A municipality improves the quality of community life through its projects and actions. However, project selection and prioritization by municipalities are highly complex processes. Therefore, multicriteria decision making (MCDM) methodologies are very suitable for determining the best alternative. Recently, some studies have concentrated on the selection of the best project alternatives. In this paper, a two phased fuzzy MCDM methodology is proposed for the selection among municipal projects. In the first phase, fuzzy TOPSIS method is used to select the main project group and then fuzzy AHP is used to select the best sub-municipal project. The application of the suggested methodology has been made at the central district municipality in Konya, Turkey. , quality management and control, multicriteria decision making, and fuzzy sets applications.
M. E. Baysal et al. A two phased fuzzy methodology for selection among municipal projects
Energy has been an essential factor in determining the governments' policies. The countries had to produce their own energy to decrease dependency on external resources. That also provided to gain a great importance on investment on power plants. In this study, a multi-objective Mixed-Integer Linear Programming (MILP) model synchronously optimizing five targets determined as decreasing carbon dioxide (CO 2 ) emission, increasing power consumption, increasing power plants, increasing energy generation and installed capacity was used. In described model, it was solved by considering renewable power plants in Turkey and fossil fuel-based power plants having most share in Turkey. By trying to minimize deviation values of Turkey's 2023 targets, it aimed to determine which power plants need to be increased. To determine the priorities of these targets, Ranking Approach for fuzzy numbers by Liou and Wang (1992) was used. Besides, Fuzzy Analytic Hierarchy Process (AHP) was used to prioritize investment planning of renewable power plants in Turkey and five different kinds of power plants under the visual pollution criteria based on amount of CO 2 emission released, environmental damage, capital costs, space requirement and provided employment were evaluated.
The distributed permutation flow shop scheduling (DPFSS) is a permutation flow shop scheduling problem including the multi-factory environment. The processing times of the jobs in a real life scheduling problem cannot be precisely know because of the human factor. In this study, the process times and due dates of the jobs are considered triangular and trapezoidal fuzzy numbers for DPFSS environment. An artificial bee colony (ABC) algorithm is developed to solve the multi-objective distributed fuzzy permutation flow shop (DFPFS) problem. First, the proposed ABC algorithm is calibrated with the well-known DPFSS instances in the literature. Then, the DPFSS instances are fuzzified and solved with the algorithm. According to the results, the proposed ABC algorithm performs well to solve the DFPFS problems.
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