Unleashing Constraint Optimisation Problem solving in Big Data environments

摘要

The application of the optimisation problems in the daily decisions of companies is able to be used for finding the best management according to the necessities of the organisations. However, optimisation problems imply a high computational complexity, increased by the current necessity to include a massive quantity of data (Big Data), for the creation of optimisation problems to customise products and services for their clients. The irruption of Big Data technologies can be a challenge but also an important mechanism to tackle the computational difficulties of optimisation problems, and the possibility to distribute the problem performance. In this paper, we propose a solution that lets the query of a data set supported by Big Data technologies that imply the resolution of Constraint Optimisation Problem (COP). This proposal enables to: (1) model COPs whose input data are obtained from distributed and heterogeneous data; (2) facilitate the integration of different data sources to create the COPs; and, (3) solve the optimisation problems in a distributed way, to improve the performance. It is done by means of a framework and supported by a tool capable of modelling, solving and querying the results of optimisation problems. The tool integrates the Big Data technologies and commercial solvers of constraint programming. The suitability of the proposal and the development have been evaluated with real data sets whose computational study and results are included and discussed. (C) 2020 Elsevier B.V. All rights reserved.