Solving a multi-manned assembly line balancing problem in a Pareto sense
by Parames Chutima; Krit Prasert
International Journal of Process Management and Benchmarking (IJPMB), Vol. 8, No. 4, 2018

Abstract: This paper presents a novel algorithm which modifies the coincident algorithm (COIN), namely the adaptive extended coincident algorithm (AE-COIN), to solve a multi-manned assembly line balancing problem (MALBP). The multiple objectives are optimised in a hierarchical manner comprising of the following objectives: 1) minimise the number of workers; 2) minimise the number of stations; 3) balance workloads between stations and maximise work relatedness. The objectives in the third hierarchy are optimised in a Pareto sense since they are conflicting in nature. The performances of AE-COIN are compared against well-known algorithms, i.e., BBO, NSGA II and DPSO. The experimental results show that AE-COIN outperforms its contestants in both solution quality and diversity.

Online publication date: Mon, 01-Oct-2018

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