JOpt.SDK is a vehicle routing software library for Java that uses specialized genetic algorithms to calculate an optimized allocation of orders and stops to mobile resources. The algorithm not only provides tours at minimum costs but also considers an arbitrary set of constraints for each tour. You may define your own constraints and optimization goals in order to customize JOpt.SDK to your specific planning needs or you decide to use one of our best practices addons in order to achieve a fast application of our optimization algorithms to selected industries.
JOpt.SDK can solve nearly any problem that can be classified by one of the following types:
* TSP - Traveling Salesman Problem. JOpt.SDK finds the shortest or fastest path for your mobile resources
* VRPTW - Vehicle routing problem with time windows - like TSP but for a set of vehicles. JOpt.SDK finds an optimal allocation of orders and stops within a vehicle fleet. It may also consider different constraints for vehicles, drivers and stops.
JOpt.SDK functionality can be accessed via Java API and thus fits seamlessly into any JAVA application. Software developers may integrate the JOpt.SDK component into their application in order to offer their customers a consistent solution including optimization of mobile workforce schedules. A seamless integration into your software allows the look and feel of one piece of software for your customer.
The process of writing large parallel programs is complicated by the need to specify both the parallel behaviour of the program and the algorithm that is to be used to compute its result.
We present the Priority R-tree, or PR-tree, which is the first R-tree variant that always answers a window query using O((N/B)1-1/d+T/B) I/Os, where N is the number of d-dimensional (hyper-) rectangles stored in the R-tree, B is the disk block size, and T
AI Related Ruby Extensions This page will maintain list of AI related extensions/modules/gems for the Ruby programming language. Please contact me if you know something I missed.
The MCL algorithm is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs based on simulation of (stochastic) flow in graphs.
We propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small turbulence. It is particularly useful for motion detection from outdoor videos with low quality. It can also effectively delineate moving objects in both static and dynamic background. Experimental results demonstrate that this approach outperforms other existing methods by extracting the moving objects more completely with lower false alarms.