Browsing by Author "Oztop, Hande"

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Citation - WoS: 10
Citation - Scopus: 23
A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems
(Institute of Electrical and Electronics Engineers Inc., 2020) Damla Kizilay; M. Fatih Tasgetiren; Hande Oztop; Levent Kandiller; Ponnuthurai Nagaratnam Suganthan; Kizilay, Damla; Tasgetiren, M. Fatih; Suganthan, P. N.; Oztop, Hande; Kandiller, Levent
In this paper a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known the performance of a DE algorithm depends on the mutation strategy and its control parameters namely crossover and mutation rates. For this reason the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore a new mutation operator namely DE/Best to current/l is proposed in the DE-QL algorithm. In this paper 57 EDPs provided in 'Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results' are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 6
A General Variable Neighborhood Search for the NoIdle Flowshop Scheduling Problem with Makespan Criterion
(Institute of Electrical and Electronics Engineers Inc., 2019) Liangshan Shen; M. Fatih Tasgetiren; Hande Oztop; Levent Kandiller; Liang Gao; Shen, Liangshan; Tasgetiren, Mehmet Fatih; Gao, Liang; Oztop, Hande; Kandiller, Levent
This paper proposes a novel general variable neighborhood search (GVNS) algorithm to solve the no-idle flowshop scheduling problem with the makespan criterion. The initial solution of the GVNS is generated using the FRB5 heuristic. In the outer loop insert and swap operations are employed to shake the permutation. In the inner loop of variable neighborhood descent procedure two effective algorithms namely Iterated Greedy (IG) and Variable Block Insertion Heuristic (VBIH) algorithms are used. Note that an effective referenced insertion scheme is employed in these IG and VBIH algorithms. The proposed GVNS algorithm is compared with the standard IG algorithm using the benchmark instances. The computational experiments show that the GVNS performs much better than the standard IG. Furthermore the results of the standard IG and GVNS algorithms are compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS algorithm improves some of the current best-known solutions in the literature. Consequently it can be said that the GVNS is very effective for the no-idle flowshop scheduling problem with the makespan criterion. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 17
Citation - Scopus: 23
A Novel General Variable Neighborhood Search through Q-Learning for No-Idle Flowshop Scheduling
(IEEE, 2020) Hande Oztop; Mehmet Fatih Tasgetiren; Levent Kandiller; Quan-Ke Pan; Tasgetiren, Mehmet Fatih; Oztop, Hande; Kandiller, Levent; Pan, Quan-Ke
In this study a novel general variable neighborhood search through Q-learning (GVNS-QL) algorithm is proposed to solve the no-idle flowshop scheduling problem with the makespan objective. In the outer loop of the GVNS-QL insertion and exchange operators are used to shaking the permutation. On the other hand in the inner loop of variable neighborhood descent procedure variable iterated greedy and variable block insertion heuristic algorithms are employed with two effective insertion local search procedures. The proposed GVNS-QL defines the parameters of the algorithm using a Q-learning mechanism. The developed GVNS-QL algorithm is compared with the traditional iterated greedy (IG) algorithm using the well-known benchmark set. The comprehensive computational experiments show that the GVNS-QL outperforms the traditional IG algorithm. The results of the IG and GVNS-QL algorithms are also compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS-QL algorithm improves the current best-known solutions for 104 out of 250 instances.
Citation - WoS: 2
A Variable Block Insertion Heuristic for Single Machine with Release Dates and Sequence Dependent Setup Times for Makespan Minimization
(IEEE, 2019) Jiaxin Fan; Damla Kizilay; Hande Oztop; Kizilay, Damla; Oztop, Hande; Fan, Jiaxin
This paper is concerned with solving the single machine scheduling problem with release dates and sequence-dependent setup times in order to minimize the makespan. For this purpose a variable block insertion heuristic (VBIH) algorithm is applied to the problem. The VBIH algorithm performs block moves on a given solution. Mainly it removes a block of jobs with a given size from the solution and inserts the block into the best position of the partial solution. Furthermore we present a novel profile-fitting constructive heuristic for the problem. We evaluate the performance of the VBIH algorithm by comparisons with the beam search heuristic and the iterated greedy algorithm from the literature. Extensive computational results on the benchmark suite consisting of 900 instances from the literature show that the proposed VBIH algorithm is very competitive to the recent beam search heuristic and iterated greedy algorithm from the literature.
