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PurposeTo develop a multi‐attribute decision making model for evaluating and selecting among logistic information technologies.Design/methodology/approachFirst a multi‐attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented.FindingsReviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone for future business; policy issues as risk and necessity level; resources as costs and completion time. Presents a methodology that is developed for the complex, uncertain and vague characteristics of the problem.Research limitations/implicationsComparisons with other multi‐attribute decision making techniques such as AHP, ELECTRE, PROMETHEE and ORESTE under fuzzy conditions can be done for further research.Practical implicationsThis article is a very useful source of information both for logistic managers and stakeholders in making decisions about logistic information technology investments.Originality/valueThis paper addresses the logistic information technology evaluation and selection criteria for practitioners and proposes a new multi‐attribute decision making methodology, hierarchical fuzzy TOPSIS, for the problem.

Storage and release functions of western U.S. traditional river valley irrigation systems may counteract early and rapid spring river runoff associated with climate variation. Along the Rio Grande in northern New Mexico, we instrumented a 20-km-long irrigated valley to measure water balance components from 2005 to 2007. Hydrologic processes of the system were incorporated into a system dynamics model to test scenarios of changed water use. Of river water diverted into an earthen irrigation canal system, some was consumed by crop evapotranspiration ͑7.4%͒, the rest returned to the river as surface return flow ͑59.3%͒ and shallow groundwater return flow that originated as seepage from canals ͑12.1%͒ and fields ͑21.2%͒. The modeled simulations showed that the coupled surface water irrigation system and shallow aquifer act together to store water underground and then release it to the river, effectively retransmitting river flow until later in the year. Water use conversion to nonirrigation purposes and reduced seepage from canals and fields will likely result in higher spring runoff and lower fall and winter river flow.

E-service evaluation is a complex problem in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Cost-benefit analyses applied to various areas are usually based on the data under certainty or risk. In case of uncertain, vague, and/or linguistic data, the fuzzy set theory can be used to handle the analysis. In this article, after the evaluation attributes of e-services and the fuzzy multi-attribute decisionmaking methods are introduced, a fuzzy hierarchical TOPSIS model is developed and applied to an e-service provider selection problem with some sensitivity analyses. The developed model is a useful tool for the companies that prefer outsourcing for e-activities. It is shown that service systems can be effectively evaluated by the proposed method.

Performance of a faculty is vital both for students and school, and must be measured and evaluated for positive reinforcement to faculty. Faculty performance evaluation problem is a difficult and sensitive issue which has quantitative and qualitative aspects, complexity and imprecision. In literature many different approaches are proposed in order to evaluate faculty performance. To deal with imprecision and vagueness of evaluation measures, fuzzy multi-attribute evaluation techniques can be used. In this paper, a comprehensive hierarchical evaluation model with many main and sub-attributes is constructed and a new algorithm for fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) that enables taking into account the hierarchy in the evaluation model is proposed. The obtained results from this new fuzzy TOPSIS approach are compared with fuzzy Analytic Hierarchy Process (AHP) on an application in an engineering department of a university and some sensitivity analyses are presented.

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