A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling

ABSTRACT

Nowadays, manufacturing enterprises must have fast response and flexible production capabilities to meet personalized and diversified market demands. Mixed-model production and distributed production have become the preferred production methods for enterprises. This article studies a distributed heterogeneous hybrid flow shop scheduling problem with a mixed-model assembly line (DHHFSP-MMAL), which consists of manufacturing and assembly stages. The DHHFSP-MMAL is modelled by a mixed integer linear programming (MILP) model. Three constructive heuristics and a parallel deep adaptive large neighbourhood search (PDALNS) problem are presented. A constructive heuristic with a group strategy is employed to obtain an initial solution. Several deep destroy-and-repair operators are proposed where problem-specific greedy local search methods are applied. The PDALNS assigns weights to destroy-and-repair operators to guide the selection of operators. The parallel computing technique is introduced to increase the efficiency of training. The experiments demonstrate that the PDALNS algorithm is an efficient and effective algorithm for solving the DHHFSP-MMAL problem.

KEYWORDS:

• Distributed heterogeneous hybrid flow shop
• mixed-model assembly line
• adaptive large neighbourhood search
• scheduling
• parallel technique

Data availability statement

The results data are available from the online supplemental data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [Grants 62103195, 62003203, 62262018]; the Jiangsu Natural Science Foundation [Grant BK20210558]; the Youth Talent Support Program of the Association for Science and Technology in Xi'an, China [Grant 095920211321]; the China Postdoctoral Science Foundation funded project [Grants 2021M701700, 2023M732166]; the Fundamental Research Funds for the Central Universities [Grant GK202201014]; the Research Startup Fund of Shaanxi Normal University and Nanjing Normal University; Open Project Fund of Key Laboratory of Numerical Simulation for Large Scale Complex Systems, Ministry of Education, China.