A Variable Block Insertion Heuristic for the Energy-Efficient Permutation Flowshop Scheduling with Makespan Criterion
(Springer Science and Business Media Deutschland GmbH, 2021) M. Fatih Tasgetiren; Hande Oztop; Quanke Pan; Mustafa Arslan Ornek; Talya Temizceri; Tasgetiren, M. Fatih; Temizceri, Talya; Oztop, Hande; Pan, Quan-Ke; Ornek, M. Arslan
Permutation flow shop scheduling problem is a well-known problem in the scheduling literature. Even though various multi-objective permutation flowshop scheduling problems have been studied in the literature energy consumption consideration in scheduling is still very seldom. In this paper we consider a bi-objective permutation flowshop scheduling problem with the objectives of minimizing the total energy consumption and the makespan. We present a bi-objective mixed integer programming model for the problem applying a speed-scaling approach. Then we employ the augmented ε -constraint method to generate the Pareto-optimal solution sets for small-sized instances. For larger instances we use the augmented ε -constraint method with a time limit on CPLEX solver to approximate the Pareto frontiers. We also propose a heuristic approach which employs a very recent variable block insertion heuristic algorithm. In order to evaluate performance of the proposed algorithm we have carried out detailed computational experiments using well-known benchmarks from the literature. First we present the performance of the proposed algorithm on small-sized problems, then we show that the proposed algorithm is very effective to solve larger problems as compared with the time-limited CPLEX. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 12
Citation - Scopus: 14
A Variable Iterated Local Search Algorithm for Energy-Efficient No-idle Flowshop Scheduling Problem
(Elsevier B.V., 2019) M. Fatih Tasgetiren; Hande Oztop; Liang Gao; Quanke Pan; Xinyu Li; Tasgetiren, M. Fatih; Gao, Liang; Li, Xinyu; Fatih Tasgetiren, M.; Oztop, Hande; Pan, Quan-Ke; C.H. Dagli , G.A. Suer
No-idle permutation flowshop scheduling problem (NIPFSP) is a well-known NP-hard problem in which each machine must perform the jobs consecutively without any idle time. Even though various algorithms have been proposed for this problem energy efficiency has not been considered in these studies. In this paper we consider a bi-objective energy-efficient NIPFSP (EE-NIPFSP) with the objectives of makespan and total energy consumption. In the studied EE-NIPFSP we employ a speed scaling approach in which there are various speed levels for the jobs. We propose a novel mixed-integer linear programming model for the problem and we obtain Pareto-optimal solution sets for small instances using the augmented ε-constraint method. As the studied problem is NP-hard three metaheuristic algorithms are also proposed namely a multi-objective variable iterated local search (MOVILS) algorithm a multi-objective genetic algorithm (MOGA) and a MOGA with local search (MOGA-LS) for the problem. Then the performance of the proposed algorithms is assessed on both small and large instances in terms of various quality measures. The results show that the proposed algorithms are very effective for the EE-NIPFSP in terms of solution quality. Especially MOVILS and MOGA-LS algorithms are more efficient to solve large instances when compared to the MOGA. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 42
Citation - Scopus: 45
An evolution strategy approach for the distributed permutation flowshop scheduling problem with sequence-dependent setup times
(Elsevier Ltd, 2022) Korhan Karabulut; Hande Oztop; Damla Kizilay; M. Fatih Tasgetiren; Levent Kandiller; Kizilay, Damla; Tasgetiren, M. Fatih; Karabulut, Korhan; Oztop, Hande; Kandiller, Levent
This paper considers a distributed permutation flowshop scheduling problem with sequence-dependent setup times (DPFSP-SDST) to minimize the maximum completion time among the factories. The global economy has enabled large companies to have distributed production centers to become widespread and effective production scheduling between these centers plays a vital role in the competitiveness of companies. To provide effective scheduling for the DPFSP-SDST we propose a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model which is presented for the first time in literature to the best of our knowledge. As the CP has become a solid competitor to the MILP in the literature this study aims to exploit the effectiveness of CP to solve such a complex DPFSP-SDST. Since the problem is NP-hard we also offer an evolution strategy (ES_en) algorithm that employs a self-adaptive scheme to obtain high-quality