[pgrouting-users] Evaluate swap

Tue Apr 11 10:06:17 PDT 2017

This is very helpful, thank you.

In this particular case, only the lateness of the load is a concern because
of contractual obligations. Earliness is inefficient, but a different
problem.

Planning and dispatch appear to be a VRPTW problem. If we have to use a
speed we generally use 55 miles per hour or the speed limit of the road if
known.

In the case of "rescues" there are only a few points to consider if you are
evaluating just two trucks, the "problem" truck and the possible solution.

Unit 1 location.
Unit 1 destination.
Unit 2 (evaluated truck)
Unit 2 destination (there may not be a destination if the unit is not under
dispatch)
Swap location

Output: lateness of both trucks on their swapped destinations, along with
"out of route" miles and projected date and time of meeting for each unit.

The problem is to find an ideal replacement truck and the best place to
meet and exchange trailers (out of a set of given locations). The obvious
way is to do this evaluation for all trucks, but that is extremely time
consuming.

Thank you for your input and all of your work, I've been following the
pgRouting project for several years now and it is very useful.

Tom White

On Tue, Apr 11, 2017 at 11:15 AM Vicky Vergara <vicky at georepublic.de> wrote:

> Hello Tom.
>
> I am the one working on the code of the VRP pick & delivery, and I will
> write a little about the pgr_gsoc_vrppdtw and how I changing it.
> This will help me flush my ideas in an organized manner, while trying to
> understand the problem.
> At the end I talk about your problem in particular.
>
> Hope this helps
>
> Regards
> Vicky
>
>
>
> The  VRP problem makes some assumptions, like having a single depot, a
> homogeneous fleet of vehicles, one route per vehicle. if you have one
> Vehicle then it becomes the TSP problem, which is NP-HARD optimization
> problem.
>
> Then you have different flavours of VRP like:
> CVRP
> VRPPD
> VRPTW
> CVRPTW
> VRPMT
> OVRP
> Each one has the own assumptions.
>
> The Vehicle kind: truck, car, bicycle, air plane, tractor trailor, as a
> start is irrelevant
>
> The methods to solve any of them, can go from brute force search trying
> all possible permutations, or simulated annaeling, or tabu search and many
> more that I don't know about. I am doing sort a of tabu-search: get
> different initail solutions with optimize until It can't.
>
> Traveling salesman problem
> The inputs:
> One Vehicle.
> A set N of locations
> PROBLEM
> The Vehicles start and end the trip from the same location and visit all
> locations, (no location is visited twice).
> SOLVE
> with any of "know" methods
> http://www.math.uwaterloo.ca/tsp/
> The problem is NP
>   - with luck then its the global minimum
>   - with brute force them N! (N factorial) seconds later get the global
> minimum
> RESULT
> A route for the single truck that is a local minimum
>
> VRP
> The inputs:
> A set of homogenous vehicles.
> A set of locations
> PROBLEM
> The Vehicles start and end the trip from the same location and visit all
> locations, (no location is visited twice).
> SOLVE TRY 1
> Distribute the locations among the Vehicles
> Solve TSP for each Vehicle
>
> Distributing the locations, was it a good distribution?, maybe not, so:
>
> SOLVE TRY 2
> While (I am not happy with the solution) {
>   Distribute the locations among the Vehicles
>   Solve TSP for each Vehicle of the fleet
> }
>
> lets refine a little:
> SOLVE TRY 3
> Distribute the locations the Vehicles
> Solve TSP for each Vehicle of the fleet
> While (I am not happy with the solution) {
>   Re-distribute some of the locations among a subset of the Vehicles
>   Solve TSP for each modified Vehicle of the fleet
> }
>
> more refining:
> SOLVE TRY 3
> Distribute the locations among Vehicles
> Solve TSP for each Vehicle of the fleet
> Now there is an initial solution
> While (I am not happy with the solution) {
>   Re-distribute "wisely" some of the locations among a subset of the
> Vehicles
>      - by inserting the location in the "best place" because the current
> trip is already in a local minimum
>   Re-Evaluate the whole trips of the fleet
> }
>
> Adding Time Windows
> The Drivers shift defines the Vehicle's trip time windows: From what time
> can the Vehicle depart, to what time can the Vehicle arrive
