Browsing by Author "Buyukdagli, Ozge"

Now showing 1 - 5 of 5

Citation - WoS: 13
Citation - Scopus: 15
A General Variable Neighborhood Search Algorithm for the No-Idle Permutation Flowshop Scheduling Problem
(SPRINGER-VERLAG BERLIN, 2013) M. Fatih Tasgetiren; Ozge Buyukdagli; Quan-Ke Pan; Ponnuthurai Nagaratnam Suganthan; Tasgetiren, M. Fatih; Suganthan, Ponnuthurai Nagaratnam; Buyukdagli, Ozge; Pan, Quan-Ke; BK Panigrahi; PN Suganthan; S Das; SS Dash
In this study a general variable neighborhood search (GVNS) is presented to solve no-idle permutation flowshop scheduling problem (NIPFS) where idle times are not allowed on machines. GVNS is a metaheuristic where inner loop operates a variable neighborhood descend (VND) algorithm whereas the outer loop carries out some perturbations on the current solution. We employ a simple insert and swap moves in the outer loop whereas iterated greedy (IG) and iterated local search (ILS) algorithms are employed in the VND as neighborhood structures. The results of the GVNS algorithm are compared to those generated by the variable iterated greedy algorithm with differential evolution (vIG_DE). The performance of the proposed algorithm is tested on the Ruben Ruiz' benchmark suite that is presented in http://soa.iti.es/rruiz. Computational results showed that the GVNS algorithm further improved 85 out of 250 best solutions found so far in the literature.
Citation - WoS: 3
Citation - Scopus: 3
A Novel Differential Evolution Algorithm with Q-Learning for Economical and Statistical Design of X-Bar Control Charts
(Institute of Electrical and Electronics Engineers Inc., 2020) Ahmad Abdulla Al-Buenain; Damla Kizilay; Ozge Buyukdagli; M. Fatih Tasgetiren; Kizilay, Damla; Tasgetiren, M. Fatih; Buyukdagli, Ozge; Al-Buenain, Ahmad Abdulla
This paper presents a novel differential evolution algorithm with Q-Learning (DE_QL) for the economical and statistical design of X-Bar control charts which has been commonly used in industry to control manufacturing processes. In X-Bar charts samples are taken from the production process at regular intervals for measurements of a quality characteristic and the sample means are plotted on this chart. When designing a control chart three parameters should be selected namely the sample size (n) the sampling interval (h) and the width of control limits (k). On the other hand when designing an economical and statistical design these three control chart parameters should be selected in such a way that the total cost of controlling the process should be minimized by finding optimal values of these three parameters. In this paper we develop a DE_QL algorithm for the global minimization of a loss cost function expressed as a function of three variables n h and k in an economic model of the X-bar chart. A problem instance that is commonly used in the literature has been solved and better results are found than the earlier published results. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 3
A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem
(SPRINGER-VERLAG BERLIN, 2013) M. Fatih Tasgetiren; Ozge Buyukdagli; Damla Kizilay; Korhan Karabulut; Tasgetiren, M. Fatih; Kizilay, Damla; Buyukdagli, Ozge; Karabulut, Korhan; BK Panigrahi; PN Suganthan; S Das; SS Dash
In this study we propose a populated iterated greedy algorithm with an Inver-Over operator to solve the traveling salesman problem. The iterated greedy (IG) algorithm is mainly based on the central procedures of destruction and construction. The basic idea behind it is to remove some solution components from a current solution and reconstruct them in the partial solution to obtain the complete solution again. In this paper we apply this idea in a populated manner (IGP) to the traveling salesman problem (TSP). Since the destruction and construction procedure is computationally expensive we also propose an iteration jumping to an Inver-Over operator during the search process. We applied the proposed algorithm to the well-known 14 TSP instances from TSPLIB. The computational results show that the proposed algorithm is very competitive to the recent best performing algorithms from the literature.
Citation - Scopus: 1
A Variable Iterated Greedy Algorithm with Differential Evolution for Solving No-Idle Flowshops
(SPRINGER-VERLAG BERLIN, 2012) M. Fatih Tasgetiren; Quan-Ke Pan; P. N. Suganthan; Ozge Buyukdagli; Tasgetiren, M. Fatih; Suganthan, P.N.; Buyukdagli, Ozge; Pan, Quan-Ke; L Rutkowski; M Korytkowski; R Scherer; R Tadeusiewicz; LA Zadeh; JM Zurada
In this paper we present a variable iterated greedy algorithm where its parameters (basically destruction size and probability of whether or not to apply the iterated greedy algorithm to an individual) are optimized by the differential evolution algorithm. A unique multi-chromosome solution representation is presented in such a way that the first chromosome represents the destruction size and the probability whereas the second chromosome is simply a job permutation assigned to each individual in the population randomly. The proposed algorithm is applied to the no-idle permutation flowshop scheduling problem with the makespan criterion. The performance of the proposed algorithm is tested on the Ruben Ruiz's benchmark suite and compared to their best known solutions available in http://soa.iti.es/rruiz as well as to a very recent discrete differential evolution algorithm from the literature. The computational results show its highly competitive performance and ultimately 183 out of 250 instances are further improved. In comparison to the very recent hybrid discrete differential evolution algorithm 114 out of 150 new best known solutions they provided are also further improved.
Citation - WoS: 89
Citation - Scopus: 102
A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem
(PERGAMON-ELSEVIER SCIENCE LTD, 2013) M. Fatih Tasgetiren; Quan-Ke Pan; P. N. Suganthan; Ozge Buyukdagli; Tasgetiren, M. Fatih; Suganthan, P. N.; Buyukdagli, Ozge; Fatih Tasgetiren, M.; Pan, Quan-Ke
This paper presents a variable iterated greedy algorithm (IG) with differential evolution (vIG_DE) designed to solve the no-idle permutation flowshop scheduling problem. In an IG algorithm size d of jobs are removed from a sequence and re-inserted into all possible positions of the remaining sequences of jobs which affects the performance of the algorithm. The basic concept behind the proposed vIG_DE algorithm is to employ differential evolution (DE) to determine two important parameters for the IG algorithm which are the destruction size and the probability of applying the IG algorithm to an individual. While DE optimizes the destruction size and the probability on a continuous domain by using DE mutation and crossover operators these two parameters are used to generate a trial individual by directly applying the IG algorithm to each target individual depending on the probability. Next the trial individual is replaced with the corresponding target individual if it is better in terms of fitness. A unique multi-vector chromosome representation is presented in such a way that the first vector represents the destruction size and the probability which is a DE vector whereas the second vector simply consists of a job permutation assigned to each individual in the target population. Furthermore the traditional IG and a variable IG from the literature are re-implemented as well. The proposed algorithms are applied to the no-idle permutation flowshop scheduling (NIPFS) problem with the makespan and total flowtime criteria. The performances of the proposed algorithms are tested on the Ruben Ruiz benchmark suite and compared to the best-known solutions available at http://soa.iti.es/rruiz as well as to those from a recent discrete differential evolution algorithm (HDDE) from the literature. The computational results show that all three IG variants represent state-of-art methods for the NIPFS problem. (C) 2013 Elsevier Ltd. All rights reserved.