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Turkey's Aegean region is a very suitable region for fruit production in terms of climatic conditions and soil characteristics. Fruit and vegetable production in the Aegean region has an important position in terms of Turkey's agricultural economy. The fruit growing sector has a very important place in Turkey's agricultural economy. The aim of the study is to prevent unbalanced fruit production in the Aegean region and to ensure more efficient fruit growing. In the study, it was aimed to assign a balanced production of fruits grown under different conditions in provinces with different characteristics in the Aegean region. For the assignment problem, the conditions of the cities in the Aegean region and the growing conditions of the fruits are considered together. These conditions are altitude, temperature, soil quality, climate, humidity, labor and irrigation. Conditions for fruit production were analyzed with the AHP method and factor weights were calculated. ARAS and Fuzzy Logic methods were used to solve the assignment problem of fruits. These methods enable to reach the result by digitizing the non-computable features of a particular situation. Aydın, Manisa, Denizli and Afyonkarahisar provinces have been proposed, respectively, for the production of figs, grapes, thyme and cherries, which have high industrial value and export income. The study has shown that unbalanced production can be prevented by assigning fruits and vegetables in this way in every region of the country. As a result, the country's agricultural lands will be used effectively and the contribution of agriculture to the national economy will increase. [ABSTRACT FROM AUTHOR]

The multi-resource generalized assignment problem (MRGAP) is the assignment of jobs to agents under capacity constraints to minimize the total assignment. In this problem, each agent has multiple resources, each job is assigned to only one agent, and multiple jobs can be assigned to one agent. In the MRGAP problem, it is essential to distribute the loads in balance to the agents. In the literature, for balancing agent loads, bottleneck MRGAP with the aim of minimizing the maximum agent load, and balanced assignment MRGAP models with the aim of minimizing the difference between the maximum agent load and the minimum agent load have been proposed. In this study, the sum of load squares model has been adapted for MRGAP, as well as a new goal programming MRGAP model and a matheuristic algorithm for the solution of this model are developed. The performances of the proposed model and the algorithm were compared with the models taken from the literature by using randomly derived test problems. The quality of the obtained solutions is determined by considering the maximum load, coefficient of variation and total load criteria. The test results are evaluated in terms of these three criteria. The results obtained revealed the success of the proposed goal programming model and the matheuristic algorithm. [ABSTRACT FROM AUTHOR]