International Journal of Data Envelopment Analysis and *Operations Research*, 2014, Vol. 1, No. 1, 1-11 Available online at http://pubs.sciepub.com/ijdeaor/1/1/1 © Science and Education Publishing DOI:10.12691/ijdeaor-1-1-1 Measuring Efficiency and Effectiveness for Non-Storable Commodities: A Mixed Separate Data Envelopment Analysis Spproaches with Real and Fuzzy Data Babooshka Shavazipour * Department of Mathematics, Mashhad Branch, Islamic Azad University, Mashhad, Iran *Corresponding author: b.shavazipour@gmail.com Received November 15, 2013; Revised January 12, 2014; Accepted January 21, 2014 Abstract Data Envelopment Analysis (DEA) is a technique for measuring the relative efficiency of Decision Making Units (DMUs) which produce similar products. Measures of both technical efficiency and service effectiveness for storable commodities are essentially the same. However, these measures for non-storable commodities, such as transport services, represent two distinct dimensions and a joint measurement of both or measurement with their impression mutual is necessary to fully capture the overall performance. In this paper, a Mixed Separate Data Envelopment Analysis (MSDEA) approach is introduced to analyze the overall performance of non-storable commodities. Then, the case of ten intercity car companies is described as the application of this novel approach. Moreover, when some observations are fuzzy, the efficiencies and effectiveness become fuzzy as well. For more extension, MSDEA approach with fuzzy observations called Fuzzy Mixed Separate Data Envelopment Analysis (FMSDEA) approach will be presented and illustrated with a numerical example. Keywords: Data Envelopment Analysis (DEA), efficiency, effectiveness, Mixed Separate DEA (MSDEA), fuzzy data Cite This Article: Babooshka Shavazipour, “Measuring Efficiency and Effectiveness for Non-Storable Commodities: A Mixed Separate Data Envelopment Analysis Spproaches with Real and Fuzzy Data.” International Journal of Data Envelopment Analysis and *Operations Research* vol. 1, no. 1 (2014): 1-11. doi: 10.12691/ijdeaor-1-1-1. 1. Introduction Data Envelopment Analysis (DEA) is a technique for measuring the relative efficiency of Decision Making Units (DMUs) which produce similar products. Measures of both technical efficiency (a transformation of factors to production) and service effectiveness (consumption of production) for storable commodities are essentially the same because of the commodities, once produced, can be stockpiled until consumed. Nothing will be lost throughout the transformation from production to consumption if one assumes that all the stockpiles are eventually sold, there is no storage cost, and there is no loss incurred. Namely, conventional measures for storable commodities assume perfect sale and no storage cost effectiveness. However, technical efficiency and service effectiveness for non-storable commodities, such as transport services, represent two distinct measurements because one can never store the surplus service during periods of low demand (off peak hours) for use during periods of high demand (peak hours). When such non- storable commodities are produced and a portion of which are not concurrently consumed, the technical effectiveness (a joint effect of both technical efficiency and service effectiveness) would be less than the technical efficiency. Over the past three decades, various DEA models have been widely used to evaluate the technical efficiency or technical effectiveness of DMUs in different organizations or industries. In transport performance evaluation, numerous applications of DEA have also been found in various fields. In order to completely and fairly evaluate the relative performance of non-storable transport services, several recent works have employed various DEA approaches to evaluating the efficiency and effectiveness. In general, they can be divided into five categories: separate DEA model (hereinafter, SDEA; e.g. [1,2]), separate two-stage DEA model (hereinafter, STDEA; e.g. [3,4,5]), network DEA model (hereinafter, NDEA; e.g. [6,7,8]), integrated two-stage DEA model (hereinafter, ITDEA; e.g. [9,10,11]), and integrated DEA model (hereinafter, IDEA; e.g. [12]). The SDEA employs independent DEA models to measure technical efficiency, service effectiveness, and technical effectiveness separately. Hence, paradoxical improvement strategies were usually generated based on the results of these independent DEA models. To overcome this shortcoming, the STDEA uses an input- oriented DEA model to evaluate the technical efficiency and an output-oriented DEA model to assess the service effectiveness, holding the output level unchanged. Although the STDEA model will not generate conflicting improvement strategies, it suggests the organization be