A stochastic dual dynamic integer programming based approach for remanufacturing planning under uncertainty

ABSTRACT

We seek to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. We consider a multi-stage stochastic integer programming approach and use scenario trees to represent the uncertain information structure. We introduce a new dynamic programming formulation that relies on a partial nested decomposition of the scenario tree. We then propose a new approximate stochastic dual dynamic integer programming algorithm based on this partial decomposition. Our numerical results show that the proposed solution approach is able to provide near-optimal solutions for large-size instances with a reasonable computational effort.

KEYWORDS:

• Production planning
• lot-sizing
• remanufacturing
• multi-stage stochastic integer programming
• stochastic dual dynamic programming

Acknowledgements

The authors would like to thank anonymous referees for their detailed reviews that helped to improve an initial version of this paper.

Data availability statement

The data that support the findings of this study are openly available in GitHub at https://github.com/FrancoQuezada/SDDiP_RLS_IJPR2022.git.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

This research was partially supported by Dicyt project 062217QV, Vicerrectoría de Investigación, Desarrollo e Innovación, Universidad de Santiago de Chile.

Notes on contributors

Franco Quezada

Franco Quezada received his B.S degree in industrial engineering from University of Santiago (Chile) in 2014 and his Ph.D. degree in Computer Science from Sorbonne University (France) in 2021. He is currently assistant professor at university of Santiago, Chile, and researcher at the Program for the Development of Sustainable Production Systems (PDSPS), Chile. His research interests include combinatorial optimisation, mixed-integer linear programming, stochastic programming, production planning and lot-sizing problems in remanufacturing systems.

Céline Gicquel

Céline Gicquel received a Ph.D. in Operations Research in 2008 from Ecole Centrale Paris and got an HDR degree from the University Paris Saclay in 2021. She is currently an Associate Professor in Computer Science at the Université Paris Saclay and a member of the Laboratory for interdisciplinary research in numerical sciences (LISN). Her research interests include industrial production planning, facility location, combinatorial optimisation and stochastic programming. She is the author of 19 international journal papers.

Safia Kedad-Sidhoum

Safia Kedad-Sidhoum received a Ph.D. in Operations Research in January 1997 from Ecole Centrale Paris and an HdR degree from Pierre et Marie Curie University in November 2010. She is a member of the CEDRIC at CNAM Paris since January 2018 as Full Professor in Operations Research. She is leading the Combinatorial Optimization (OC) team of the CEDRIC. She is co-director of the MPRO (Parisian Master of Operations Research). She received a CNRS-Google Focused Research Award in 2011. Her research interests include combinatorial optimisation, supply chain, scheduling theory, planning and lot-sizing. She is the author of more than 30 international journal papers.