In this paper, we propose novel mathematical models involving both single‐ and biobjective functions that deal with a flexible job shop scheduling problem in cellular manufacturing environment by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence‐dependent family setup times, and recirculation. The problem has been known as NP‐hard. The proposed models have been tested and solved using Lingo 11.0 with minimization of makespan for the problems involving about 4 cells, 4 part families, 15 parts, and 12 machines. The most suitable model among the proposed single‐objective models is determined using the test results. Then, another objective function as total tardiness is added to this model. The obtained biobjective model is solved using the scalarization methods, the weighted sum method, ɛ‐constraint method, and conic scalarization method (CSM), in order to convert the mathematical model's objectives into a single‐objective function. By utilizing these scalarization methods, the Pareto effective solutions are generated for a specific test problem. The advantages of the CSM are demonstrated by considering the Pareto effective solutions.

Student selection is a multicriteria decision-making problem that includes both tangible and intangible factors.In these problems if educational institutions have budget or other different constraints, two problems will exist: which students are the best and how students are assigned to the predefined programs? In this study, an integrated approach of fuzzy MULTIMOORA and multichoice conic goal programming is proposed to consider criteria in choosing the best students and define the optimum assignments among the predefined programs to maximize both the total preference value and total ranking value. The rankings of the students are determined by using fuzzy MULTIMOORA. The rankings of candidates are set as the parameters of the first objective function. The placement preferences of the students according to the predefined programs are considered in the second objective function. The candidates are assigned to their placement preferences both by using multichoice conic goal programming among partner universities according to the objectives and by considering the budget and quota.

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