or-tools

问题 I am using pywraplp to solve for a minimization problem. I noticed that the solver gives an optimal solution for smaller input matrices (6 X 99). However, it gives an infeasible solution for a matrix size larger than mentioned. Is this a solver limitation? I am guessing there could be some default parameters that we can modify. Can you tell me how to tackle this scenario? 来源： https://stackoverflow.com/questions/59987051/is-there-a-constraint-on-the-size-of-input-matrices-to-pyraplps-linear

问题 I am using pywraplp to solve for a minimization problem. I noticed that the solver gives an optimal solution for smaller input matrices (6 X 99). However, it gives an infeasible solution for a matrix size larger than mentioned. Is this a solver limitation? I am guessing there could be some default parameters that we can modify. Can you tell me how to tackle this scenario? 来源： https://stackoverflow.com/questions/59987051/is-there-a-constraint-on-the-size-of-input-matrices-to-pyraplps-linear

问题 I am using pywraplp to solve for a minimization problem. I noticed that the solver gives an optimal solution for smaller input matrices (6 X 99). However, it gives an infeasible solution for a matrix size larger than mentioned. Is this a solver limitation? I am guessing there could be some default parameters that we can modify. Can you tell me how to tackle this scenario? 来源： https://stackoverflow.com/questions/59987051/is-there-a-constraint-on-the-size-of-input-matrices-to-pyraplps-linear

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 24 days ago . How can we add service time in end time and next arrival time change according to previous end time + service time 来源： https://stackoverflow.com/questions/59692546/how-to-add-delivery-service-time-in-google-ortools-vrp

问题 Generating some random Gaussian coordinates, I noticed the TSP-solver returns horrible solutions, however it also returns the same horrible solution over and over again for the same input. Given this code: import numpy import math from ortools.constraint_solver import pywrapcp from ortools.constraint_solver import routing_enums_pb2 import matplotlib %matplotlib inline from matplotlib import pyplot, pylab pylab.rcParams['figure.figsize'] = 20, 10 n_points = 200 orders = numpy.random.randn(n

问题 I tried to elaborate on the Assignment with Task Sizes example by adding a multi-dimensional "size"(demand) and "total_size_max" . The solution seems to decide to allocate everything to 1 worker only despite the fact that others are available. The result when run allocates everything to worker(machine 18). Sample output : Machine 18 is assigned to produce Component 0 with minimum Cycle_Time = 10000000 Machine 18 is assigned to produce Component 1 with minimum Cycle_Time = 10000000 Machine 18

问题 I'm trying to install Google's or-tools on mac 10.10 - https://code.google.com/p/or-tools/wiki/OrToolsWithPyPi . I install using python2.7 setup.py install --user (tried also with sudo and without --user) but get the following when it goes to pypi to download the package: .. Some other output .. Installed /Users/Zach/Library/Python/2.7/lib/python/site-packages/ortools_examples-1.3549-py2.7.egg Processing dependencies for ortools-examples==1.3549 Searching for ortools Reading https://pypi

问题 I used ortools from Google to solve the problem of employees shifts using C# language. I have a simple problem in ShiftSchedulingSat is that I specified the number of employees 21 and the number of weeks 4 weeks and every day 18 Shift evenly distributed over the 3 periods and the table was created successfully and then I run the program again with all the same settings, but I was surprised that the Status: UnKnown Why? static void Main(string[] args) { SolveShiftScheduling(); } static void

问题 I would like to build a Google OR Tools model to use linear_solver for a CBC_MIXED_INTEGER_PROGRAMMING . Following Google tutorial I learned hot to build the constraints but I have a question... is it necessary to hand write every constraint? I mean, I have the following DataFrame df_constraint which contain the coefficient of the constraints in the form of ax+by<=c . +---+---+---+ | A | B | C | +---+---+---+ | 1 | 5 | 7 | | 2 | 9 | 3 | | 3 | 0 | 4 | +---+---+---+ the table could be

问题 I have a linear problem of finding all solutions that meet all constraints. For example my variables are = [0.323, 0.123, 1.32, 6.3...] Is it possible to get for example top 100 solutions sorted by fitness(maximization/minimization) function? 回答1: In a continuous LP enumerating different solutions is a difficult concept. E.g. consider max x, s.t. x <= 1 . Obviously x=1 , x=0.99999 are solutions and so are the infinite number of solutions in between. We could enumerate "corner solutions" (or