Browsing by Author "Fadiloglu, M. Murat"

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Citation - WoS: 3
Citation - Scopus: 1
A differential evolution algorithm for the median cycle problem
(IEEE, 2011) M. Fatih Tasgetiren; Quanke Pan; Önder Bulut; Ponnuthurai Nagaratnam Suganthan; Tasgetiren, M. Fatih; Suganthan, P. N.; Pan, Quan-Ke; Bulut, Onder; Fadiloglu, M. Murat
This paper extends the applications of differential evolution algorithms to the Median Cycle Problem. The median cycle problem is concerned with constructing a simple cycle composed of a subset of vertices of a mixed graph. The objective is to minimize the cost of the cycle and the cost of assigning vertices not on the cycle to the nearest vertex on the cycle. A unique solution representation is presented for the differential evolution algorithm in order to solve the median cycle problem. To the best of our knowledge this is the first reported application of differential evolution algorithms to the median cycle problem in the literature. No local search is employed in order to see the performance of the pure differential evolution algorithm. The differential evolution algorithm is tested on a set of benchmark instances from the literature. For comparisons a continuous genetic algorithm is also developed. The computational results show that the differential evolution algorithm was superior to the genetic algorithm. In addition the computational results also show that the differential evolution algorithm is very promising in solving the median cycle problem when compared to the best performing algorithms from the literature. Ultimately given the fact that no local search is employed the DE algorithm was able to further improve the 5 out of 20 instances. © 2011 IEEE. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 15
A Discrete Artificial Bee Colony Algorithm For the Economic Lot Scheduling Problem
(IEEE, 2011) M. Fatih Tasgetiren; Onder Bulut; M. Murat Fadiloglu; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat
In this study we present a discrete artificial bee colony (DABC) algorithm to solve the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. In specific our algorithm provides a cyclic production schedule of n items to be produced on a single machine such that the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers are in the form of power-of-two and under EBP approach feasibility is guaranteed with a constraint that checks if the items assigned in each period can be produced within the length of the period. For this problem which is NP-hard our DABC algorithm employs a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also fused into DABC algorithm to further enhance the solution quality. The experimental results show that the proposed algorithm is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy.
Citation - Scopus: 5
A discrete harmony search algorithm for the economic lot scheduling problem with power of two policy
(IEEE, 2012) M. Fatih Tasgetiren; Önder Bulut; Mehmet Murat Fadiloglu; Murat Fadiloglu, M.; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat
In this paper we present a problem specific discrete harmony search (DHS) algorithms to solve the economic lot scheduling problem (ELSP) under the extended basic period (EBP) approach and power-of-two (PoT) policy. In particular DHS algorithms generate a cyclic production schedule consisting of n items to be produced on a single machine where the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers take the form of PoT which restricts the search space but provides good solution qualities. Under the EBP approach feasibility is guaranteed with a constraint checking whether or not the items assigned in each period can be produced within the length of the period. For this restricted problem which is still NP-hard the proposed DHS algorithms employ a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also hybridized with DHS algorithms to further enhance the solution quality. The experimental results show that the proposed algorithms are very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy. © 2012 IEEE. © 2012 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
A Genetic Algorithm for the Economic Lot Scheduling Problem under Extended Basic Period Approach and Power-of-Two Policy
(SPRINGER-VERLAG BERLIN, 2012) Onder Bulut; M. Fatih Tasgetiren; M. Murat Fadiloglu; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat; D Huang; Y Gan; P Gupta; MM Gromiha
In this study we propose a genetic algorithm (GA) for the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. The proposed GA employs a multi-chromosome solution representation to encode PoT multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. Furthermore a variable neighborhood search (VNS) algorithm is also fused into GA to further enhance the solution quality. The experimental results show that the proposed GA is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy.
Reinforcement Learning in Condition-Based Maintenance: A Survey
(Springer Science and Business Media Deutschland GmbH, 2025) Gamze Erdem; Mehmet Cemali Dinçer; Mehmet Murat Fadiloglu; Dincer, M. Cemali; Fadiloglu, M. Murat; Erdem, Gamze; C. Kahraman , S. Cebi , B. Oztaysi , S. Cevik Onar , C. Tolga , I. Ucal Sari , I. Otay
This literature review examines the convergence of Reinforcement Learning (RL) and Condition-Based Maintenance (CBM) emphasizing the trans- formative impact of RL methodologies on maintenance decision-making in com- plex industrial settings. By integrating insights from a diverse array of studies the review critically assesses the use of various RL techniques such as Q-learning deep reinforcement learning and policy gradient approaches in forecasting equipment failures optimizing maintenance schedules and reducing operational downtime. It outlines the shift from conventional rule-based maintenance practices to adaptive data-driven strategies that exploit real-time sensor data and probabilistic modeling. Key challenges highlighted include computational complexity the extensive training data requirements and the integration of RL models into existing industrial frameworks. Furthermore the review explores literature on CBM within multi-component systems where prevalent approaches include numerical analyses Markov Decision Processes (MDPs) and case studies all of which demonstrate notable cost reductions and decreased downtime. Relevant studies were identified through searches on databases such as Google Scholar Scopus and Web of Science. Overall this review provides a comprehensive analysis of the current state and prospects of employing reinforcement learning in condition-based maintenance offering valuable insights for both academic researchers and industry practitioners. © 2025 Elsevier B.V. All rights reserved.