Browsing by Author "Pan, Quan-Ke"

Now showing 1 - 20 of 52

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: 47
Citation - Scopus: 52
A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion
(Taylor & Francis Ltd, 2011) M. Fatih Tasgetiren; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Tay Jin Chua; Tasgetiren, M. Fatih; Suganthan, P. N.; Jin Chua, Tay; Pan, Quan-Ke; Chua, Tay Jin
In this paper we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all a continuous optimisation algorithm is used to solve a combinatorial optimisation problem where some efficient methods of converting a continuous vector to a discrete job permutation and vice versa are presented. These methods are not problem specific and can be employed in any continuous algorithm to tackle the permutation type of optimisation problems. Secondly a variable parameter search is introduced for the differential evolution algorithm which significantly accelerates the search process for global optimisation and enhances the solution quality. Thirdly some novel ways of calculating the total tardiness from makespan are introduced for the NIPFS problem. The performance of vpsDE is evaluated against a well-known RKGA from the literature. The computational results show its highly competitive performance when compared to RKGA. It is shown in this paper that the vpsDE performs better than the RKGA thus providing an alternative solution approach to the literature that the RKGA can be well suited. © 2011 Taylor & Francis. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 16
Citation - Scopus: 23
A differential evolution algorithm with variable neighborhood search for multidimensional knapsack problem
(Institute of Electrical and Electronics Engineers Inc., 2015) M. Fatih Tasgetiren; Quanke Pan; Damla Kizilay; Gürsel A. Süer; Tasgetiren, M. Fatih; Kizilay, Damla; Pan, Quan-Ke; Suer, Gursel
This paper presents a differential evolution algorithm with a variable neighborhood search to solve the multidimensional knapsack problem. Unlike the studies employing check and repair operators we employ some sophisticated constraint handling methods to enrich the population diversity by taking advantages of infeasible solution within a predetermined threshold. We propose to a variable neighborhood search employing different mutation strategies to generate the trial population. The proposed algorithm in fact works on a continuous domain but these real-values are converted to 0-1 binary values by using the sigmoid function. In order to enhance the solution quality the differential evolution algorithm with a variable neighborhood search is combined with a binary swap local search algorithm. To the best of our knowledge this is the first reported application of the differential evolution algorithm to solve the multidimensional knapsack problem in the literature. The proposed algorithm is tested on a benchmark instances from the OR-Library. Computational results show its efficiency in solving benchmark instances and its superiority to the best performing algorithms from the literature. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 11
A Discrete Artificial Bee Colony Algorithm for the Energy-Efficient No-Wait Flowshop Scheduling Problem
(ELSEVIER SCIENCE BV, 2019) M. Fatih Tasgetiren; Damla Yuksel; Liang Gao; Quan-Ke Pan; Peigen Li; Yuksel, Damla; Tasgetiren, M. Fatih; Gao, Liang; Li, Peigen; Fatih Tasgetiren, M.; Pan, Quan-Ke; CH Dagli; GA Suer
No-wait permutation flow shop scheduling problem (NWPFSP) is a variant of permutation flow shop scheduling problem (PFSP) where the processing of each job must be continuous from start to end without any interruption. That is once a job starts its processing it has to be processed until the last machine without any interruption. The aim of this study is to propose an energy-efficient NWPFSP for the determination of a trade-off between total flow time and total energy consumption by obtaining the Pareto optimal set that is the non-dominated solution set. A bi-objective mixed-integer programming model is developed where the machines can operate at different speed levels. Since the problem is NP-complete an energy-efficient discrete artificial bee colony (DABC) and an energy-efficient genetic algorithm (MOGA) also a variant of this algorithm (MOGALS) are developed as heuristic methods. First the performance of these algorithms for comparison with the mathematical model is represented in small size instances in the scope of cardinality and quality of the non-dominated solutions then it is shown that DABC performs better than two other algorithms in larger instances. (C) 2019 The Authors. Published by Elsevier Ltd.
