Implementation of fuzzy goal programming approach to automobile production in Turkey

The general aim of the various methods that the steering research has is to bring the problem at hand to the best result. However, it is not possible to mention many problems encountered in everyday life as a result of the rapid change that takes place today. For this reason, multi-purpose decision making models have been developed. Providing effective solutions within the developed models; the goal programming model could be the first model that sounds believeble. The target programming model requires fuzzy set theory if the problem is ambiguity or blurring at parameter values and boundaries. In the target programming model, it is difficult to precisely determine the preference order and weighting of the access values related to the targets of the targets. The fuzzy target programming model is used when access values, goals and decision makers are ambiguous. Fuzzy Linear Programming; It enables modeling in uncertain situations and provides flexible results. In the Fuzzy Goal Programming model, the components of purpose and decision criteria that define each other are defined as 'Fuzzy Target Function'. In the model, the parameter values of the restrictors describing the events are expressed as 'fuzzy restrictors'. Accepted values for deviations in the access values determined by the decision-maker for blurred targets in fuzzy target programming are called tolerance values. In this study by taking 12 company engaged in automobile production in Turkey established a model with fuzzy goal programming approach and this model has been solved. The aim of the model is to find the value of the targets set for 2017 using the data for 2012-2016 years. In the world, about 50 companies operate in the automotive sector, which is at the forefront of sectors that require intensive capital and technology investment. At the beginning of the companies that have the bet is the USA, Japan, Germany, France, Italy and England. Automotive production in Turkey remains a wavy structure. Due to the regular follow-up of domestic and foreign demand conditions, the sector has a significant impact on production levels. There are automobile-weighted structure in automotive production in Turkey. In the study, for Turkey in 2017 to maximize the profits of the automobile manufacturing companies, find out the number of people who will work for the production of a car and constitute the objective function is maximum demand. In such cases, multi-purpose programming can be used when there is more than one objective function in the model. In addition, fuzzy objective programming approach can be used if the objective function values are deviations, in other words if there are tolerance amounts. Automotive companies have distributed logistics and cost concerns to different countries around the world. As a result, in addition to the design and development stages, many processes that require advanced technology continue to remain in developed countries, and they have brought assembly processes to developing countries in terms of their proximity to cheap labor and possible market options. As the people in the developing countries begin to develop economically, it seems that the demand for automobiles in the developing countries is increasing. The standard deviations of the relevant variables of the dataset for targets created in this study are determined as the amount of tolerance. In addition, production capacities and minimum production numbers in the model were used as non-blurring constraints. The model based on the fuzzy targets and the non-blurred constraints is called "Tiwari, Dharmar and Rao", "Chen and Tsai", "Zimmermann", "Yang, Ignizio and Kim", "Kim and Whang", "Wang and Fu" and "Fu (square)" and "Chen" approaches. The results of the approaches used to solve the model were found to be the same or very close to each other. This is why all the algorithms discussed have the same membership functions. The results of the model are an estimate for the year 2017. According to the year of 2017values, it is seen that there is not much differnces between the values estimated between the years 2012 and 2016.