Browsing by Author "Tasgetiren, Mehmet Fatih"

Now showing 1 - 14 of 14

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: 14
Citation - Scopus: 18
A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements
(MDPI AG, 2016) Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; Mehmet Fatih Tasgetiren; I. Sevil Sariyildiz; Quan-Ke Pan; Chatzikonstantinou, Ioannis; Tasgetiren, Mehmet Fatih; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre; Pan, Quan-Ke
This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla which is a rural touristic region located on the west coast of Turkey near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically we consider three objectives which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces as well as special functions such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation and gives promising results when the Pareto front approximation is examined.
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: 20
Citation - Scopus: 25
A variable block insertion heuristic for solving permutation flow shop scheduling problem with makespan criterion
(MDPI AG indexing@mdpi.com Postfach Basel CH-4005, 2019) Damla Kizilay; M. Fatih Tasgetiren; Quanke Pan; Liang Gao; Kizilay, Damla; Tasgetiren, Mehmet Fatih; Gao, Liang; Pan, Quan-Ke
In this paper we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution it replaces the current solution with the new one. As long as it improves it retains the same block size. Otherwise the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately we run Taillard's benchmark suite and compare the algorithms. In addition to the above three instances of Taillard's benchmark suite are also further improved for the first time in this paper since 1993. © 2019 Elsevier B.V. All rights reserved.
Citation - WoS: 30
Citation - Scopus: 36
A Variable Block Insertion Heuristic for the Blocking Flowshop Scheduling Problem with Total Flowtime Criterion
(MDPI AG, 2016) Mehmet Fatih Tasgetiren; Quan-Ke Pan; Damla Kizilay; Kaizhou Gao; Tasgetiren, Mehmet Fatih; Kizilay, Damla; Pan, Quan-Ke; Gao, Kaizhou
In this paper we present a variable block insertion heuristic (VBIH) algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm we define a minimum and a maximum block size. After constructing the initial sequence the VBIH algorithm starts with a minimum block size being equal to one. It removes the block from the current sequence and inserts it into the partial sequence sequentially with a predetermined move size. The sequence which is obtained after several block moves goes under a variable local search (VLS) which is based on traditional insertion and swap neighborhood structures. If the new sequence obtained after the VLS local search is better than the current sequence it replaces the current sequence. As long as it improves it keeps the same block size. However if it does not improve the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the current sequence. This process is repeated until the block size reaches at the maximum block size. Furthermore we present a novel constructive heuristic which is based on the profile fitting heuristic from the literature. The proposed constructive heuristic is able to further improve the best known solutions for some larger instances in a few seconds. Parameters of the constructive heuristic and the VBIH algorithm are determined through a design of experiment approach. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed VBIH algorithm outperforms the discrete artificial bee colony algorithm which is one of the most efficient algorithms recently in the literature. Ultimately 52 out of the 150 best known solutions are further improved with substantial margins.
Citation - WoS: 84
Citation - Scopus: 97
An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time
(Taylor and Francis Ltd., 2015) Kaizhou Gao; Ponnuthurai Nagaratnam Suganthan; Quanke Pan; M. Fatih Tasgetiren; Suganthan, Ponnuthurai Nagaratnam; Tasgetiren, Mehmet Fatih; Gao, Kai Zhou; Pan, Quan Ke
This study addresses flexible job shop scheduling problem (FJSP) with fuzzy processing time. The fuzzy or uncertainty of processing time is one of seven characteristics in remanufacturing. A discrete harmony search (DHS) algorithm is proposed for FJSP with fuzzy processing time. The objective is to minimise maximum fuzzy completion time. A simple and effective heuristic rule is proposed to initialise harmony population. Extensive computational experiments are carried out using five benchmark cases with eight instances from remanufacturing. The proposed heuristic rule is evaluated using five benchmark cases. The proposed DHS algorithm is compared to six metaheuristics. The results and comparisons show the effectiveness and efficiency of DHS for solving FJSP with fuzzy processing time. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 132
Citation - Scopus: 151
Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion
(Elsevier B.V., 2016) Kaizhou Gao; Ponnuthurai Nagaratnam Suganthan; Quanke Pan; M. Fatih Tasgetiren; Ali Sadollah; Suganthan, Ponnuthurai Nagaratnam; Tasgetiren, Mehmet Fatih; Sadollah, Ali; Gao, Kai Zhou; Pan, Quan Ke
