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Thanks for asking! Value is actually the value of a state itself, and it can increase or decrease from one state to its successors. If the value must increase or decrease monotonically, then that (very useful) information can be given to Hop through a Bounds() method. In the simplest cases, value is added or subtracted with each state transition, and can be stored in an attribute. However, that isn't required. It's possible to do just about anything in the value function, such as run a simulation to estimate a KPI. If estimating value is expensive, then it's probably best to keep the diagram width low, and/or use a searcher that dives to high quality solutions before expanding the search. Hop comes with some of these capabilities built in, and most of its solver components are plug-able through interfaces so you can provide your own if you need. When it comes to scale, we're trying to make it so Hop scales up and down well to different size problems. As you add side constraints such as precedence, time windows, driver hours, capacity, etc., you'll likely find that DDs do better since those constraints tend to support the search. Dash is a generic discrete event simulation engine that (intentionally) looks a lot like Hop (JSON in/out, runners that package up sims as atomic binaries). Sim actors conform to a simple interface and use an event sourcing architecture to publish events, update state, and record measurements. We should have a blog post about it in the next couple weeks, as well as our docs posted online, so check back! |