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Multi-objective optimization for mixed-model assembly line sequencing and balancing in the context of Industry 4.0

Abstract : During the last decade, customers' demands have been transformed significantly and are becoming more and vaster. To answer this transformation, a new production approach, called customized mass production, is introduced. All kinds of production systems are concerned by this transformation; however, we focus mainly on mixed-model assembly lines having a brilliant role in producing a vast variant of products at a low quantity. One of the most important challenges in assembly lines is concerned with the determination of production sequence. In customized mass production, operation time can be varied from one product to the next one, and therefore, we generate an unbalancing between several workstations. Then, for customized mass production, we are obligated to solve the sequencing and line balancing problem simultaneously. This study proposes a new multi-objective mathematical model for this problem. Since the objective functions are conflicting with each other, the augmented e constraint (AUGMECON) method is used to solve the problem. Knowing that after the resolution of the given problem, this method can create several alternative solutions, and a multi-criteria decision-making tool is used to rank these alternatives solutions.
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https://hal.archives-ouvertes.fr/hal-03098335
Contributor : Armand Baboli Connect in order to contact the contributor
Submitted on : Tuesday, January 5, 2021 - 5:05:16 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:08 PM

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  • HAL Id : hal-03098335, version 1

Citation

Mehran Majidian-Eidgahi, Armand Baboli, Reza Tavakkoli-Moghaddam. Multi-objective optimization for mixed-model assembly line sequencing and balancing in the context of Industry 4.0. 2020 IEEE International Conference on Industrial Engineering and Engineering Management - IEEM2020, Dec 2020, Singapore, Singapore. ⟨hal-03098335⟩

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