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Towards Efficient Solvers for Optimisation Problems

EasyChair Preprint no. 1738

4 pagesDate: October 22, 2019

Abstract

Constraint programming is pervasive and widely used to solve real-time problems which input data could be scaled up to the huge sizes, and the results are required to be given efficiently and dynamically. Many technologies such as constraint programming, hybrid technologies, mixed integer programming, constraint-based local search, boolean satisfiability could have different solvers and backends to solve the real-time problems. Streaming videos problem is the problem that requires to decide which videos to put in which cache servers in order to minimise the waiting time for all requests with a description of cache servers, network endpoints and videos are given. In this paper, we will model the streaming videos problem in two different ways. The first model will be implemented using heuristics, and the global constraints will be used in the second model. The experiments will be benchmarked using MiniZinc, which is an open-source constraint modelling language that can be used to model constraint satisfaction and optimisation problems in high-level, solver-independent way. The aim of the paper is to benchmark those technologies to evaluate the execution time and final scores of the two models using large instances of input data from Google Hash Code.

Keyphrases: Constraint Programming, modelling, Optimisation

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:1738,
  author = {Huu-Phuc Vo},
  title = {Towards Efficient Solvers for Optimisation Problems},
  howpublished = {EasyChair Preprint no. 1738},

  year = {EasyChair, 2019}}
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