a multi-objective leagile demand-driven optimization model incorporating a reliable omnichannel retailer: a case study

This research proposed a comprehensive model designed for the optimization of supply chain networks, particularly emphasizing leagile demand-driven systems within the context of omnichannel operations. the proposed model integrates various parameters such as total cost, lead time, service level, and residual capacity, addressing the complex interdependencies among an omnichannel environment of retailers. to enhance the model's reliability, a hybrid meta-heuristic algorithm is employed, combining the strengths of moea/d-de (multi-objective evolutionary algorithm with differential evolution), ibea (indicator-based evolutionary algorithm), and nsga-ii (non-dominated sorting genetic algorithm ii). the collaborative optimization approach ensures adaptability and efficiency in addressing diverse and intricate optimization challenges inherent in omnichannel networks. the numerical data from a case study on the supply of sanitary masks in tabriz, iran, during august 2021 is utilized to validate the model within the specific omnichannel context. the study includes a thorough sensitivity analysis, demonstrating the model's robustness against disturbances in the omnichannel network. the consistent performance of the odel across various disruption scenarios underscores its reliability and efficacy in ensuring the stability of supply chain operations within omni-channel frameworks. this observed resilience significantly enhances the overall robustness of the supply chain, especially when confronted with disruptive events. the model's ability to maintain stability under diverse conditions contributes to fortifying the supply chain against potential disruptions, thereby augmenting its adaptive capabilities in dynamic environments..managerial and practical implications are discussed, emphasizing the significance of the proposed reliable omnichannel approach in leagile demand-driven systems.