A genetic programming hyper-heuristic for the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{SONG:2021:SEC,
-
author = "Hong-Bo Song and Jian Lin",
-
title = "A genetic programming hyper-heuristic for the
distributed assembly permutation flow-shop scheduling
problem with sequence dependent setup times",
-
journal = "Swarm and Evolutionary Computation",
-
volume = "60",
-
pages = "100807",
-
year = "2021",
-
ISSN = "2210-6502",
-
DOI = "doi:10.1016/j.swevo.2020.100807",
-
URL = "https://www.sciencedirect.com/science/article/pii/S2210650220304600",
-
keywords = "genetic algorithms, genetic programming, Distributed
assembly flow-shop scheduling, Hyper-heuristic,
Sequence dependent setup time",
-
abstract = "In this paper, a genetic programming hyper heuristic
(GP-HH) algorithm is proposed to solve the distributed
assembly permutation flow-shop scheduling problem with
sequence dependent setup times (DAPFSP-SDST) and the
objective of makespan minimization. The main idea is to
use genetic programming (GP) as the high level strategy
to generate heuristic sequences from a pre-designed
low-level heuristics (LLHs) set. In each generation,
the heuristic sequences are evolved by GP and then
successively operated on the solution space for better
solutions. Additionally, simulated annealing is
embedded into each LLH to improve the local search
ability. An effective encoding and decoding pair is
also presented for the algorithm to obtain feasible
schedules. Finally, computational simulation and
comparison are both carried out on a benchmark set and
the results demonstrate the effectiveness of the
proposed GP-HH. The best-known solutions are updated
for 333 out of the 540 benchmark instances",
- }
Genetic Programming entries for
Hong-Bo Song
Jian Lin
Citations