Integrating production, replenishment and fulfillment decisions for supply chains: a target-based robust optimisation approach

Abstract

AbstractIn this paper, a three-echelon supply chain problem under demand uncertainty is considered. The problem is formulated as a multiperiod two-stage stochastic optimisation model. The first stage, consisting of production and replenishment decisions, is integrated with the second stage, which comprises reactive fulfillment decisions, allowing seamless determination as demands are revealed over time. The demand in each period is characterised by an uncertainty set based on the nominal value and demand bounds. We propose a target-based robust optimisation (TRO) approach to determine the most robust planning with respect to a pre-specified cost target. The proposed TRO approach can trade off the total cost (performance) and model feasibility in the presence of demand perturbation (robustness) by fine-tuning the cost target. The robust counterpart is converted to a quadratically constrained linear programming (QCLP) problem, which can be solved by commercial solvers. Numerical experiments demonstrate that the TRO approach can outperform traditional robust optimisation methods in terms of both cost and feasibility against demand uncertainty by enabling precise adjustment of the cost target. Importantly, the TRO approach provides a flexible means to strike a balance between performance and robustness metrics, making it a valuable tool for supply chain planning under uncertain conditions.Keywords: Supply chain planningtarget-based robust optimisationdemand fulfillmentinventory poolinglateral transshipments Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementDerived data supporting the findings of this study are available from the corresponding author, Daoheng Zhang, on request.Additional informationFundingThis work was supported by University of New South Wales Canberra [Tuition Fee Scholarship].Notes on contributorsDaoheng ZhangDaoheng Zhang Daoheng Zhang is currently a Ph.D. student in Computer Science at UNSW Canberra. He received an MS degree in Management Science and Engineering from Nanjing University in 2017. His research areas are robust optimisation and its application to supply chain management.Hasan Hüseyin TuranHasan Hüseyin Turan H. Turan is a Lecturer and the Research Lead at Capability Systems Centre, UNSW Canberra. Before joining UNSW Canberra, he worked as a post-doc research fellow at Qatar University, Mechanical and Industrial Engineering Department from 2015 to 2017. He obtained his Ph.D. and master's degrees both in Industrial and Systems Engineering from Istanbul Technical University and North Carolina State University, respectively.Ruhul SarkerRuhul Sarker Ruhul A Sarker is a Professor in the School of Systems and Computing at UNSW Canberra. He served as the Director of Faculty PG Research (June 2015 to May 2020) and as the Deputy Head of School (Research) of the School of Engineering and IT (2011-2014). Prof. Sarker's broad research interests are decision analytics, operations research, applied optimisation, and Computational Intelligence with an special emphasis on evolutionary optimisation.Daryl EssamDaryl Essam Daryl Essam is a senior lecturer and Deputy Head (Research) in the School of Systems and Computing at UNSW Canberra. His research interests include Fractal Image Generation and Compression, Artificial Intelligence, particularly Genetic Programming, and Operations Research.