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    FACILITY LOCATION DECISION UNDER DEMAND UNCERTAINTY AND TRAVEL TIME FLUCTUATION
    (Univ Belgrade, Fac Transport And Traffic Engineering, 2015) Yildiz, Ecem; Ekinci, Yeliz
    This study solves the facility location problem of an IT service company whose main problem is to arrive at the customer's site in the shortest time when support is needed. This problem should consider both the demand of the customers and the distance and/or travel time between the customers and the company. Travel times and demand are not constant since they are affected by many factors. Therefore, one should take into account uncertainty. This study formulates the problem by considering the demand as fuzzy and travel time as varying based on different time intervals, which are defined hourly, daily and seasonally. Moreover, we consider the cases of minimum speed, average speed and maximum speed for travel time. We illustrate the application of the proposed framework using data of a company. We compare the proposed framework with the traditional distance based optimization approach and show the advantages of the proposed method.
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    A Two-Phase Framework for Facility Location Decision Under Demand Uncertainty and Travel Time Fluctuation
    (Old City Publishing Inc, 2017) Yildiz, Ecem; Ekinci, Yeliz
    Facility location is one of the most important long-term strategic decisions made by any organization. The importance comes from the fact that this decision is given once and it is almost impossible to change later. In this study facility location problem is solved by both subjective and objective methods. Analytical Hierarchy Process, as a subjective method, is used to decrease the number of alternative facility locations in the first phase. In the second phase, a mathematical model is developed as an objective model. This model should consider both the demand of the customers and the distance and/or travel time between the customers and the company. In practice, travel times and demand are not constant since they are affected by many factors. Therefore, one should take into account uncertainty while solving the problem. This study formulates the problem by considering the demand as fuzzy and travel time as varying based on different time intervals, which are defined hourly, daily and seasonally. Thus, all possible conditions of traffic and demand variability are taken into account. Moreover, we consider the cases of minimum speed, average speed and maximum speed for travel time in order to make a sensitivity analysis. We illustrate the application of the proposed framework using data of a company in Istanbul. We compare the proposed framework with the traditional distance based optimization approach and show the advantages of the proposed method.