Abstract: The efficient management of reverse supply chains, particularly the collection and remanufacturing of defective products, plays a critical role in reducing production costs and determining the final pricing of remanufactured products. While existing research extensively explores warranty policies and maintenance services to enhance customer satisfaction and profitability, the integration of vehicle routing for product collection and sustainability advertising strategies remains underexplored. Addressing this gap, this study introduces a comprehensive two-stage reverse supply chain model that captures the interactions between manufacturers (MFRs) and remanufacturers (RMFRs) through a Stackelberg game framework. Methods: The proposed model incorporates interactive production constraints, vehicle routing problem (VRP) for optimizing collection logistics, and sustainability advertising to influence consumer behavior towards remanufactured products. Utilizing mixed nonlinear programming (MINLP) and nonlinear programming (NLP) techniques, the model simultaneously optimizes pricing strategies, collection efforts, and advertising investments for both MFRs and RMFRs. Numerical analyses are conducted to solve the optimization problems, accompanied by sensitivity analyses to evaluate the impact of key parameters such as production costs, defect rates, and routing constraints. The numerical results demonstrate that increases in production costs for MFRs lead to higher selling prices, thereby reducing their profit margins and negatively impacting RMFR profitability due to decreased demand for remanufactured products. Sensitivity analysis reveals that higher defect rates (α ≥ 0.8) significantly diminish overall supply chain profitability by lowering customer acceptance of RMPs. Additionally, expanding the allowable vehicle routing distance L effectively reduces collection costs, enhancing RMFR profits and enabling greater investment in sustainability advertising. The study shows that the integration of VRP and advertising strategies proves crucial in balancing cost efficiencies and market competitiveness, ultimately fostering a more sustainable and profitable reverse supply chain.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This paper presents a new integrated framework combining the Joint Replenishment Problem (JRP) with a generalized Vendor Managed Inventory (VMI) system. The model under consideration represents a three-level supply chain consisting of a supplier, manufacturer, and retailer. The model incorporates multiple product types, each produced on a dedicated machine at the manufacturer, subject to setup costs, and major and minor ordering costs. The primary objective of this research is to optimize a set of critical decision variables, including the common order interval, order frequencies for each item, backorder levels at the retailer, and production initiation times at the manufacturer for each product type, under both deterministic and stochastic demand scenarios. This analysis will provide valuable insights for improving joint replenishment operations in manufacturing. The research begins with a deterministic model fit for the particular issue area derived from the canonical JRP. Within a VMI context, the manufacturer, acting as the supply chain leader, utilizes shared information to derive initial feasible solutions. Subsequently, an optimization technique is employed, combining marginal cost-based and cumulative cost-based algorithms, while leveraging embedded discrete Markov chain decomposition method adapting Jacobi stepping method to determine steady-state probabilities. A cost function is then defined for each action state within this framework. The integration of the VMI policy into the JRP model can significantly reduce the whole cost of the supply chain, through balancing between production initiation and backorders under both the deterministic and stochastic models.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)