Open this publication in new window or tab >>2025 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 79, p. 515-527Article in journal (Refereed) Published
Abstract [en]
The efficiency of an assembly line depends on how the work is distributed along the line. This is known as the Assembly Line Balancing Problem, an NP-hard optimization problem. Automatic solvers for this problem have been studied for decades but have not been widely adopted in the industry, resulting in a theory-practice gap. The typical automation approach assumes that all constraints and objectives are known and can be statically defined ahead of time such that solvers with a precisely defined objective function can take a fully specified problem instance as input and produce a (near) optimal solution as output. In some industries, meeting these assumptions is particularly challenging because of properties such as mixed-model production with high model variance, multi-manned stations, large task graphs, etc. This paper explains why, in certain industries, such as automotive end assembly, complete automation is likely infeasible in practice due to challenges in modeling the problem, collecting data, and specifying the objective function. Manual intervention by an engineer as a decision-maker is therefore unavoidable. We argue that maximizing automation, by helping the decision-maker be as effective as possible, requires a decision support system (DSS) that supports an interactive and iterative workflow, thereby enabling assisted planning. Furthermore, we identify solver features that become relevant in the DSS context, thus making the case that focusing on standalone solvers, and treating the integration into a DSS as an implementation detail, is not a viable option. We conclude that decision support systems play a central role in closing the theory-practice gap.
Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Assembly line balancing, Decision support systems, Theory-practice gap, Rebalancing, Collaborative intelligence, Automotive industry
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-109997 (URN)10.1016/j.jmsy.2025.01.019 (DOI)001433886800001 ()2-s2.0-85217801633 (Scopus ID)
Funder
Vinnova, 2021-05071, 2023-00970, 2023-00450
Note
Validerad;2025;Nivå 2;2025-02-26 (u2);
Funder: KDT JU grant 2023-000450;
Full text: CC BY license;
This article has previously appeared as a manuscript in a thesis.
2024-09-162024-09-162025-06-24Bibliographically approved