Hybrid spider monkey optimisation algorithm for multi-level planning and scheduling problems of assembly lines

Jabir Mumtaz, Zailin Guan, Lei Yue, Li Zhang and Cong He

International Journal of Production Research, 2020, vol. 58, issue 20, 6252-6267

Abstract: The production planning and scheduling problems of printed circuit board (PCB) assembly line robustly influence the production efficiency of PCB industries. The current study focuses on the optimisation of the multi-level planning and scheduling problem by minimising the cycle time of the PCB assembly lines. Two levels of planning problems i.e. component allocation problem (CAP) and component placement sequence problem (CPSP) are solved simultaneously using mixed integer linear programming model. In CAP, a model is formulated with the objective of balancing the workload among the surface mounted machines (SMM), while in CPSP, a model is formulated to find optimum sequencing for allocated components at each SMM. A novel hybrid spider monkey optimisation (HSMO) algorithm is proposed with the addition of new sorting food sources and genetic operators in the standard spider monkey optimisation (SMO) algorithm. The performance of the proposed HSMO algorithm is validated by comparing the solutions with well-known algorithms, i.e. genetic algorithm (GA), particle swarm optimisation (PSO), simulated annealing (SA) and artificial bee colony (ABC) algorithms. The proposed HSMO algorithm is tested on different problem set instances scaled based on the realistic production of PCB industries. The detailed analysis of results indicates that the proposed HSMO algorithm outperforms the compared algorithms in efficiency and effectiveness.

Date: 2020
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DOI: 10.1080/00207543.2019.1675917

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