I'd like to add following restrictions to the CVRPTW problem, and know if it can be modeled on optaplanner:
1) Vehicles start in a garage, loaded in the customers and unloaded in a depot. After last unload, return to the garage
2) In each customer, the vehicle has a different volume to load, and a different time duration for the loading.
TW restrictions is global, every vehicles should start at time x and return to the garage at most on time z.
Thanks,
In the VRP examples in optaplanner-examples, it already has capacity and serviceDuration per TimeWinowedCustomer. All vehicles also return to the depot (= garage).
The only thing I see missing is an arrivalTimeBackAtTheDepot for a Vehicle, which is straightforward to calculate from the last customer's departureTime for each vehicle.
Related
I am tweaking Vehicle Routing Problem to cater our business needs using the provided Optaplanner example.
Capacity = Total number of hours available to a vehicle in a day(X hours)
Demand = Time to move to a Customer + Serving Time at Customer's location
The problems I'm facing -
Scheduling should be done on day to day basis with new Customer orders added and removed dynamically. How to do?
(Assumption - Customer orders will be available before scheduling but new Customer order might come while scheduling)
How to reset the Capacity? (Every other day vehicles are renewed with X hours. Every other day vehicles will have X hours available again.)
Vehicle may not need to return to the Depot at the end of the day and can start from the Last Customer location to get to new Customer another day.
What if the vehicle consumes all it's available hours during the transit before reaching the Customers location? Will it result in ageing for that particular customer?
Any insights about any of the problems mentioned above are appreciated.
I am working on using Optaplanner to solve the following a complex vrp problem with many requirements. I was able to handle most of them except for the following 2 aspects.
Pickups before DropOffs only
Enforce a specific path on the way to pickup customers.
The goal is to pickup a group of customers who are going to destinations that close together and put them in the same vehicle.
Thanks in Advance! I appreciate the help!
The Problem is very similar to the example VRP TimeWindow example but with the following changes.
Customers will be picked up at fixed locations (in a circuit)
Every pickup Customer will have a drop off destination (outside of
Circuit)
The vehicle will not head to a drop-Off then come back to pickup
again. (Once vehicle leaves circuit all it does is drop-Off its
customers at set location)
The Vehicle moving in the circuit has to move in a specific path
(imagine a 1 way street)
Planning on Using Road Distances with the Score between each Pickup-to-Pickup is Known. Pickup -> Drop-Off is not known (Planning on using Air).
I'm having a hard time in enforcing that after leaving the circuit to drop-Off customers a vehicle may not come back to pickup more customers, and having this work with the fixed path a vehicle can make in the circuit.
My main idea was to do the following.
Added a TYPE attribute to the customer to differentiate between pickup & customer
Added a variable listener to the customer class that keep track of all the DropOffIds currently when a vehicle arrives to it so that it only goes to a dropOffLocation if it has a passenger heading to that place. When a vehicle arrives to a dropOff it removes that item from the list. (Essentially serves as a stack).
The problem is theoretically this isn't stopping from a vehicle picking up a customer dropping him off then picking up another, if the customers locations are relatively close.
Also having a hardtime enforcing a fixed route a vehicle must take in a circuit, was planning on using a Cost Matrix to use the soft constraint to enforce the route implicitity(A vehicle wont go backwards or a skip a point as the cost would be too high), but not working the way it should be.
I might consider a domain model like this:
#PlanningEntity
class Pickup implements PickupOrVehicle {
Customer customer;
#PlanningVariable
PickupOrVehicle previousPickup;
#PlanningVariable
int dropOffPriority;
}
#PlanningEntity // Shadow entity
class Vehicle implements PickupOrVehicle {
...
#ShadowVariable(based on dropOffPriority and previousPickup)
List<Customer> dropOffOrderList;
// For consistency we might also add pickUpOrderList
}
That dropOffPriority should either be globally unique (by initializing it uniquely and only configure SwapMoves for that variable.
Or otherwise, the VariableListener should just order 2 assignments with the same dropOffPriority by their customer's id (because the ordering must be deterministic!).
No sure if it will work well. If you do try it out, do let us know here if it works well or not.
I am working on a VRP with the following characteristics:
The vehicle can only pick up 1 customer at one time
The vehicle picks up a customer at a location and drops off the customer at the destination
After dropping off the customer the vehicle goes on to pick up another customer
There is no depot that vehicle needs to go to in between the trips .
