SUSTAINABLE CASHEW SUPPLY CHAIN WITH RADIO FREQUENCY IDENTIFICATION: A HYBRID ROBUST OPTIMIZATION APPROACH

Date

2026-07-08

Author

Mondal, Arijit
Roy, Sankar Kumar
He, Shawei
Weber, Gerhard Wilhelm
Yu, Vincent F.
Jablonsky, Josef

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This study integrates forward and reverse logistics with sophisticated uncertainty modeling to create a technology-enabled, sustainable supply chain network for the cashew sector. A multi-objective mixed-integer programming (MOMIP) model is proposed to simultaneously optimize four sustainability dimensions: economical, environmental, water consumption, and social impact (including job creation and child labour considerations). Radio frequency identification (RFID) technology is integrated to improve traceability and decrease operational inefficiencies. An additional optimization model is created to identify its ideal deployment position. Uncertainty in transportation cost and emission factors is addressed using distributionally robust optimization (DRO) with a Wasserstein ambiguity set to capture partial distributional information. Robust convex programming (RCP) is used to represent demand uncertainty, which is defined as the lack of trustworthy probabilistic data. The resulting hybrid robust framework enables simultaneous handling of both randomness and deep uncertainty. The LP-metric approach is used to solve the multi-objective model, enabling a fair compromise between competing sustain-ability objectives. Computational experiments based on multiple test instances demonstrate that the proposed hybrid robust model shows better performance than traditional stochastic programming approach in terms of reduced variability in objective functions and enhanced service reliability. Sensitivity analyses further highlight the impact of uncertainty parameters and RFID deployment on network performance. The results offer practical advice for creating agricultural supply chains that are transparent, robust, and sustainable, particularly in data scarcity.

URI

https://hdl.handle.net/11511/120595

Collections

Graduate School of Applied Mathematics, Article