Part Feeding and Internal Transportation Decision Making for a Machinery Manufacturer

Ebenezer Olatunde Adenipekun (), Veronique Limère () and Nico André Schmid ()
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Ebenezer Olatunde Adenipekun: Department of Business Informatics and Operations Management, Ghent University, Ghent 9000, Belgium; and Flanders Make@UGent—Core Laboratory Cost and Value Analytics, Models and Optimization, Ghent 9000, Belgium
Veronique Limère: Department of Business Informatics and Operations Management, Ghent University, Ghent 9000, Belgium; and Flanders Make@UGent—Core Laboratory Cost and Value Analytics, Models and Optimization, Ghent 9000, Belgium
Nico André Schmid: Institut d’Économie Scientifique Et de Gestion School of Management, Université de Lille, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 9221, LEM–Lille Economie Management, F-59000 Lille, France

Interfaces, 2025, vol. 55, issue 2, 154-177

Abstract: Because of a constant increase in the variety and number of parts required for assembly, part provision to the border of line requires careful orchestration. Therefore, every part must be assigned to the most suitable feeding policy, determining logistical in-house processes, presorting degree, and load carrier sizing. This assignment allows effective space management at the border of line while avoiding unnecessary intralogistics activities. Furthermore, timely part delivery necessitates decisions on vehicle type selection and delivery frequencies. In this research, we adapt an existing optimization model to the specific case of a machinery manufacturing company, validating the model for practical usage. The model aims to achieve cost minimization through optimal part feeding policy and vehicle type selection decisions, considering various constraints related to space availability at the border of line and the capacity of transportation vehicles. Our results reveal that the optimal assignment reduces costs by 56% compared with the company’s current assignment. Although the optimal assignment requires additional capacity investments for the company, we also test various restrictive solutions by adding practically relevant constraints. The latter still results in minimum cost savings of 23%, compared with the current assignment. Besides the cost benefits, the number of different line feeding policies is reduced. This streamlines the process, making it more conducive to automation.

Keywords: assembly system; manufacturing; optimization; transportation; decision making (search for similar items in EconPapers)
Date: 2025
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