Open AccessMulti-Objective Faculty Course Assignment Problem with Result and Feedback Based Uncertain Preferences
Author(s) -
Sunil B. Bhoi,
Jayesh M. Dhodiya
Publication year - 2021
Publication title -
international journal of mathematical, engineering and management sciences
Language(s) - English
Resource type - Journals
ISSN - 2455-7749
DOI - 10.33889/ijmems.2021.6.4.062
Subject(s) - mathematical optimization , ranking (information retrieval) , transformation (genetics) , sensitivity (control systems) , exponential function , computer science , linear programming , uncertainty theory , function (biology) , pareto principle , bellman equation , fuzzy logic , mathematics , variation (astronomy) , artificial intelligence , mathematical analysis , biochemistry , chemistry , electronic engineering , evolutionary biology , biology , engineering , gene , physics , astrophysics
In this paper, a multi-objective faculty course allocation problem with result analysis and feedback analysis based on uncertain preferences mathematical model is presented. To deal with an uncertain model, three different ranking criteria are being used to develop: a) Expected value, b) Optimistic value, c) Dependent optimistic value criterion. These mathematical models are transformed into their corresponding deterministic forms using the basic concepts of uncertainty theory. The deterministic model of DOCM consists of fractional objectives which are converted into their linear form using Charnes and Cooper’s transformation. These deterministic formulations MOFCAP are converted into a single objective problem by using the fuzzy programming technique with linear and exponential membership functions. Further, the single objective problem for all the defined models is solved in the Lingo 18.0 software to derive the Pareto-optimal solution. The sensitivity of the models is also performed to examine the variation in the objective function due to the variation in parameters. Finally, a numerical example is given to exhibit the application and algorithm of the models.