A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times

Abstract

This paper investigates a distributed assembly permutation flow-shop scheduling problem with transportation and sequence-dependent set-up times (DAPFSP-TSDST). A hybrid genetic programming (HGP) algorithm is proposed to optimize the makespan of the assembly stage, which inherits the merits of genetic programming (GP) and neighbourhood search operators. In HGP, a hybrid problem-specific initialization heuristic is developed to make populations more diverse. Multiple neighbourhood search operators are employed as the leaf nodes, which are vital for the success of GP. A product shift strategy is proposed to strengthen its exploitability. In addition, a simulated annealing criterion is adopted to make the HGP explore more thoroughly. Finally, statistical and computational experiments are carried out on the benchmark instances. The results exhaustively identify the notable competitiveness of the HGP algorithm in coping with the DAPFSP-TSDST.

KEYWORDS:

• Distributed assembly problem; genetic programming; scheduling; manufacturing

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

Data are available from the authors upon reasonable request.

Additional information

Funding

This research is supported by the Natural Science Foundation of China [grant numbers 61673228, 62072260], the Natural Science Foundation of Shandong Province [grant number ZR2020MF094] and the Science Technology Program of Qingdao [grant number 21-1-2-16-zhz].