Minimizing Total Tardiness on Identical Parallel Machines Using VNS with Learning Memory

Minimizing total tardiness on identical parallel machines is an NP-hard parallel machine scheduling problem that has received much attention in literature due to its direct application to real-world applications. For solving this problem, we present a variable neighbourhood search that incorporates a learning mechanism for guiding the search. Computational results comparing with the best approaches for this problem reveals that our algorithm is a suitable alternative to efficiently solve this problem.