Optimal planning of technology roadmap under uncertainty

Chaoan Lai, Liang Xu and Jennifer Shang

Journal of the Operational Research Society, 2020, vol. 71, issue 4, 673-686

Abstract: The selection and planning of technical projects is an important and challenging investment decision for companies as significant amount of capital is often involved. With the growing complexity and scale, managing technical research projects and technology roadmap (TRM) are greatly affected by uncertainties than ever before. However, existing approaches for addressing these problems are restricted to deterministic environments. In this study, a general methodology based on graph theory and mathematical programming for R&D projects planning subject to uncertainty is proposed to maximize profit and to find precedence relations according to technological trends for given budgets and time. We first put forward a new graph model and its mathematical definition to represent the relations among technologies. The network contains nodes to represent technologies and edges to denote feasible paths between two technology nodes. To deal with uncertainty, a network-based novel robust optimization model as well as a chance constrained model is developed. Finally, we apply the proposed model and solution approach to the TRM of Smart Home industry. The numerical study shows that the proposed method can effectively and efficiently solve the optimization problems for technical project planning, path designing, and project management, under uncertainty.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1581406 (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:tjorxx:v:71:y:2020:i:4:p:673-686

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2019.1581406

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

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