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

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11 Citazioni (Scopus)

Abstract

Even if more reliable than the past, the performance ofmodern manufacturing systems is still affected by machine’sdeteriorations and breakdowns. As a consequence, adequatemaintenance programs must be implemented to adequately satisfydemands during manufacturing stops due to unexpected failuresor preventive maintenance (PM) actions.Despite productionand maintenance are closely related issues, their joint optimizationhas become an important research topic just during the lastdecade. Therefore, the present paper proposes a model for thecombined optimization of production/inventory control and PMpolicies with the aim of minimizing the total expected cost perunit time. The model is formulated referring to a continuousproduction system characterized by a random deteriorating behaviorso that the presence of a buffer is considered to ensure acontinuous products supply during interruptions of servicecaused by breakdowns or planned maintenance actions on theproduction system. Unlike the main part of the existing literature,non-restriction on the failure occurrence is here forced, namelythat the manufacturing system may fail at any age within theproduction cycle as well as more than one failure may occurduring the same period. A Simulated Annealing-based algorithmcombined with aMonte Carlo simulation module is proposed asa resolution approach. The robustness of the developed algorithmis demonstrated by means of repeated runs of different simulatedscenarios characterized by diverse sets of cost parameters.Results also confirm the effectiveness of the proposed threeleveltheoretical inventory profile.
Lingua originaleEnglish
pagine (da-a)3899-3909
Numero di pagine11
RivistaINTERNATIONAL JOURNAL, ADVANCED MANUFACTURING TECHNOLOGY
Volume91
Stato di pubblicazionePublished - 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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