Towards Efficient Solvers for Optimisation Problems

Abstract

Constraint programming (CP) 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 CP, hybrid technologies, mixed integer programming (MIP), constraint-based local search (CBLS), boolean satisfiability (SAT) 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 model the streaming videos problem in two different ways. The first model is implemented using heuristics, and the global constraints are used in the second model. The experiments are benchmarked using MiniZinc, which is an open-source constraint modelling language that can be used to model constraint satisfaction and optimisation problems in the high-level, solver-independent way. The aim of the paper is to benchmark these technologies to evaluate the execution time and final scores of the two models using large instances of input data from Google Hash Code.