CVRP | Capacitated Vehicle Routing Problem Solution | Machine Learning library

 by   daentech Java Version: Current License: No License

kandi X-RAY | CVRP Summary

kandi X-RAY | CVRP Summary

CVRP is a Java library typically used in Artificial Intelligence, Machine Learning applications. CVRP has no bugs, it has no vulnerabilities and it has low support. However CVRP build file is not available. You can download it from GitLab, GitHub.

Capacitated Vehicle Routing Problem (CVRP) Solution using Evolutionary/Genetic Algorithms.

            kandi-support Support

              CVRP has a low active ecosystem.
              It has 3 star(s) with 2 fork(s). There are 2 watchers for this library.
              It had no major release in the last 6 months.
              CVRP has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of CVRP is current.

            kandi-Quality Quality

              CVRP has 0 bugs and 0 code smells.

            kandi-Security Security

              CVRP has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              CVRP code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              CVRP does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              CVRP releases are not available. You will need to build from source code and install.
              CVRP has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed CVRP and discovered the below as its top functions. This is intended to give you an instant insight into CVRP implemented functionality, and help decide if they suit your requirements.
            • Runs the QR algorithm
            • Performs the prx operation
            • Renders the results in the graph
            • Performs the genetic algorithm
            Get all kandi verified functions for this library.

            CVRP Key Features

            No Key Features are available at this moment for CVRP.

            CVRP Examples and Code Snippets

            No Code Snippets are available at this moment for CVRP.

            Community Discussions


            Vehicle routing problem with dependent dimension constraints (Google ORTools)
            Asked 2021-Dec-12 at 21:16

            I'm very new to OR-Tools and I'm trying to solve a modified VRP with capacity constraints from Google's guide.

            In my problem vehicles transport multiple types of items. Some types can be transported together and others cannot.

            What I tried

            In the following code the types are A and B (they should not be transported together).

            First I defined the two callbacks for demands and added the dimensions to the routing model



            Answered 2021-Dec-08 at 09:23

            I fixed the problem.

            The issue was that the number of nodes was 3 (noDeliveries), however the number of indices was 6, so I only set the constraint on half of them.

            Fixed code:



            Fast pathfinding and distance matrix in 2d grid
            Asked 2021-Aug-25 at 08:41

            Context: I'm working on a warehouse simulation that supports different floor designs and simulates one or multiple agents that are tasked with order picking. One order can consist of more than one product. The routing for picking products is solved as a capacitated vehicle routing problem (CVRP). This requires a distance matrix between product locations, for which the A* algorithm is used. Currently, distance matrices are generated per order, just before picking on that order is started. Many simulation runs are desired for accurate measures, so computational efficiency is of high importance. For completeness, I included a screenshot of the simulation below, with product locations (dark), an agent (white), products in the current order (blue), and the products being picked in the current route (green/red). Note that the white lines represent the current picking priority, not the exact paths.

            Problem: The size of the distance matrix grows quadratically with the number of products per order. Therefore, the time for computing it with A* quickly becomes unacceptable.

            Question: I need a method that makes the computation of distance matrices more efficient. This can be either an exact method or a heuristic, as long as not too much accuracy is sacrificed. I am not looking for implementations or specific code snippets, but for ideas and/or methods that are used for similar problems that I can implement myself.

            Attempted methods/ideas: Here are some approaches I've considered or tried to implement with no success:

            • Distance matrix for the full warehouse: unfeasible, as the number of product locations is simply too large.
            • Using Euclidean distance: not good enough. This would assume that products on opposite sides of a warehouse row are close together when in reality an agent would have to take a long detour between the two.
            • Using a clustering algorithm to identify areas that are close together and base a distance matrix on clusters instead of individual locations: this would reduce the total matrix size, making it possible to pre-compute it completely. However, this would greatly reduce accuracy and I've yet to find a clustering algorithm that reliably works for this problem with different floor layouts.

            Layout examples: White pixels indicate floor cells, black pixels indicate product locations. Products within an order are randomly selected from all possible locations. More floor layouts (any floor layout!) should be supported by the chosen method.

            Here is a pasteable array for layout 1 if anyone wants to mess around with it:



            Answered 2021-Aug-25 at 08:41

            I know link-only answers are usually discouraged, but "what algorithms can make A* faster" is a hugely complicated topic that's been an active area of research nonstop for the past 50 years. So it's not really possible to give anything more than a vague summary in a Stackoverflow answer.

