Abstract
As a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.
Highlights
Several manufacturing companies are resorting to increased product variety and customization in order to face competition (Wiengarten et al 2017)
Since commercial solvers and expertise in the optimization field are often not available in these contexts, the authors propose to run the optimization only for a limited number of days, with the support of an external partner, aiming to gather a data sample for the training of a supervised machine learning (ML) model that can be later applied to support daily transshipment decisions. They consider different ML techniques (i.e., k-nearest neighbor, classification and regression tree, random forest, multilayer perceptron artificial neural network) and they apply a heuristic algorithm to turn the solution obtained through the ML model into a feasible one, concluding that the end-to-end learning method always allows to achieve total costs which are close to the optimal ones, compared to the empirical approach currently used by the hospitals
The explanatory attributes correspond to part and assembly system characteristics: while it is possible to include in the dataset all the available data, we suggest selecting only significant ones based on the knowledge of the specific problem, avoiding the risk of low interpretability and accuracy due to an overly complex ML model (Fischetti and Fraccaro 2019)
Summary
Several manufacturing companies are resorting to increased product variety and customization in order to face competition (Wiengarten et al 2017). The ALFP has been addressed in literature either by developing optimization models (e.g., Caputo et al 2018; Baller et al 2020; Schmid et al 2021) or by categorizing parts and assigning these categories to policies based on the value of some characteristics of both the parts and the assembly system (e.g., Caputo and Pelagagge 2011; Usta et al 2017) Previous contributions focus on specific problems, neither formalizing the steps required for the implementation of such methods nor offering any general methodological guidelines Given these premises, this paper explores the application of ML to deal with the ALFP, aiming at improving the trade-off between efficiency (i.e., amount of data and resources needed to solve the ALFP) and solution quality (i.e., closeness to the optimal solution) of the approaches currently available in literature.
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