A robust possibilistic multi-echelon multi-product multi-period production-inventory-routing problem considering internal operations of cross-docks: Case study of FMCG supply chain

Integrated and simultaneous decision-making is essential for the production, reproduction, storage, distribution, and cross-docking, especially in Fast-Moving Consumer Goods (FMCG) supply chains. In this paper, a production -inventory-routing problem (PIRP) is essential. We have addressed the PIRP considering four-echelon multi-product, multiple periods, and the reverse flow of defective products. The PIRP also considers the consolidation of the tasks of cross-docks. Robust possibilistic programming (RPP) and possibilistic chance-constrained pro-gramming (PCCP) model the demand uncertainty. Uncertainty performance metrics evaluate the proposed so-lution approaches. Meta-Heuristics, including Teaching-Learning-based Optimization (TLBO) and Invasive weed optimization (IWO) algorithms, solve the deterministic equivalence of RPP and PCCP. Results demonstrate the benefits of the developed models and the robustness of the solution procedures in a real-life FMCG supply chain.

Automated summary

This paper addresses the production-inventory-routing problem (PIRP) in Fast-Moving Consumer Goods (FMCG) supply chains, considering multi-products, multi-periods, and the reverse flow of defective products in a four-echelon system. The consolidation of cross-dock tasks is also taken into account. Robust possibilistic programming and possibilistic chance-constrained programming are used to model demand uncertainty. Meta-Heuristics algorithms are applied to solve the deterministic equivalence of the models. The results demonstrate the effectiveness and robustness of the proposed models and solution procedures in real-life FMCG supply chains.