Browsing by Author "Guldogan, Evrim Ursavas"

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Citation - WoS: 5
Citation - Scopus: 8
A Dynamic Berth Allocation Problem with Priority Considerations under Stochastic Nature
(SPRINGER-VERLAG BERLIN, 2012) Evrim Ursavas Guldogan; Onder Bulut; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Guldogan, Evrim Ursavas; Bulut, Onder; D Huang; Y Gan; P Gupta; MM Gromiha
Stochastic nature of vessel arrivals and handling times adds to the complexity of the well-known NP-hard berth allocation problem. To aid real decision-making under customer differentiations a dynamic stochastic model designed to reflect different levels of vessel priorities is put forward. For exponential interarrival and handling times a recursive procedure to calculate the objective function value is proposed. To reveal the characteristics of the model numerical experiments based on heuristic approaches are conducted. Solution procedures based on artificial bee colony and genetic algorithms covering both global and local search features are launched to improve the solution quality. The practical inferences led by these approaches are shown to be helpful for container terminals faced with multifaceted priority considerations.
Citation - WoS: 7
Citation - Scopus: 9
An integrated approach to machine selection and operation allocation problem
(Springer London Ltd, 2011) Evrim Ursavas Güldogan; Guldogan, Evrim Ursavas
Machine selection and operation allocation is a multi-criteria decision-making problem which involves the consideration of both qualitative and quantitative factors. Thus a hybrid model integrating the knowledge-based expert system and the genetic algorithm may be effectively applied to the decision problem. This paper proposes a two-step approach where suitable machines for every operation in a work center is selected and optimized as a whole to obtain the optimum machine park. The first step of the model determines the suitability of each machine type for every operation using the knowledge-based expert system. The second stage searches through the solution space to find the optimal machine park with the use of a genetic algorithm. A real-life case study at an outdoor advertisement manufacturing company demonstrates the applicability of the model. © 2010 Springer-Verlag London Limited. © 2011 Elsevier B.V. All rights reserved.