Even if more reliable than the past, the performance of modern manufacturing systems is still affected by machine’s deteriorations and breakdowns. As a consequence, adequate maintenance programs must be implemented to adequately satisfy demands during manufacturing stops due to unexpected failures or preventive maintenance (PM) actions.Despite production and maintenance are closely related issues, their joint optimization has become an important research topic just during the last decade. Therefore, the present paper proposes a model for the combined optimization of production/inventory control and PM policies with the aim of minimizing the total expected cost per unit time. The model is formulated referring to a continuous production system characterized by a random deteriorating behavior so that the presence of a buffer is considered to ensure a continuous products supply during interruptions of service caused by breakdowns or planned maintenance actions on the production system. Unlike the main part of the existing literature, non-restriction on the failure occurrence is here forced, namely that the manufacturing system may fail at any age within the production cycle as well as more than one failure may occur during the same period. A Simulated Annealing-based algorithm combined with aMonte Carlo simulation module is proposed as a resolution approach. The robustness of the developed algorithm is demonstrated by means of repeated runs of different simulated scenarios characterized by diverse sets of cost parameters. Results also confirm the effectiveness of the proposed threelevel theoretical inventory profile.

La Fata, C., & Passannanti, G. (2017). A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies. INTERNATIONAL JOURNAL, ADVANCED MANUFACTURING TECHNOLOGY, 91(9-12), 3899-3909 [10.1007/s00170-017-0053-3].

A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies

LA FATA, Concetta Manuela
;
PASSANNANTI, Gianfranco
2017

Abstract

Even if more reliable than the past, the performance of modern manufacturing systems is still affected by machine’s deteriorations and breakdowns. As a consequence, adequate maintenance programs must be implemented to adequately satisfy demands during manufacturing stops due to unexpected failures or preventive maintenance (PM) actions.Despite production and maintenance are closely related issues, their joint optimization has become an important research topic just during the last decade. Therefore, the present paper proposes a model for the combined optimization of production/inventory control and PM policies with the aim of minimizing the total expected cost per unit time. The model is formulated referring to a continuous production system characterized by a random deteriorating behavior so that the presence of a buffer is considered to ensure a continuous products supply during interruptions of service caused by breakdowns or planned maintenance actions on the production system. Unlike the main part of the existing literature, non-restriction on the failure occurrence is here forced, namely that the manufacturing system may fail at any age within the production cycle as well as more than one failure may occur during the same period. A Simulated Annealing-based algorithm combined with aMonte Carlo simulation module is proposed as a resolution approach. The robustness of the developed algorithm is demonstrated by means of repeated runs of different simulated scenarios characterized by diverse sets of cost parameters. Results also confirm the effectiveness of the proposed threelevel theoretical inventory profile.
Settore ING-IND/17 - Impianti Industriali Meccanici
Settore ING-IND/16 - Tecnologie E Sistemi Di Lavorazione
La Fata, C., & Passannanti, G. (2017). A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies. INTERNATIONAL JOURNAL, ADVANCED MANUFACTURING TECHNOLOGY, 91(9-12), 3899-3909 [10.1007/s00170-017-0053-3].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/230718
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