Publication Type

Master Thesis

Publication Date



This paper analyzes the problem of a remanufacturing news-vendor with selling and take-back price decision. In our model, the remanufacturer decides selling price, take-back price, and order quantity for new materials. She then uses the stochastic take-back quantity and the new material to meet the stochastic demand comparably to a news vendor setting. We allow demand and take-back supply to be correlated. In this thesis, we study a production problem with dual input sources: raw materials and recycled or remanufactured take-back items. To answer when mixed-sourcing is best, we analyze the model under deterministic setting first, provide criteria for different sourcing strategies, and give corresponding joint optimal solutions. Assuming that a mixed strategy is optimal, we then analyze the stochastic case, and find the optimal joint decision for raw-material order quantity, selling product price and take-back price. We find that, when the selling price remains fixed, the optimal takeback price and thus the expected take-back quantity does not change with increased demand and take-back supply variance. Also, the takeback price can exceed the net savings achieved by remanufacturing if consumers take this price into account when purchasing new products. And, the adding of randomness of demand and take-back supply will lower the optimal selling price and thus lower the take-back price. In future research, we will provide numerical analysis to report the impact and performance if a required recycling level is imposed in the problem; study the remanufacture problems with multiplicative demand function; multiple customer classes, such as the trade-in consideration; or multiple order opportunities, such as postponing the raw material procurement.


news-vendor, remanufacturing, pricing, take-back pricing

Degree Awarded

MSc in Operations Management


Operations and Supply Chain Management



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.