Optimizing Technology R&D Supply Chain Problem Under Technology Concern Uncertainty

Technology research and development (R&D) plays an increasingly critical role in emerging supply chains by endowing products with desirable technical features. Due to unpredictable market acceptance, the technology concern coefficient, namely, the sensitivity of price to the technology level of technological products is uncertain in the process of R&D, and usually only its partial distribution information can be obtained by referring to similar products or resorting to experts in related fields. It reflects the subjective uncertainty of technology concern, which is different from the existing relevant literature. Motivated by this challenge, we construct a new ambiguity set of possibility distributions to characterize the distribution uncertainty of technology concern and build a novel robust fuzzy tri-level optimization model for decision-making in technology R&D supply chain (tRDsc) with government subsidy. Based on the proposed ambiguity set, we have derived the closed-form equilibrium solutions to the developed robust optimization model. At the same time, the equilibrium under manufacturer subsidy is consistent with that under consumer subsidy. Besides, manufacturer subsidy is more beneficial to the supplier, while technology supplier subsidy benefits the manufacturer more when the subsidy ratio is big enough. Finally, an application case about electric vehicles is studied to illustrate the effectiveness of our new optimization method. The case study shows that the government's subsidy strategy may change when the uncertainty perturbation parameters change. That is, taking investment efficiency as the selection criterion, the change of perturbation set will influence subsidy policy choices.

Automated summary

Technology research and development (R&D) in emerging supply chains is crucial with uncertainty. Researchers constructed a new ambiguity set to characterize distribution uncertainty of technology concern and developed a decision-making optimization model, studying a case on electric vehicles to show the impact of uncertainty on government subsidy strategies.