> Remember they depart from the same location. well what actually I am doing
> is that its not the same concept of location now.
> Before: depot location location(x,y)
> Now: depot location (x,y,open,close)
> The before in terms of now: location(x,y,0,inf)
>
> Time is involved now, before everything was distance. more information is
> needed about the truck, like Speed.
> When reading some papers the Speed is implicitly 1 and they don't bother
> about the speed, actually pgr_gsoc_vrppdtw, does exactly that.
>
> Adding time windows will affect the code, as a start TSP, keeping track of
> the time is needed now, and the "solution" found might be invalid because
> it can make the truck arrive time to the destination late. So the way to
> distribute the locations becomes difficult, consider that It will become
> NP-HARD, because to get the initial solution:
>
> while (The solution found is not valid)
>   Distribute the locations among the Vehicles
>   Solve TSP for each Vehicle of the fleet
> }
> Currently this is what I am trying to improve.
>
>
>
> *So, talking about the trailer trucks of your problem using pgRouting:*
> its an almost pick and delivery? Cargo goes from A to H:
> - traliler 1 departs from A, arrives at B delivers cargo,  continues trip,
> arrives at destination C
> - trailer 2 departs from D, arrives at B pickups cargo, on arrival at E
> delivers cargo, continues trip, arrives at destination F
> - trailer 3 departs from G, arrves at E pickups cargo, continues trip,
> arrives at destination H and unloads cargo
>
> I say almost, because for example at point B:
> - truck 1 arrives unload the cargo, after that it can leave
> - truck 2 arrives loads the cargo, it can leave after that.
>
> So data is needed somehow like this:
> From the point of view of truck 1, the cargo at point B might have a time
> window [8:15, 8:30] with a service time of 0:02 minutes (time to unload the
> cargo)
>
> From point of view of truck 2 the cargo at point B has a time window
> [8:10, 8:35] with a service time of 0:19, that is, make sure it arrives
> before the one that unloads, make sure it leaves after the one that
> unloaded, and the 19 minutes is the time window length of the possible
> arrival time of the cargo + 2 minutes of uloading of the other truck + 2
> minutes of loading to this truck.
>
> So the same cargo is a different cargo (different location, different
> time, different truck, different time window) the only similarity might be
> that is the same weight.
>
> Note that the time windows are given as data, they are not calculated.
> What I mean by this, is that, the algorithm will try to find a suitable
> trucks that can accomplish the time windows restriction.
> What the algorithm will not do is that based on the arrival time of the
> cargo, change the time windows of thcost matrixe departure time of another
> cargo.
> btw
> - locations given by (x,y) and speed 1 is pgr_gsoc_vrppdtw
> - working on: locations given by (x,y) and different speeds on vehicles
> (so the vehicles are no homogeneous)
> - working on: cost matrix is an input
>   - if the cost matrix is a distance matrix, then trucks must have a speed
>   - if the cost matrix is a time matrix, then trucks must have a factor
> (to simulate different speeds)
>
>
> (If being working on this topic since November any donation is
> appreciated. http://pgrouting.org/)
>
>
>
> On Tue, Apr 11, 2017 at 8:45 AM, Tom White <tom.white at gmail.com> wrote:
>
> Hello,
>
> I am trying to figure out a way to evaluate a swap between two vehicles.
> Tractor-trailers can meet and swap trailers and continue to each other's
> destinations. The advantage lies in drivable hours - drivers are limited to
> a certain number of driving hours and are required to take breaks of
> varying lengths. One truck may have enough hours available to prevent a
> load from being late. One truck may even have two drivers.
>
> Does anyone know of any previous work in this area, either open source or
> academic research? Does anybody have an idea how this could be solved with
> pgRouting tools?
>
> Thank you,
>
> Tom White
>
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>
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> Vicky Vergara
> Operations Research
>
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