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This paper presents the application of information gap decision theory (IGDT) to help the distribution network operators (DNOs) in choosing the supplying resources for meeting the demand of their customers. The three main energy resources are pool market, distributed generations (DGs), and the bilateral contracts. In deregulated environment, the DNO is faced with many uncertainties associated to the mentioned resources which may not have enough information about their nature and behaviors. In such cases, the classical methods like probabilistic methods or fuzzy methods are not applicable for uncertainty modeling because they need some information about the uncertainty behaviors like... 

In this research, Repetitive Group Sampling (RGS) plans are developed for the Weibull and generalized exponential distributions. To design the proposed plans, the median of a life-time is first used as the quality parameter. Then, a decision-making framework is developed, based on first and second type errors. Next, based on acceptable and limiting quality level criteria, tables are obtained to select the parameters of the proposed decision-making framework. The advantages of the proposed method over single sampling plans are discussed at the end  

Subcontractor (SC) selection is a critical part of SC management, and a major challenge is the existence of multiple criteria that the project management team needs to evaluate in the selection process. To overcome this challenge, the Kano model is applied to classify the selection criteria into five categories including must-be, reverse, one-dimensional, attractive, and indifferent. Data were obtained via a questionnaire based on Kano model concepts to categorize the identified selection criteria. The obtained results show that the selection criteria can be categorized into three categories; must-be, one-dimensional, and attractive. According to this categorization, the weight of criteria... 

In order to design a decision-making framework in production environments, in this study, we use both the stochastic dynamic programming and Bayesian inference concepts. Using the posterior probability of the production process to be in state λ (the hazard rate of defective products), first we formulate the problem into a stochastic dynamic programming model. Next, we derive some properties for the optimal value of the objective function. Then, we propose a solution algorithm. At the end, the applications and the performances of the proposed methodology are demonstrated by two numerical examples. © 2007 Asian Network for Scientific Information