Discrete Particle Swarm Optimization Approach Research Articles

A Discrete Particle Swarm Optimization Approach for Airline Crew Scheduling

A Discrete Particle Swarm Optimization Approach for Airline Crew Scheduling

A new discrete particle swarm optimization approach for the single-machine total weighted tardiness scheduling problem with sequence-dependent setup times

In this paper we present a new Discrete Particle Swarm Optimization (DPSO) approach to face the NP-hard single machine total weighted tardiness scheduling problem in presence of sequence-dependent setup times. Differently from previous approaches the proposed DPSO uses a discrete model both for particle position and velocity and a coherent sequence metric. We tested the proposed DPSO mainly over a benchmark originally proposed by Cicirello in 2003 and available online. The results obtained show the competitiveness of our DPSO, which is able to outperform the best known results for the benchmark. In addition, we also tested the DPSO on a set of benchmark instances from ORLIB for the single machine total weighted tardiness problem, and we analysed the role of the DPSO swarm intelligence mechanisms as well as the local search intensification phase included in the algorithm.

A discrete differential evolution algorithm for the single machine total weighted tardiness problem with sequence dependent setup times

This paper is concerned with solving the single machine total weighted tardiness problem with sequence dependent setup times by a discrete differential evolution algorithm developed by the authors recently. Its performance is enhanced by employing different population initialization schemes based on some constructive heuristics such as the well-known NEH and the greedy randomized adaptive search procedure (GRASP) as well as some priority rules such as the earliest weighted due date (EWDD) and the apparent tardiness cost with setups (ATCS). Additional performance enhancement is further achieved by the inclusion of a referenced local search (RLS) in the algorithm together with the use of destruction and construction (DC) procedure when obtaining the mutant population. Furthermore, to facilitate the greedy job insertion into a partial solution which will be employed in the NEH, GRASP, DC heuristics as well as in the RLS local search, some newly designed speed-up methods are presented for the insertion move for the first time in the literature. They are novel contributions of this paper to the single machine tardiness related scheduling problems with sequence dependent setup times. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, its highly effective performance with substantial margins both in solution quality and CPU time is shown against the best performing algorithms from the literature, in particular, against the very recent newly designed particle swarm and ant colony optimization algorithms of Anghinolfi and Paolucci [A new discrete particle swarm optimization approach for the single machine total weighted tardiness scheduling problem with sequence dependent setup times. European Journal of Operational Research 2007; doi: 10.1016/j.ejor.2007.10.044] and Anghinolfi and Paolucci [A new ant colony optimization approach for the single machine total weighted tardiness scheduling problem. http://www.discovery.dist.unige.it/papers/Anghinolfi_Paolucci_ACO.pdf, respectively. Ultimately, 51 out of 120 overall aggregated best known solutions so far in the literature are further improved while other 50 instances are solved equally.