This study addresses flexible job shop scheduling problem (FJSP) with two constraints namely fuzzy processing time and new job insertion. The uncertainty of processing time and new job insertion are two scheduling related characteristics in remanufacturing. Fuzzy processing time is used to describe the uncertainty in processing time. Rescheduling operator is executed when new job(s) is (are) inserted into the schedule currently being executed. A two-stage artificial bee colony (TABC) algorithm with several improvements is proposed to solve FJSP with fuzzy processing time and new job insertion constraints. Also several new solution generation methods and improvement strategies are proposed and compared with each other. The objective is to minimize maximum fuzzy completion time. Eight instances from remanufacturing are solved using the proposed TABC algorithm. The proposed improvement strategies are compared and discussed in detail. Two proposed ABC algorithms with the best performances are compared against seven existing algorithms over by five benchmark cases. The optimization results and comparisons show the competitiveness of the proposed TABC algorithm for solving FJSP. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 1
Citation - Scopus: 1
Designing a Railway Network in Cesme Izmir with Bi-objective Ring Star Problem
(SPRINGER-VERLAG SINGAPORE PTE LTD, 2022) Oya Merve Puskul; Dilara Aslan; Ceren Onay; Mehmet Serdar Erdogan; Mehmet Fatih Tasgetiren; Erdogan, Mehmet Serdar; Tasgetiren, Mehmet Fatih; Onay, Ceren; Aslan, Dilara; Puskul, Oya Merve; NM Durakbasa; MG Gencyilmaz
Transportation is a significant subject in today's world especially in terms of the environment and the needs of the community. Clearly high rates of urbanization and population growth result in high volumes of demand for public transportation at the same growing ratio. This project aims to design an optimal railway network to meet the region's public transportation needs and to reduce the region's pollution due to the high seasonal density of the population in the Cesme district. The objective functions of a project are determined by minimizing both assignment cost and routing cost. The assignment cost denotes the total cost of getting on the tram for people. The routing cost is defined as the total construction costs of the tram line's selected nodes. This problem is solved by the epsilon-constraint method as a multi-objective optimization problem. Consequently it has been determined that the two main costs do not decrease at the same time. They are in a correlation where one reduces and the other increases. This is the first study that applies a multi objective ring star problem to a real life case study.
Citation - WoS: 64
Citation - Scopus: 76
Effective ensembles of heuristics for scheduling flexible job shop problem with new job insertion
(Elsevier Ltd, 2015) Kaizhou Gao; Ponnuthurai Nagaratnam Suganthan; M. Fatih Tasgetiren; Quanke Pan; Qiangqiang Sun; Suganthan, Ponnuthurai Nagaratnam; Tasgetiren, Mehmet Fatih; Sun, Qiang Qiang; Gao, Kai Zhou; Pan, Quan Ke
This study investigates the flexible job shop scheduling problem (FJSP) with new job insertion. FJSP with new job insertion includes two phases: initializing schedules and rescheduling after each new job insertion. Initializing schedules is the standard FJSP problem while rescheduling is an FJSP with different job start time and different machine start time. The time to do rescheduling is the same as the time of new job insertion. Four ensembles of heuristics are proposed for scheduling FJSP with new job insertion. The objectives are to minimize maximum completion time (makespan) to minimize the average of earliness and tardiness (E/T) to minimize maximum machine workload (Mworkload) and total machine workload (Tworkload). Extensive computational experiments are carried out on eight real instances from remanufacturing enterprise. The results and comparisons show the effectiveness of the proposed heuristics for solving FJSP with new job insertion. © 2015 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 7
Ensemble of differential evolution algorithms for electromagnetic target recognition problem
(Inst Engineering Technology-Iet, 2013) Mustafa Seçmen; M. Fatih Tasgetiren; Secmen, Mustafa; Tasgetiren, Mehmet Fatih
In this study an ensemble of differential evolution (DE) algorithms is presented to classify electromagnetic targets in resonance scattering region. The algorithm aims to synthesize a special incident signal for each target which is defined as the main discrimination feature in the given target recognition method. In the proposed algorithm the amplitudes of basis functions and the duration of this incident signal are optimised to give minimum late-time scattered signal's energy which is the main fitness function of the algorithm. The proposed DE algorithm is applied to a target set consisting of lossless dielectric spheres and correct recognition rates for both noiseless and noisy signals are obtained. The results for both developed DE algorithm and other DE variants of traditional DE adaptive differential evolution with optional external archive (JADE) jDE are also given to compare the algorithms and show the effectiveness of the proposed one. © The Institution of Engineering and Technology 2013. © 2013 Elsevier B.V. All rights reserved.
