Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942

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  • Book Part
    Customer Segmentation Using Rfm Analysis: Real Case Application on a Fuel Company
    (Emerald Publishing Limited., 2020-12-04) Ucal Sarı, İrem; Sergi, Duygu; Ozkan, Burcu
    Customer segmentation is an important research area that helps organizations to improve their services according to customer needs. With the increased information that shows customer attitudes, it is much easier and also more necessary than before to analyze customer responses on different campaigns. Recency, frequency, and monetary (RFM) analysis allows us to segment customers according to their common features. In this chapter, customer segmentation and RFM analysis are explained first, then a real case application of RFM analysis on customer segmentation for a Fuel company is presented. At the end of the application part, possible strategies for the company are generated.
  • Conference Object
    Citation - Scopus: 28
    Fuzzy Capital Budgeting Using Fermatean Fuzzy Sets
    (Springer, 2020-07-11) Sergi, Duygu; Sarı, İrem Ucal
    Investment projects are mostly evaluated by capital budgeting techniques to measure their profitability. The parameters used in capital budgeting such as future cash flows, interest rate and useful life involves high uncertainty due to the lack of information for the future environment. Since the uncertainty involved in forecasting the parameters is mostly in high levels, fuzzy set theory could be used in the determination of capital budgeting parameters to handle uncertain information in the analyses. Fermatean fuzzy sets are one of the most recent extensions of fuzzy set theory which are capable to handle higher levels of uncertainties by assigning fuzzy parameters from a larger domain. In this paper, fuzzy capital budgeting techniques that are fuzzy net present worth, fuzzy net future worth and fuzzy net annual worth are extended using fermatean fuzzy sets. An illustration for the calculations is also presented.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 18
    Gradual Covering Location Problem With Multi-Type Facilities Considering Customer Preferences
    (Elsevier, 2020-09-01) Küçükaydın, Hande; Aras, Necati
    In this paper, we address a discrete facility location problem where a retailer aims at locating new facilities with possibly different characteristics. Customers visit the facilities based on their preferences which are represented as probabilities. These probabilities are determined in a novel way by using a fuzzy clustering algorithm. It is assumed that the sum of the probabilities with which customers at a given demand zone patronize different types of facilities is equal to one. However, among the same type of facilities they choose the closest facility, and the strength at which this facility covers the customer is based on two distances referred to as full coverage distance and gradual (partial) coverage distance. If the distance between the customer location and the closest facility is smaller (larger) than the full (partial) coverage distance, this customer is fully (not) covered, whereas for all distance values between full and partial coverage, the customer is partially covered. Both distance values depend on both the customer attributes and the type of the facility. Furthermore, facilities can only be opened if their revenue exceeds a certain threshold value. A final restriction is incorporated into the model by defining a minimum separation distance between the same facility types. This restriction is also extended to the case where a minimum threshold distance exists among facilities of different types. The objective of the retailer is to find the optimal locations and types of the new facilities in order to maximize its profit. Two versions of the problem are formulated using integer linear programming, which differ according to whether the minimum separation distance applies to the same facility type or different facility types. The resulting integer linear programming models are solved by three approaches: commercial solver CPLEX, heuristics based on Lagrangean relaxation, and local search implemented with 1-Add and 1-Swap moves. Apart from experimentally assessing the accuracy and the efficiency of the solution methods on a set of randomly generated test instances, we also carry out sensitivity analysis using a real-world problem instance.
  • Conference Object
    Combining Acceleration Techniques for Pricing in a Vrp With Time Windows
    (2016) Michelini, S; Arda, Y; Küçükaydın, Hande
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  • Conference Object
    The Traveling Salesman Problem With Time-Dependent Service Times
    (2016) Taş, Duygu
    This paper introduces a version of the classical traveling salesman problem with time-dependent service times. In our setting, the duration required to provide service to any customer is not fixed but defined as a function of the time at which service starts at that location. The objective is to minimize the total route duration, which consists of the total travel time plus the total service time. The proposed model can handle several types of service time functions, e.g., linear and quadratic functions. We describe basic properties for certain classes of service time functions, followed by the computation of valid lower and upper bounds. We apply several classes of subtour elimination constraints and measure their effect on the performance of our model. Numerical results obtained by implementing different linear and quadratic service time functions on several test instances are presented.
  • Article
    Determining the Most Vulnerable Components in a Transportatıon Network
    (Yıldız Technical University, 2018) Küçükaydın, Hande; Aras, Necati
    Transportation networks belong to the class of critical infrastructure networks since a small deterioration in the service provision has the potential to cause considerable negative consequences on everyday activities. Among the reasons for the deterioration we can mention the shutdown of a subway station, the closure of one or more lanes on a bridge, the operation of an airport at a much reduced capacity. In order to measure the vulnerability of transportation network, it is necessary to determine the maximum possible disruption by assuming that there is an intelligent attacker wishing to give damage to the components of the network including the stations/stops and linkages. Identifying the worst disruptions can be realized by using interdiction models that are formulated by a bilevel mathematical programming model involving two decision makers: leader and follower. In this paper, we develop such a model referred to as attacker-operator model, where the leader is a virtual attacker who wants to cause the maximum possible disruption in the transportation network by minimizing the amount of flow among the nodes of the network, while the follower is the system operator who tries to reorganize the flow in the most effective way by maximizing the flow after the disruption. The benefit of such a model to the system operator is to determine the most vulnerable stations and linkages in the transportation network on one hand, and to take precautions in preventing the negative effects of the disruption on the other hand.