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?
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.
Comments
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.
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.
- 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.
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.
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.