Development of a Mobile Decision Support System Based on the Smart Method for Android Platform

The work is devoted to the development of a mobile decision support system for solving the multiple criteria decision-making problems. To ensure the autonomous operation of the system, it was proposed to use a three-layer architecture. For reuse and distribution of the code, this model is implemented in three levels: presentation level, application level and data level. The development of the application level in the developed mobile decision support system involves the creation of three subsystems: a decision-making subsystem, a database interaction subsystem and a message management subsystem. At the core of the decision-making subsystem of the developed mobile decision support system, an improved Smart method was chosen. This method differs from the classical Smart method in that the decision maker uses the elements of the decision matrix as estimates of each alternative for all criteria. Also, the nature of actions on the criteria (maximization or minimization) is taken into account. This, in turn, takes into account the normalization of elements of the decision matrix. The startup of the database interaction subsystem, which is responsible for transferring and retrieving data to/from the database, occurs via the user interface. To create the database, the SQLite relational database management system was used. SQLite stores the entire database (including definitions, tables, indexes, and data) in one standard file on the device on which the application runs. The message management subsystem allows the decision maker to send the calculation results via the Internet or using the short message service (SMS). The mobile decision support system has been developed in Java in Android Studio 3.2.1. The task of buying a smartphone was considered, as an application of the developed mobile decision support system.

[1]  Ghalem Belalem,et al.  The Design of a Cloud-based Clinical Decision Support System Prototype: Management of Drugs Intoxications in Childhood , 2018, Int. J. Heal. Inf. Syst. Informatics.

[2]  Xiang Guo,et al.  Mobile Decision Support System Usage in Organizations , 2013, AMCIS.

[3]  E. J. Ha,et al.  Implementation of Korean Clinical Imaging Guidelines: A Mobile App-Based Decision Support System , 2018, Korean journal of radiology.

[4]  Daniel J. Power,et al.  Web-Based and Model-Driven Decision Support Systems: Concepts and Issues , 2000 .

[5]  Mouzhi Ge,et al.  Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm , 2017, J. Sens. Actuator Networks.

[6]  Natalya Volkova,et al.  Mobile Application for Decision Support in Multi-Criteria Problems , 2018, 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP).

[7]  Chong Feng,et al.  An Empirical Study of Investigating Mobile Applications Development Challenges , 2018, IEEE Access.

[8]  Abbas Mardani,et al.  Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014 , 2015 .

[9]  Melih Iphar,et al.  A mobile application based on multi-criteria decision-making methods for underground mining method selection , 2019 .

[10]  Enrique Herrera-Viedma,et al.  A Mobile Decision Support System for Dynamic Group Decision-Making Problems , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  Vassilis Kostoglou,et al.  Design and development of an interactive mobile-based decision support system for selecting higher education studies , 2017 .