2 research outputs found
A speed-up procedure for the hybrid flow shop scheduling problem
Article number 115903During the last decades, hundreds of approximate algorithms have been proposed in the literature addressing
flow-shop-based scheduling problems. In the race for finding the best proposals to solve these problems, speedup procedures to compute objective functions represent a key factor in the efficiency of the algorithms. This
is the case of the well-known Taillard’s accelerations proposed for the traditional flow shop with makespan
minimisation or several other accelerations proposed for related scheduling problems. Despite the interest in
proposing such methods to improve the efficiency of approximate algorithms, to the best of our knowledge,
no speed-up procedure has been proposed so far in the hybrid flow shop literature. To tackle this challenge,
we propose in this paper a speed-up procedure for makespan minimisation, which can be incorporate in
insertion-based neighbourhoods using a complete representation of the solutions. This procedure is embedded
in the traditional iterated greedy algorithm. The computational experience shows that even incorporating the
proposed speed-up procedure in this simple metaheuristic results in outperforming the best metaheuristic for
the problem under consideration.Junta de Andalucía(España) US-1264511Ministerio de Ciencia e Innovación (España) PID2019-108756RB-I0
A critical-path based iterated local search for the green permutation flowshop problem
The permutation flowshop scheduling problem is a widely studied combinatorial optimization problem with
several real-world applications. In this paper we address a green variant of the problem with controllable processing times and two objective functions: one related to the service level of the factory (makespan) and another
one related to the total cost or the total energy/carbon consumption. For this problem we propose a novel
Critical-Path based Iterated Local Search. This metaheuristic incorporates several theoretical results to accelerate
the search of solutions in the intensification phase. The proposed algorithm has been compared on an extensive
benchmark with the most promising algorithms in the literature. The computational results show the excellent
performance of the proposal.Ministerio de Ciencia e Innovación PID2019-108756RB-I00Junta de Andalucía US-126451