An extended continuous estimation of distribution algorithm for solving the permutation flow-shop scheduling problem

ABSTRACT

This article proposes an extended continuous estimation of distribution algorithm (ECEDA) to solve the permutation flow-shop scheduling problem (PFSP). In ECEDA, to make a continuous estimation of distribution algorithm (EDA) suitable for the PFSP, the largest order value rule is applied to convert continuous vectors to discrete job permutations. A probabilistic model based on a mixed Gaussian and Cauchy distribution is built to maintain the exploration ability of the EDA. Two effective local search methods, i.e. revolver-based variable neighbourhood search and Hénon chaotic-based local search, are designed and incorporated into the EDA to enhance the local exploitation. The parameters of the proposed ECEDA are calibrated by means of a design of experiments approach. Simulation results and comparisons based on some benchmark instances show the efficiency of the proposed algorithm for solving the PFSP.

KEYWORDS:

• Extended continuous estimation of distribution algorithm
• local search
• hybrid algorithm
• permutation flow-shop scheduling problem

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number U1433116], Jiangsu Innovation Program for Graduate Education [grant number KYLX16_0382] and the Aviation Science Foundation of China [grant number 20145752033].