solutions in a short time. A ruin-and-recreate procedure is also embedded into the developed ES_en. We calibrate the parameters of the proposed ES_en using a design of experiment approach. We also compare the proposed ES_en algorithm's performance with three state-of-the-art metaheuristic algorithms from the literature i.e. the IG2S (a variant of an iterated greedy algorithm with NEH2_en initialization) IGR (another variant of an iterated greedy algorithm with a restart scheme) and discrete artificial bee colony (DABC) algorithm. A detailed computational experiment is carried out to evaluate the performance of the mathematical models (MILP and CP) and the heuristic algorithms (ES_en IG2S IGR and DABC). A comprehensive benchmark set is generated for the DPFSP-SDST from the well-known PFSP instances from the literature considering various combinations of jobs machines factories and SDST settings resulting in 2880 benchmark instances. For 216 out of 240 small-size instances optimal results are reported by solving the proposed MILP and CP models whereas time-limited model results are reported for the rest. The computational results show that the CP model outperforms the MILP model in terms of the solution time for small-size instances. Initially the performance of the heuristic algorithms is verified concerning the optimal results on small-size instances. Then the performance of the heuristic algorithms is evaluated for large instances. ES_en algorithm significantly outperforms the IG2S IGR and DABC algorithms for solving large instances. The computational results show that the proposed ES_en algorithm is robust and provides good-quality solutions for the DPFSP-SDST in a short computational time. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 13
Citation - Scopus: 14
Energy-efficient single machine total weighted tardiness problem with sequence-dependent setup times
(Springer Verlag service@springer.de, 2018) M. Fatih Tasgetiren; Hande Oztop; Uǧur Eliiyi; D. T. Eliiyi; Quanke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Oztop, Hande; Pan, Quan-Ke; Eliiyi, Ugur; Eliiyi, Deniz Tursel; P. Premaratne , P. Gupta , D. Huang , V. Bevilacqua
Most of the problems defined in the scheduling literature do not yet take into account the energy consumption of manufacturing processes as in most of the variants with tardiness objectives. This study handles scheduling of jobs with due dates and sequence-dependent setup times (SMWTSD) while minimizing total weighted tardiness and total energy consumed in machine operations. The trade-off between total energy consumption (TEC) and total weighted tardiness is examined in a single machine environment where different jobs can be operated at varying speed levels. A bi-objective mixed integer linear programming model is formulated including this speed-scaling plan. Moreover an efficient multi-objective block insertion heuristic (BIH) and a multi-objective iterated greedy (IG) algorithm are proposed for this NP-hard problem. The performances of the proposed BIH and IG algorithms are compared with each other. The preliminary computational results on a benchmark suite consisting of instances with 60 jobs reveal that the proposed BIH algorithm is very promising in terms of providing good Pareto frontier approximations for the problem. © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 14
Citation - Scopus: 17
Green Permutation Flowshop Scheduling: A Trade- off- Between Energy Consumption and Total Flow Time
(SPRINGER INTERNATIONAL PUBLISHING AG, 2018) Hande Oztop; M. Fatih Tasgetiren; Deniz Tursel Eliiyi; Quan-Ke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Oztop, Hande; Pan, Quan-Ke; Türsel Eliiyi, Deniz; Eliiyi, Deniz Tursel; DS Huang; MM Gromiha; K Han; A Hussain
Permutation flow shop scheduling problem (PFSP) is a well-known problem in the scheduling literature. Even though many multi-objective PFSPs are presented in the literature with the objectives related to production efficiency and customer satisfaction studies considering energy consumption and environmental effects in scheduling is very seldom. In this paper the trade-off between total energy consumption (TEC) and total flow time is investigated in a PFSP environment where the machines are assumed to operate at varying speed levels. A multi-objective mixed integer linear programming model is proposed based on a speed-scaling strategy. Due to the NP-complete nature of the problem an efficient multi-objective iterated greedy (IGALL) algorithm is also developed. The performance of IGALL is compared with model performance in terms of quality and cardinality of the solutions.
