Optimizing a recover-and-assemble remanufacturing system with production smoothing
Seyed Mehdi Zahraei and
International Journal of Production Economics, 2018, vol. 197, issue C, 330-341
We consider a recover-and-assemble remanufacturing system in which an upstream stage recovers components from used products before a downstream stage performs assembly using the recovered components. In such systems, the inventory variances of the recovered components depend significantly on the production variabilities of the recovery and assembly processes. The production variabilities, in turn, are functions of the uncertain arrivals of used products (at the recovery) and demand for remanufactured products (at the assembly). We study how best to employ production smoothing to mitigate the uncertainties. We first build a network model that incorporates the inter-stage material and demand flows. We then derive the safety stock formulae as functions of the smoothing behavior and uncertainties. We embed the variables characterized by the model into an optimization procedure that optimizes the smoothing and the safety stocks. Through numerical examples, we demonstrate the application of the model, the value of production smoothing in this context and related managerial insights.
Keywords: Remanufacturing; Recover-and-assemble; Production smoothing; Safety stocks (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:197:y:2018:i:c:p:330-341
Access Statistics for this article
International Journal of Production Economics is currently edited by R. W. GrubbstrÃ¶m
More articles in International Journal of Production Economics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().