Title: Genetic regulatory network-based optimisation of master production scheduling and mixed-model sequencing in assembly lines
Authors: Youlong Lv; Jie Zhang; Liling Zuo
Addresses: Institute of Artificial Intelligence, Donghua University, Shanghai, 201620, China ' Institute of Artificial Intelligence, Donghua University, Shanghai, 201620, China ' College of Mechanical Engineering, Donghua University, Shanghai, 201620, China
Abstract: The integration of master production scheduling and mixed-model sequencing ensures just in time production of orders and balanced workload between stations for assembly lines. However, such integrated optimisation is complicated because of the high interdependence between these two problems. Based on mathematical model of the integrated optimisation problem, a two-level genetic regulatory network is constructed by representing decision variables with gene states and describing multiple objectives and various constraints with gene regulation equations. The solutions are generated through gene expression procedures in which some gene states are activated based on regulation equations, and the optimal one with minimum objective function value is obtained via parameter optimisation in regulation equations by using a real-coded genetic algorithm. The genetic regulatory network-based method is applied to the case study of a diesel engine assembly line, and the results demonstrate the effectiveness of this method over other ones in realising integrated optimisation.
Keywords: integrated optimisation; diesel engine assembly line; master production scheduling; MPS; mixed-model sequencing; MMS; genetic regulatory network; GRN.
International Journal of Bio-Inspired Computation, 2022 Vol.20 No.3, pp.150 - 159
Received: 13 Mar 2020
Accepted: 16 Apr 2021
Published online: 07 Dec 2022 *