The vehicle makes as many trips as it can in a day to pickup/drop-off customers
At the end of day, the vehicle goes back to its designated parking station
Essentially, it's like a taxi system. It seems to me that it's a VRP with multiple trips(VRPMT) and no depot (Open VRP). I'd like to know
Does optaplanner has support for VPRMT and OVRP out of box?
If VPRMT and OVRP are not supported out of box, how easy is it to modify the existing CVRP/CVRPTW examples to make it solve OVRP and VPRMT?
Maybe I am on the wrong track and it's not even a VRP. If so, what would be the right planning problem for the taxi like system?
Many thanks in advance!
No, the examples don't include pickup and delivery or multiple trips yet
Users have done it before to add Pickup and Delivery (which is easy if multiple customers cannot be in the vehicle at the same time (otherwise it's harder and I might add an example for that way some day). Multiple trips and open VRP should by easy (although I think your parking station is a depot and you probably don't have multiple trips, just multiple customer sequentially in the same trip).
This is definitely a variant of VRP. Do note that every business has their own flavor of VRP. You'll always find some custom constraints (now or later in the development process). OptaPlanner allows you to add those custom constraints as needed.
Try this: Take the OptaPlanner VRP example, rename Depot to ParkingStation. Split up Customer's Location into pickupLocation and deliveryLocation and adjust the score rules so the soft score is the sum of:
all pickup to delivery distances
all delivery to next pickup distances
each parking to first pickup distance
each last delivery to parking distance
I am working on a VRPTW problem where vehicles have availablity constraints. This means that they might not be available the entire period of a day - basically a time windows for vehicles. Is there a way to build this in optaplanner?
Thank you and Brgds,
Paul
This following should work:
add 2 fields (start and end) on (TimeWindowed)Vehicle
in ArrivalTimeUpdatingVariableListener use at least the vehicle's start time before it leaves a depot
add a hard constraint that the vehicle should be back at the depot by it's end time. Make sure to penalize not just -1, but instead, minus the amount of time it's overdue (to avoid a score trap, see docs).
I'm new to optaplanner and im trying to tweak the VRP example to solve a "similar" problem... but with some differences:
We have a central depot, n customers and a fleet of one or more vehicles.
Each customer has a certain "maximum capacity" (not the same for all of them).
Same for each vehicle.
Initial conditions from the problem include:
"actual capacity" and "desired capacity" for each customer, so that:
actual capacity is >=0 and <=max capacity
desired capacity is >=0 and <=max capacity
"actual depot capacity" is the amount of items available at the depot
We want to REDISTRIBUTE some items so that each customer gets his "desired capacity".
(Tipically most items will be redistributed from one customer to another)
Hard constraints are "maximum capacity"s cannot be excedeed
Soft constraints are vehicle distance (minimize) and difference from "desired capacity" (minimize)
I started by generating a data file for the VRP example with negative customer demand (to simulate desired capacity < actual capacity) but quickly discover that constraint in drl that sums up all customers demands to satisfy vehicle capacity restriction is not what we need here.
¿Do you think that this problem is "similar enough" to worth modifying VRP example?
Yes, it's definitely worth taking the OptaPlanner VRP example (with or without the example GUI) and customizing it to these needs. Start from OptaPlanner 6.0.0.CR1 or better yet CR4 which will be released next week normally. Don't use CR3 because it might have a bug related to shadow variables in VRP.
A) Let's presume customerDemand = customerDesired - customerActual. This can be positive (need more items) and negative (need to get rid of items).
Question: How do we validate the Vehicle capacity? We can't just sum it, because it fluctuates from customer to customer. Answer: We use a shadow variable (let's call it freight) on Customer to calculate how many items the vehicle is transporting from the previous customer to the current. This is very similar to how arrivalTime works for the TimewindowedCustomer example. Then use a score constraint rule to verify freight on every customer is lower than the vehicle's maximum capacity. And also that freight is >= 0 at every customer.
Now, A) works well if there are plenty of vehicles. Even if there aren't enough items, checking the soft constraint is pointless because it's a fixed constant number (the number of items lacking). You could even decide to spread that shortage fairly over the customers and adjust their customerDesired accordingly, before scheduling.
B) However, if there aren't plenty of vehicles, relative to the number of customers, then A) might be insufficient. We 'll want to allow to deliver/pickup less than customerDemand, making it more flexible (and therefore complex :). In this case, a Customer has 2 genuine (=non-shadow) planning variables (instead of just 1): the previousStandstill (~ previous customer) and the deliveryPickup (<= demand * 2).