In-house part supply logistics optimisation based on the workforce's ergonomic strain and environmental considerations

Parames Chutima and Chayanee Prakong

International Journal of Industrial and Systems Engineering, 2024, vol. 47, issue 3, 271-310

Abstract: This paper focused on in-house part supply logistics adopted by an automotive manufacturer to make just-in-time deliveries of parts from a supermarket to mixed-model serpentine-shaped assembly lines without shortage. Five objectives are optimised simultaneously, i.e., minimising the total number of tours, minimising the number of tow train drivers, minimising the energy expenditure load discrepancy among tow train drivers, minimising the total inventory kept at the border of the line and minimising the total PM2.5 emission released by a fleet of tow trains. The mathematical model is formulated for the problem. Due to its NP-hard in nature, multi-objective metaheuristics have to be developed for solving practical-sized problem instances. As a result, the non-dominated sorting teaching-learning-based optimisation III (NSTLBO III) which is a hybrid of the non-dominated sorting genetic algorithm III (NSGA III) and teaching-learning-based optimisation (TLBO) is proposed to solve the problem. The results show that NSTLBO III outperforms NSGA III and the multi-objective evolutionary algorithm based on decomposition (MOEA/D) in terms of qualitative, convergence-related and comprehensive metrics.

Keywords: part feeding; automotive industry; multi-objective optimisation; NSGA III; TLBO. (search for similar items in EconPapers)
Date: 2024
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