            For 2D grids like your own, there are two common techniques that give huge speedups:

            • JPS (Jump Point Search) is a variant of A* that exploits the symmetries in 2D grids that contain lots of open space to avoid queuing/dequeuing huge numbers of extraneous nodes.
            • RSR (Rectangular Symmetry Reduction) is a preprocessing algorithm that reduces a map into "rooms" (or in your case, "hallways") to form a sort of navigation mesh for your map, reducing the size of the graph.

            Additionally, since you mentioned it does not need be optimal,

            Finally, if your grid changes over time, there is a whole field of incremental algorithms that perform better than A*.



            Divide individual Pandas dataframe values by corresponding value in dataset
            Asked 2021-Aug-24 at 17:37

            I am working on a CVRP and have gotten to the point where I have a list of vehicle fleets, where each fleet is made up of a homogeneous vehicle type, and the amount of goods that should be in each fleet. The corresponding dataframe looks like this:



            Answered 2021-Aug-24 at 17:03

            I think the problem lies in the way you access your data, especially with "x.Quantity_Employed". This works, so the problem is probably in the lambda function:



            Solving Time-constrained CVRP with two vehicle types in Google or-tools
            Asked 2021-Jun-15 at 12:54

            I am modeling a Time-constrained CVRP. The problem is to minimize the total travel time (not including the package dropping time) subject to vehicle (delivery) capacity and total time spent (per vehicle) constraints. The package dropping time refers to an additional time to be spent at each node, and the total time spent equals to the travel time plus this additional time. I have the below model that works for a single vehicle-type case. I would like to introduce two-vehicle type concept in there, meaning that I have a set of V1 type vehicles and another set of V2 type vehicles. The only difference of the vehicle-types is the per time cost of travel. Let x denote the per time unit cost of travel by V1, and y denote the per time unit travel cost of V2. How can I design the model so that it incorporates this additional aspect?



            Answered 2021-Jun-13 at 13:34

            Simply register two transits callbacks (i.e. one per vehicle type)

            Then use the overload of AddDimension() to pass an array of registered transit callback index.

            e.G. Mizux/



            Time taken to solve a Vehicle Routing Problem (CVRPTW) using Google OR Tools depends on the ordering of vehicle capacity
            Asked 2021-May-20 at 10:47

            I have tried to create a CVRPTW solution by combining CVRP and VRPTW from examples provided in the official documentation.

            Since the code is big I cannot paste it here, so I am sharing a colab notebook demonstrating a short example reproducing the problem:

            The quantities to deliver at each node are [0, 80, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1]

            I have 22 vehicles, 21 vehicles with capacity 25 and 1 vehicle with capacity 600. If the vehicles are sorted in ascending order the example with 14 nodes take more than 200 seconds to solve and if they are sorted in descending order the same example gets solved in less than a second.

            I am using Google OR Tools for the first time so I don't know if this was expected or not. I have tried adding Guided Local Search also but when I add a time limit to stop it stops without giving a solution.

            I created an issue in the repo but it was converted to a Discussion.


            I have also asked this question in the official mailing list.




            Answered 2021-May-17 at 16:09

            This is a well known property of OR solvers. Because they include lots of heuristics, they are very sensitive to the order of variables or constraints in the model.



            OR-TOOLS: PCVRP with delivery patterns
            Asked 2021-Apr-20 at 08:37

            Please help me on my periodic capacitated vehicle routing problem.

            Find the same question here on google-groups.

            What I am trying to model:

            • 1 depot

            • multiple customers (let's assume 4)

            • multiple days (let's assume 3)

            • Multiple vehicle types with price per km and specific capacity

            • Every customer has: delivery frequency and demand per delivery

            • Every customer has to be assigned to a delivery pattern. Multiple delivery patterns are possible for each frequency. The pattern indicates if a delivery is possible on that day or not. For a frequency of 1 and 3 working days: [[1,0,0],[0,1,0],[0,0,1]], a frequency of 2 [[1,1,0],[1,0,1],[1,1,0]], a frequency of 3: [1,1,1]these are the possible patterns.

