Stochastic dynamic production planning in hybrid manufacturing and remanufacturing system with random usage durations

ABSTRACT

This paper studies a multi-period dynamic production planning problem in a hybrid manufacturing and remanufacturing system (HMRS), where new and remanufactured products are perfect substitutes. The HMRS encounters uncertain return amounts due to products' random usage durations. As returns are from previous sales, production planning in each period impacts both current sales and future returns. The random return amount and the correlation among different periods make it a complex system. To solve the problem, we first utilise the hazard rate function and in-use products' information to derive an estimator of the return amount. Then, we formulate a dynamic programming model and prove a threshold policy is optimal under uniformly distributed demand. We employ marginal analysis to derive an approximation of the optimal threshold value. Through simulating all alternatives, the derived threshold is verified to be a good approximation as it achieves more than 99% of the optimal revenue in most scenarios. In addition, the calculated return amount based on the hazard rate function is almost identical to the return amount obtained via simulation. Compared to other return measurements, our method achieves the highest revenue in all considered scenarios, including heterogeneous usage durations and general demand distributions.

KEYWORDS:

• Remanufacturing
• dynamic programming
• random usage duration
• hazard rate function
• production planning

Data availability statement

The author confirms that the data supporting the findings of this study are available within the article and its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by China Scholarship Council (CSC).

Notes on contributors

Zhenxia Cheng

Zhenxia Cheng is currently a Ph.D. candidate in Management Science and Engineering at Shanghai Jiao Tong University. Her research includes stochastic modelling, dynamic programming, and simulation optimisation.