Why decision support systems are needed for addressing the theory-practice gap in assembly line balancing

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Názov: Why decision support systems are needed for addressing the theory-practice gap in assembly line balancing
Autori: Fink, Christoffer, 1984, Bodin, Ulf, Schelén, Olov
Zdroj: Journal of manufacturing systems. 79:515-527
Predmety: Assembly line balancing, Decision support systems, Theory-practice gap, Rebalancing, Collaborative intelligence, Automotive industry, Cyberfysiska system, Cyber-Physical Systems
Popis: 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.
Popis súboru: electronic
Prístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-109997
https://doi.org/10.1016/j.jmsy.2025.01.019
Databáza: SwePub
Popis
Abstrakt: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.
ISSN:20210507
02786125
DOI:10.1016/j.jmsy.2025.01.019