Enhancing and extending the classical GRASP framework with biased randomisation and simulation

Abstract

Greedy Randomised Adaptive Search Procedure (GRASP) is one of the best-known metaheuristics to solve complex combinatorial optimisation problems (COPs). This paper proposes two extensions of the typical GRASP framework. On the one hand, applying biased randomisation techniques during the solution construction phase enhances the efficiency of the GRASP solving approach compared to the traditional use of a restricted candidate list. On the other hand, the inclusion of simulation at certain points of the GRASP framework constitutes an efficient simulation–optimisation approach that allows to solve stochastic versions of COPs. To show the effectiveness of these GRASP improvements and extensions, tests are run with both deterministic and stochastic problem settings related to flow shop scheduling, vehicle routing, and facility location.

Keywords:

• GRASP
• combinatorial optimisation
• stochastic optimisation
• biased randomisation
• simheuristics

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness [grant Numbers TRA2013-48180-C3-3-P and TRA2015-71883-REDT], FEDER, the Catalan Government [grant Number 2014-CTP-00001], and the Ibero-American Program for Science and Technology for Development [grant Number CYTED2014-515RT0489]. Likewise, we want to acknowledge the support received by the doctoral grant from the UOC.