
Remanufacturing is becoming an important activity due to increasing awareness among societies to protect the environment and reduce waste. Remanufacturing is one of the candidates at the materials end of life (EOL) for bringing used products to a “like-new” state, and an effective way to maintain them in a closed-loop system for reducing environmental impacts. Among various factors, profit and cost are important criteria in remanufacturing process to retain competitive advantage of remanufactured products in the market and should be controlled constantly, efficient and sustainable remanufacturing process is also another significant factor which should be considered. Because of unknown condition of returned products, there are often more uncertainties along with remanufacturing in comparison with manufacturing and defining appropriate measures to anticipate operational results is necessary. In this paper a new model is developed with the purpose of optimizing remanufacturing process by applying stochastic multi-objective goal programming considering profit and cost. The output of the model can be used to decide about the ratio of products which should be ordered for remanufacturing process. Finally, the whole approach is illustrated with an example for demonstrating the validity of the proposed model.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 15 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