Citation - WoS: 1
Citation - Scopus: 2
Intelligent Valid Inequalities for No-Wait Permutation Flowshop Scheduling Problems
(Springer Science and Business Media Deutschland GmbH, 2022) Damla Yüksel; Levent Kandiller; M. Fatih Tasgetiren; Yuksel, Damla; Tasgetiren, Mehmet Fatih; Kandiller, Levent; C. Kahraman , S. Cevik Onar , B. Oztaysi , I.U. Sari , A.C. Tolga , S. Cebi
The no-wait permutation flowshop scheduling problem is a well-recognized scheduling problem. Examples can be encountered in several industries such as hot metal rolling painting chemical steel industries etc. In this flowshop setting the jobs are not allowed to wait between consecutive machines. Owing to the NP-hardness identity of the problem the developed mathematical models to solve this problem cannot reach optimal solutions for large instances in polynomial time. However the quality of the objective functions and the gap values obtained by the mathematical models in a specific time window can be improved by valid inequalities. This study generates intelligent valid inequalities to improve a mathematical model’s performance in optimizing the no-wait permutation flow shop scheduling problems. Valid inequalities’ performance is tested for three significant objective functions: (i) makespan (ii) total flow time and (iii) total tardiness. According to the computational experiments the new valid inequalities improve the outcomes of the mathematical models mostly in the way of the gap values for makespan total flow time and total tardiness objective criteria. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 9
Metaheuristics for Energy-Efficient No-Wait Flowshops: A Trade-off Between Makespan and Total Energy Consumption
(IEEE, 2020) Damla Yuksel; Mehmet Fatih Tasgetiren; Levent Kandiller; Quan-Ke Pan; Yuksel, Damla; Tasgetiren, Mehmet Fatih; Kandiller, Levent; Pan, Quan-Ke
No-wait flowshop scheduling problem (NWFSP) is a well-known strongly NP-hard problem where in-process waiting is not allowed between any two consecutive machines in such a way that once a job is started subsequent processing must be carried out on all machines until completion. In this paper we propose an energy-efficient NWFSP in order to investigate the trade-off between makespan and total energy consumption. The energy-efficient NWFSP aims to seek to obtain Pareto solution sets to minimize the makespan and the total energy consumption conflicting with each other. Unlike the classical NWFSP there are different speed levels for each job on machines and the processing times of jobs can differ according to the assigned speed levels. Therefore we modify the formulation of NWFSP by introducing a speed scaling strategy in order to approximate Pareto solution sets i.e. non-dominated solution sets. In this paper we propose a mixed-integer linear programming model (MILP) an energy-efficient variable block insertion heuristic (EE-VBIH) an energy-efficient iterated greedy algorithm (IG) and an energy-efficient & IG-ALL) to solve the energy-efficient NWFSP. Extensive computational analyses on Taillard's benchmark suite show that the proposed algorithms are very effective for approximating Pareto solution sets.
Citation - WoS: 33
Citation - Scopus: 42
OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling
(MDPI, 2019) Cemre Cubukcuoglu; Berk Ekici; Mehmet Fatih Tasgetiren; Sevil Sariyildiz; Ekici, Berk; Sariyildiz, Sevil; Tasgetiren, Mehmet Fatih; Cubukcuoglu, Cemre
Most of the architectural design problems are basically real-parameter optimization problems. So any type of evolutionary and swarm algorithms can be used in this field. However there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper we present Optimus which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum maximum average standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools namely Galapagos (genetic algorithm) SilverEye (particle swarm optimization) and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function Galapagos presented slightly better result than Optimus. Ultimately we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum maximum average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results whereas Optimus and Opossum found feasible solutions. However Optimus discovered a much better fitness result than Opossum. As a conclusion we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g. architects engineers designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreover Optimus facilitates implementing different type of algorithms due to its modular system.
Citation - Scopus: 2
Solving Lot-streaming Flow Shop Scheduling Problems Using a Discrete Harmony Search Algorithm
(IEEE, 2010) Quan-Ke Pan; Mehmet Fatih Tasgetiren; Ponnuthurai Nagaratnam Suganthan; Yun-Chia Liang; Tasgetiren, Mehmet Fatih; Suganthan, Ponnuthurai Nagaratnam; Liang, Yun-Chia; Pan, Quan-Ke
The harmony search (HS) algorithm is one of the recent evolutionary computation techniques to solve optimization problems. To make it applicable for lot-streaming flow shop problems a discrete variant of the HS algorithm (DHS) with job permutations representation is proposed. In the proposed DHS algorithm a new improvisation scheme is designed to generate feasible job sequences. A local search algorithm based on the insert neighborhood structure is fused to stress the further enhancement capability of the algorithm proposed whereas a restart scheme is employed to avoid the stagnation of the evolution. Extensive computational simulations and comparisons are provided which demonstrate the effectiveness of the proposed DHS against the best performing algorithms from the literature.