Recyclers can derive benefits from horizontal demand information sharing with competitors under specific conditions. However, these advantages may be compromised by the actions of remanufacturers. Information leakage occurs when a remanufacturer selectively discloses information obtained from one recycler to another. This study aims to support recyclers within the reverse supply chain in effectively engaging in horizontal information sharing while mitigating the risk of remanufacturers disclosing proprietary information to competitors, thereby preventing the dissemination of information contrary to the recyclers' intentions for sharing. The research focuses on analyzing the impact of horizontal information sharing and information leakage on the profitability of both remanufacturers and recyclers. An analytical model has been developed based on partial and asymmetric signals of customer valuation. Three scenarios are explored: no information sharing and no leakage, information sharing only, and scenarios involving both sharing and leakage. The novelty of this study lies in its examination of a demand process characterized by distributional uncertainty, which mirrors the informational challenges faced by recyclers entering new markets or expanding into new recycling categories. Recyclers operate with incomplete information and cannot determine whether they possess superior information compared to their competitors. The findings suggest that information sharing among recyclers can enhance the profits of those experiencing high demand but may adversely affect those with lower demand levels. In the absence of horizontal information sharing between recyclers, remanufacturers tend to leak information about higher-demand recyclers to others. Ultimately, managers of competing firms who face uncertainty regarding their information standing should consider sharing information to gain improved demand forecasts or, at minimum, to prevent remanufacturers from exploiting information leakage for personal gain. This refined analysis provides critical insights for stakeholders in the reverse supply chain, highlighting the complex interplay between information sharing and competitive advantage, as well as the strategic importance of managing information flow to safeguard business interests.

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.

A reverse supply chain is viewed as a process from consumption point to recovery point and the management of domestic waste is considered as a specific and complex reverse supply chain. This important sector represents a high challenging problem for our cities, constrained by financial, social, health and environmental considerations. This paper proposes multicriteria decision aid to help choose an efficient domestic waste management strategy. In fact, Multicriteria decision making techniques are considered as a key option to solve this type of problems, giving a solution that represents a good compromise between different preferences. The adopted approach consists in outranking a set of candidate management plans using a method based on partial aggregation criteria. This model is applied on a real case study of an Algerian city and to validate the obtained results, a deep sensitivity analysis is carried out, giving the most appropriate plans.

Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ?-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied.