been quite vocal about using Hexaly.
  - whatever1 2mo ago
    All vendors just need 1 engineer in a BigCorp to have used their software to claim "Trusted by BigCorpName"
    - LPisGood 2mo ago
      This is essentially my understanding of the situation. The OR applications are extremely diverse there; they use most wvery solver and technique under the sun for something.

DannyBee 2mo ago

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 interesting to me to use even if they actually were fronting commercial solvers, because they can't actually run it any faster and having this ".solve" API does nothing - pyomo already does that for me in practice.

akoboldfrying 2mo ago
> 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?
- DannyBee 2mo ago
  Not if i have to pay for it and use a different API? Remember, NEOS is both free, and pyomo already supports it natively without me changing anything - i can use both neos and local without any issue. Why would i move to something with a different API that i have to pay for?
  - akoboldfrying 2mo ago
    > Not if i have to pay for it and use a different API? TFA says Quicopt supports Pyomo. The example uses the "free tier", so you're not paying for anything (yet -- though I'm confused by the phrase "one-time entry point"). NEOS has generous limits, but the fact that Gurobi are still in business tells me that NEOS by itself can't satisfy everyone. Were Quicopt to offer access to Gurobi, etc., per your hypothetical, I think this would clearly be valuable. More strongly I don't think it's crazy to offer "just" publicly available solvers, despite how much weaker they are than commercial ones. If I had to solve a stream of optimisation problems that were individually pretty easy but the rate of arrival was unpredictable, using such an "Optimisation as a Service" would make sense in much the same way that it makes more sense to serve spiky web traffic by spinning up cloud VMs on demand than by buying a bunch of on-prem boxes, even though those cloud VMs might be very weak.

whatever1 2mo ago

We do have standard benchmarks in the field. Hans Mittelman maintains a library. No idea why they did not bother to run them. https://plato.asu.edu/guide.html https://plato.asu.edu/guide.html Their website has just 3 cherry picked instances and claim complete dominance.

ge0ffrey 2mo ago
We run the Mittelman VRPLib benchmarks at Timefold (and beat other open source solvers like or-tools in 95%+ of the X datasets). But they are not representive of the real world, at all. The Mittelman VRPLib benchmarks have only 1-2 constraints. Skills? No need. Working hours? Unlimited. Maps integretion? Cars can fly and the earth is a flat Euclidean space. Any VRP algorithm optimized for the vrplib datasets is overfitted and not the best one in reality. Take HGS for instance. Brilliant for CVRPTW. Crumbles to dust in field service routing for telco operations etc.
- whatever1 2mo ago
  That is fair, which is why you should not over-index on academic benchmarks. But a frontier solver should be within a small margin from the competitors in these benchmarks. If you are 10x slower, there is no way you can catch up (at least not without a ton of work from the practitioner side).

deanalyzer 2mo ago

This could be interesting, but it badly needs systematic benchmarking results. It is not difficult to get Claude Code or Codex to install and run a solver locally, so the tool’s current value proposition is fairly muddled. If there were evidence that it offered better performance, I might consider running larger workloads on it.

akoboldfrying 2mo ago
As I understand it, the value proposition is that it can deal with a spiky stream of problems to solve better than buying a bunch of on-prem hardware and running local solvers on them would -- similar to why it often makes sense to spin up cloud VMs on demand to handle spiky web traffic. Performance on an individual problem is still interesting of course, but maybe not the main focus.

ge0ffrey 2mo ago

Sounds similar to Timefold Platform: app.timefold.ai That's our Solver as a Service for scheduling problems (vehicle routing problem, shift scheduling, job scheduling, etc). It runs scheduling problems implemented with our open source solver: solver.timefold.ai But this post is such a service for formula problems instead (think master capacity planning, portfolio optimization, etc), due to the choice of MILP solvers underneath. Similar to NextMv, Neos, etc.

mulmboy 2mo ago

Very difficult to take seriously when the entire site appears to be AI-written.

amelius 2mo ago

Do you still pay if the solver cannot find a solution?

amelius 2mo ago

What are the open source equivalents, and how far are they behind/ahead?

jwally 2mo ago

For whatever its worth I built this about a decade ago because I am a non academic who can't think in tableaus, but still wanted to solve optimization problems. I created a json like schema/struct/whatever to describe the problem. Maybe adopt something like this and more people will be able to see how they could use your tool: https://github.com/JWally/jsLPSolver/blob/master/API.md https://github.com/JWally/jsLPSolver/blob/master/API.md I need to re go through the docs, but you get the gist. Here is the Berlin Airlift problem for example: const model = { optimize: "capacity", opType: "max", constraints: { plane: { max: 44 }, person: { max: 512 }, cost: { max: 300000 }, }, variables: { brit: { capacity: 20000, plane: 1, person: 8, cost: 5000 }, yank: { capacity: 30000, plane: 1, person: 16, cost: 9000 }, }, };