background & purpose: in the health sector, in addition to the important consequence of treatment, health system should meet the expectations regarding the desirability of services. expectations and perceptions of patients about the desirability of services play an important role in selecting hospitals, their loyalty to the organization & replanning to purchase. thus the present study has been ...

A utility function is an important tool for representing a DM’s preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utili...

High utility itemset mining (HUIM) is a new emerging field in data mining which has gained growing interest due to its various applications. The goal of this problem is to discover all itemsets whose utility exceeds minimum threshold. The basic HUIM problem does not consider length of itemsets in its utility measurement and utility values tend to become higher for itemsets containing more items...

nowadays multicriteria decision making (mcdm) methods are a useful tool for investigating natural resources and toaddress problems related to hydro systems. the goal of this research was to assess the daliri standardization method(dsm) to prioritize and make selections for application to mcdm. and to compare the dsm method with the utilityadditive (uta) method. the (dsm) method, unlike the (uta...

Random utility models have been widely used in many diverse fields. Considering utility as a random variable opened many new analytical doors to researchers in explaining behavioral phenomena. Introducing and incorporating the random error term into the utility function had several reasons, including accounting for unobserved variables. This paper incorporates fuzziness into random utility mode...

Nowadays high fuzzy utility based pattern mining is an emerging topic in data mining. It refers to discover all patterns having a high utility meeting a user-specified minimum high utility threshold. It comprises extracting patterns which are highly accessed in mobile web service sequences. Different from the traditional fuzzy approach, high fuzzy utility mining considers not only counts of mob...

Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to m...