A stochastic approach for integrated production and distribution planning in dairy supply chains

A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry

Considering supply chain efficiency during the network design process significantly affect chain performance improvement. In this paper, the design process of a sustainable lead-acid battery supply chain network was addressed. Because the design of such networks always involves great computational complexity, in the present study, a two-stage model was proposed to overcome this issue. In the first stage, candidate sites of recycling centers were identified using data envelopment analysis (DEA) and based on their efficiency scores. Unlike the previous studies, not only economic criteria but also technical and geographical criteria were employed to select these locations. In the second stage, a bi-objective programming model was developed to simultaneously determine the tactical and strategic decisions of the chain. Since some data was subject to uncertainty, a robust possibilistic approach was presented. The model ensures that the resulting structure for the chain will be robust to noise and disturbance in parameters. A life cycle assessment model based on the ReCiPe 2008 method was developed in SimaPro software. To evaluate the applicability of the presented method, a case study in the automotive industry was used. The results of implementing the DEA method showed that from among 23 available locations, 11 potential places were selected for construct recycling centers. The final results showed that the inappropriate potential locations of recycling centers were eliminated, and the complexity of the mathematical model proposed in the second stage was reduced. The obtained results of environmental protection costs revealed that this criterion changed from 0 to 8,333,874,332. Moreover, the first objective function resulted in a centralized network to minimize costs, and in contrast, the second objective function tended to decentralize the network to minimize environmental impacts.