            The demand of each customer per delivery is the same, for every day a delivery is performed

            So we have a set of customers that have to be delivered by one depo. Multiple Vehicle Types are available for the job. The day of service is flexible because only the frequency is fixed to a customer, but there are multiple possibilities to fulfill the demand.

            What I did so far:

            • I copied every node by the number of working days.

            • I restrict the start and end to the depo nodes of each day.

            • I multiply the vehicles by the number of days.

            • Furthermore, I handle the days as different kinds of cargo. I do assign a capacity of zero to the vehicle when it should not be used on that day.



            Answered 2021-Apr-20 at 08:37

            So I found a solution for my problem.

            The solving time for problems greater than 40 customers could be better.

            I would appreciate improvement suggestions.



            OR-tools CVRP with a distance constraint not working
            Asked 2021-Apr-15 at 07:13

            I am using the OR-tools code to optimize bus routes with distance and capacity constraints. This is the output that I got:



            Answered 2021-Apr-15 at 07:13

            By default, the arc cost for empty route aka Start -> End is zero.
            So your last two vehicle routes, being empty, will return 0 when using routing.GetArcCostForVehicle(previous_index, index, vehicle_id).

            You can enable the cost using RoutingModel::ConsiderEmptyRouteCostsForVehicle(bool consider_costs, int vehicle).

            Something like this should work IMHO:



            How to get ortools to return cost?
            Asked 2021-Apr-11 at 08:17

            How does one get googleortools to give back the actual cost of a vrp? This example only returns distance and loads but if I add more cost elements I would like it to show me cheapest outcome.




            Answered 2021-Apr-11 at 08:17

            You can retrieve it using assignment.ObjectiveValue() (in Python)

            ps: on master all VRP samples should have the objective cost in the print function ;)



            Google OR-tools VRP - Pickup/Dropoff at same node
            Asked 2021-Apr-10 at 12:27

            I'm currently using Python and the Google-Or-Tools to solve an VRP problem, that I am not sure how exactly to model. (If someone has a solution with another library/tool, absolutely interested as well)

            Problem Statement:

            I have basically the classical CVRP problem also described and modelled on the documentation page, but with one addition. So the basic CVRP problem is, I have a depot where cargo is loaded and vehicles start from / end at. Also I have locations where goods are to be delivered to, e.g. they have a demand. Now the addition is, that I not only dropoff goods at the specific locations but also want to pickup goods at the same locations, and eventually dropping them off at the depot again.

            Since it is possible for a location to have more goods to pickup than to dropoff, I need to explicitly check this, too ... but I can't find a way so far to do this.

            Example: So say I have one Depot node 0 and two location nodes (A,B).

            • At location A I need to dropoff 10 units and pickup 11.
            • At location B I need to dropoff 10 units and pickup 9.

            Now for a vehicle with max capcity 20 the possible solution would be:

            • 0 --> B --> A --> 0

            First visiting A and then B would not work, since it would violate the capacity constraint in A.

            What I've tried:

            So to consider the basic dropoff capacity constraint I have the standard



            Answered 2021-Apr-09 at 07:55

            IIRC, already answered on the mailing list.
            Here a gist with a sample: Mizux/

            Basically you'll need two dimensions.

            • One to track only deliveries goods
            • One to track the total load (pickup and delivery)
            • One constraint on start nodes to make both dimension cumul vars equals.

            Main idea: Having two dimensions instead of one, will avoid the solver to use pickup goods to perform deliveries.

            Field Start A B End Deliveries 0 -10 -10 0 Total 0 -10+11 -10+9 0



            OR-Tools CVRP with Multiple Trips
            Asked 2021-Mar-17 at 10:50

            I'm trying to use OR-Tools' Routing Solver to solve a Multi Trip Capacitated VRP. What I need is

            • one depot, route starts and ends here.
            • one unloading location (different from the depot)
            • set time window and demand for each node

            So the vehicles should pick up the goods from each node until the capacity is filled. Then go to "unloading location", unload all their weight and keep collecting the demand from nodes until a time limit is reached or all the goods are collected. Then return back to the depot.

            CVRP Reload Example seems very close but in my case, at the end of the route vehicles should visit the unloading location before the depot. In other words a vehicle can not go to the depot (starting, ending location) with load.




            Answered 2021-Mar-17 at 10:50

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install CVRP

            You can download it from GitLab, GitHub.
            You can use CVRP like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the CVRP component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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