## 1 Introduction
In MODM problems, more than one objective is considered in regard to the opinions of the decision maker (DM). Interactive methods (IMs) are a kind of MODM and MOLP methods. IMs explore the criterion space on the progressive definition of the DM's preferences at each iteration [23]. IMs have been used in some reported works [24; 25]. Traditional DEA models tend to ignore the DM's preferences and value judgment in the computation of the DMU targets, completely. The use of IMs allows the obtainment of DMU targets which have perfect adaptation for the DM's preferences [24]. An IM was applied for the extension of DEA to effectiveness analysis [26]. The step method (STEM) that was introduced in 1971 [27] is an IMs in MOLP, and has been reported in several studies [28; 29; 30; 31]. To the best of our knowledge, STEM has not yet been used by researchers to find targets in non-radial FDH models which consider ASBM and enhanced Russel measures in variable return to scale technology (the first research gap). Multiple criteria decision making (MCDM) is divided into multiple attribute decision making (MADM) and MODM. In situations where the data are fuzzy, a combination of DEA and fuzzy MCDM [32; 33; 34] can be used for the development of the proposed technique. In this study, as the data of the second case study were deterministic, it was not necessary to use fuzzy methods. FDH is a well-known subclass of DEA models and is based on two distinctive features that are reflected in the PPS boundary. First, FDH ensures that efficiency evaluations are affected only by actually observed performances. Secondly, FDH relies on the non-convexity assumption which satisfies free disposability in PPS. There is inherent computational complexity to solve FDH models. As a matter of fact, FDH models are mixed 0-1 LP, and solving them is difficult. In this regard, a ratio-based approach (RBA) is proposed to solve radial FDH models without solving any mathematical programming models [13]. This approach has been employed by some other studies [15; 20]. To the best of our knowledge, RBA is one of the most suitable suggested methods to find DMU targets of radial FDH models without solving any mathematical programming models. DMU target finding of non-radial FDH models without solving any mathematical programming models can be considered as another research gap. It can be achieved by extending RBA. As ASBM relates to enhanced Russel measures, finding non-radial DMU targets has been possible using extended RBA. In Fars province pharmaceutical distributing companies (the second case study), there was a variable return to scale assumption. Moreover, as the combination of pharmaceutical distributing companies was impossible, using FDH models was beneficial. Non-radial FDH models based on ASBM in variable return to scale technology can therefore be considered in the proposal for a technique for finding DMU targets. Therefore, considering mentioned research gaps, two research questions have considered as follows:  
1. Is it possible to propose a technique to find all DMU targets in non-radial FDH models based on ASBM using IM? 2. Is it possible to find DMU targets of non-radial FDH models in the proposed technique without solving any mathematical models? A hybrid technique to answer the above two research questions with the following properties has been proposed as the innovation of this research:  
1. DMU target finding in non-radial FDH models based on ASBM are more realistic because they are based on a non-convexity assumption,
2. Proposing a new LP formulation of ASBM,
3. Applying IMs instead of regular DEA methods to find FDH models targets which have more adaptation to the DM's preferences,
4. Finding the required DMU targets in FDH models using an algorithm that works by checking some conditions for DMUs without solving of any mathematical models. To the best of our knowledge, proposing a hybrid technique with the mentioned properties has not been reported until now.