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An inverse stochastic two-stage DEA model to deal with resource planning
(AMER INST MATHEMATICAL SCIENCES-AIMS, 2025) Vishghaei, Yasaman Zibaei; Kordrostami, Sohrab; Amirteimoori, Alireza; Shokri, Soheil
Resource planning is a significant aspect in organizations with particularly complex structures and uncertain data. Accordingly, an inverse stochastic network data envelopment analysis (ISNDEA) approach is proposed in this research to estimate the changes of stochastic inputs of two-stage processes for the perturbations of stochastic desirable outputs of stages one and two while the stochastic overall and stage efficiencies are preserved. The introduced ISNDEA model is a stochastic multi-objective optimization problem and it is transformed into a deterministic linear framework. Stochastic undesirable outputs are also included in the investigation due to their presence in many real cases. A real-world examination of the banking sector is presented to show the applicability of the planned technique. The results denote the proposed approach is advantageous to plan the resources of two-stage processes with stochastic data and undesirable outputs.