title: Chance Constrained Input Relaxation to Congestion in Stochastic DEA. An Application to Iranian Hospitals.
abstract: This article developed an approached model of congestion, based on relaxed combination of inputs, in stochastic data envelopment analysis (SDEA) with chance constrained programming approaches. Classic data envelopment analysis models with deterministic data have been used by many authors to identify congestion and estimate its levels; however, data envelopment analysis with stochastic data were rarely used to identify congestion. This article used chance constrained programming approaches to replace stochastic models with "deterministic equivalents". This substitution leads us to non-linear problems that should be solved. Finally, the proposed method based on relaxed combination of inputs was used to identify congestion input in six Iranian hospital with one input and two outputs in the period of 2009 to 2012.
authors.forename: Hooshang
authors.initials: H
authors.lastname: Kheirollahi
authors.forename: Behzad Karami
authors.initials: BK
authors.lastname: Matin
authors.affiliation: Assistant professor,Ahvaz Jundishapur University of Medical Sciences, Abadan College of Medical Sciences and Health Services, Ahvaz, Iran AND kermanshah university of medical sciences, kermanshah,iran. mmahboubi@kums.ac.ir.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Mahboubi
authors.forename: Mehdi Mirzaei
authors.initials: MM
authors.lastname: Alavijeh
mesh_terms: D017598:Efficiency, Organizational / Q000706:statistics & numerical data*; D006739:Hospital Administration / Q000706:statistics & numerical data*; D006742:Hospital Bed Capacity; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D007492:Iran; D008962:Models, Theoretical; D009874:Operations Research; D011785:Quality Assurance, Health Care / Q000706:statistics & numerical data*; D013269:Stochastic Processes