DC programming and DCA for supply chain and production management: state-of-the-art models and methods

Hoai An Le Thi

International Journal of Production Research, 2020, vol. 58, issue 20, 6078-6114

Abstract: It is undoubtedly that mathematical modelling and optimisation play a key role in the supply chain and the production management (SCPM). In this paper, we provide a survey on DC (Difference of Convex function) programming and DCA (DC Algorithm), a state-of-the-art optimisation approach for challenging problems in SCPM. DC programming and DCA constitute the backbone of non-convex programming and global optimisation. Whilst DC programming and DCA were widely and successfully investigated in many areas, it seems that they were not so much popular in the community of SCPM. There is therefore a need to further develop this efficient and scalable approach for SCPM applications, especially for large-scale problems in the context of Big data. For such purpose, this paper aims to present benchmark models and state-of-the-art DCA-based methods for solving challenging problems in SCPM systems. We prove that all the benchmark classes of optimisation models appeared in SCPM systems can be formulated/reformulated as a DC program and show how to solve these classes of problems by DCA-based algorithms. We offer the community of researchers in SCPM efficient algorithms in a unified DC programming framework to tackle various applications such as supply chain design, scheduling, multi-stage production/inventory system, vehicle routing, …

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2019.1657245 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:58:y:2020:i:20:p:6078-6114

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2019.1657245

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().