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

• Published in: Computers & industrial engineering 2023-05, Vol.179, p.109206, Article 109206
• Main Authors: Dehghani Jeshvaghani, Mina, Amiri, Maghsoud, Khalili-Damghani, Kaveh, Olfat, Laya
• Format: Article
• Language: English
• Subjects: Cross-docking; FMCG supply chain; Meta-Heuristics; Possibilistic chance-constrained programming; Production-inventory-routing problem; Robust possibilistic programming
• ISSN: 0360-8352
• DOI: 10.1016/j.cie.2023.109206

•Presenting a multi-product, and multi-period model for a real Fast-moving consumer goods.•Incorporating internal activities of cross-docks in production-inventory-routing problem.•Considering robust possibilistic programming vs chance-constrained programming.•Evaluating the performance of robust possibilistic models by meta-heuristic algorithms. 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 programming (PCCP) model the demand uncertainty. Uncertainty performance metrics evaluate the proposed solution 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.