Citation - WoS: 31
Citation - Scopus: 39
Modeling and optimization of multiple traveling salesmen problems: An evolution strategy approach
(Elsevier Ltd, 2021) Korhan Karabulut; Hande Oztop; Levent Kandiller; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Karabulut, Korhan; Oztop, Hande; Kandiller, Levent
The multiple traveling salesmen problems (mTSP) are variants of the well-known traveling salesmen problems in which n cities are to be assigned to m salespeople. In this paper we propose an evolution strategy (ES) approach for solving the mTSP with minsum and minmax objectives. The ES employs a self-adaptive Ruin and Recreate (RR) heuristic to generate an offspring population. In the RR heuristic some solution components are removed from a solution and the removed components are reinserted into the partial solution to obtain a new complete solution again. We employ the Multiple Insertion Heuristic (MIH) when carrying out insertions with the speed-up method based on the nearest neighbor approach in the recreate procedure. Through the ES the ruin size parameter of the RR heuristic and the selection probabilities of applying a random search algorithm consisting of four different moves are self-adapted. 3Opt local search is also embedded in the ES to further enhance the solution quality. A Boolean flag is developed whether or not to apply 3Opt local search which is computationally expensive on non-improving tours. Moreover new constructive heuristics are presented for both minsum and minmax mTSP. Computational experiments show that the proposed ES algorithm is very competitive or superior to the best performing algorithms from the literature for both objectives. Ultimately 21 new best-solutions are presented for the mTSP with minsum and minmax objectives for the first time in the literature. In this paper the multi-depot mTSP with non-predetermined depots (M-mTSP) is also studied as an extension of the mTSP. Two new MILP models are proposed for both minsum and minmax M-mTSP as well as lower bounds and optimal results are obtained for small instances. The computational results show that the ES algorithm can find the optimal solution for all the small-sized instances. Furthermore we derive new large-sized M-mTSP benchmarks from the well-known TSPLIB which have nodes ranging from 52 and 442. We report the results for both minsum and minmax objectives for these instances as new M-mTSP benchmarks considering four different m settings. © 2021 Elsevier B.V. All rights reserved.
Citation - Scopus: 5
Solving 0-1 Bi-Objective Multi-dimensional Knapsack Problems Using Binary Genetic Algorithm
(Springer Science and Business Media Deutschland GmbH, 2021) Ozgur Kabadurmus; M. Fatih Tasgetiren; Hande Oztop; Mehmet Serdar Erdoğan; Tasgetiren, M. Fatih; Erdogan, M. Serdar; Oztop, Hande; Kabadurmus, Ozgur
The multi-dimensional knapsack problem (MDKP) is a well-known NP-hard problem in combinatorial optimization. As it has various real-life applications the MDKP has been intensively studied in the literature. On the other hand far too little attention has been paid to the multi-objective version of the MDKP. In this chapter we consider the bi-objective multi-dimensional knapsack problem (BOMDKP). We propose a Binary Genetic Algorithm (BGA) with an external archive for the problem. Our proposed BGA algorithm also employs a binary local search. The non-dominated solution sets are obtained for various bi-objective benchmark instances with 100 250 500 and 750 items by employing the proposed BGA. Then the performance of the BGA is compared with other multi-objective algorithms from the literature i.e. MOEA/D and MOFPA. Furthermore it is observed that the Pareto-optimal solution set provided by Zitzler and Laumans for 500 items and 2 knapsacks includes 30 dominated solutions. Also the Pareto-optimal solutions for the scenario with 750 items are not reported in Zitzler and Thiele [43]. Hence the true Pareto-optimal solution sets are found for all benchmark problem instances using Improved Augmented Epsilon Constraint (AUGMECON2) method. The non-dominated solution sets of the BGA MOEA/D and MOFPA are compared with the Pareto-optimal solution sets for all test instances. The computational results indicate that the proposed BGA is more effective to solve the BOMDKP than the best-performing algorithms from the literature. © 2020 Elsevier B.V. All rights reserved.