Citation - WoS: 479
Citation - Scopus: 583
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
(ELSEVIER SCIENCE INC, 2011) Quan-Ke Pan; M. Fatih Tasgetiren; P. N. Suganthan; T. J. Chua; Tasgetiren, M. Fatih; Suganthan, P. N.; Fatih Tasgetiren, M.; Pan, Quan-Ke; Chua, T. J.
In this paper a discrete artificial bee colony (DABC) algorithm is proposed to solve the lot-streaming flow shop scheduling problem with the criterion of total weighted earliness and tardiness penalties under both the idling and no-idling cases. Unlike the original ABC algorithm the proposed DABC algorithm represents a food source as a discrete job permutation and applies discrete operators to generate new neighboring food sources for the employed bees onlookers and scouts. An efficient initialization scheme which is based on the earliest due date (EDD) the smallest slack time on the last machine (LSL) and the smallest overall slack time (OSL) rules is presented to construct the initial population with certain quality and diversity. In addition a self adaptive strategy for generating neighboring food sources based on insert and swap operators is developed to enable the DABC algorithm to work on discrete/combinatorial spaces. Furthermore a simple but effective local search approach is embedded in the proposed DABC algorithm to enhance the local intensification capability. Through the analysis of experimental results the highly effective performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. (C) 2010 Elsevier Inc. All rights reserved.
Citation - WoS: 232
Citation - Scopus: 270
A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities
(Elsevier Science Inc, 2014) Junqing Li; Quanke Pan; M. Fatih Tasgetiren; Li, Jun-Qing; Tasgetiren, M. Fatih; Pan, Quan-Ke
This paper presents a novel discrete artificial bee colony (DABC) algorithm for solving the multi-objective flexible job shop scheduling problem with maintenance activities. Performance criteria considered are the maximum completion time so called makespan the total workload of machines and the workload of the critical machine. Unlike the original ABC algorithm the proposed DABC algorithm presents a unique solution representation where a food source is represented by two discrete vectors and tabu search (TS) is applied to each food source to generate neighboring food sources for the employed bees onlooker bees and scout bees. An efficient initialization scheme is introduced to construct the initial population with a certain level of quality and diversity. A self-adaptive strategy is adopted to enable the DABC algorithm with learning ability for producing neighboring solutions in different promising regions whereas an external Pareto archive set is designed to record the non-dominated solutions found so far. Furthermore a novel decoding method is also presented to tackle maintenance activities in schedules generated. The proposed DABC algorithm is tested on a set of the well-known benchmark instances from the existing literature. Through a detailed analysis of experimental results the highly effective and efficient performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature. © 2013 Elsevier Inc. © 2014 Elsevier B.V. All rights reserved.
Citation - WoS: 82
Citation - Scopus: 103
A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion
(ELSEVIER SCIENCE INC, 2013) M. Fatih Tasgetiren; Quan-Ke Pan; P. N. Suganthan; Adalet Oner; Suganthan, P.N.; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Oner, Adalet; Pan, Quan-Ke
In this paper we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly some novel methods of calculating the total tardiness from make-span are introduced for the no-idle permutation flowshop scheduling problem. Finally the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard's benchmark suit. (C) 2013 Elsevier Inc. All rights reserved.
Citation - Scopus: 17
A Discrete Artificial Bee Colony Algorithm for the Permutation Flow Shop Scheduling Problem with Total Flowtime Criterion
(IEEE, 2010) M. Fatih Tasgetiren; Quan-Ke Pan; P. Nagaratnam Suganthan; Angela H-L Chen; Tasgetiren, M. Fatih; Suganthan, P. Nagaratnam; Karabulut, Korhan; Pan, Quan-Ke; Ince, Yavuz; Chen, Angela H.-L.; Wang, Ling
Very recently Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638-2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84-92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion respectively. Both algorithms generated excellent results thus improving all the best known solutions reported in the literature so far. However in this paper we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.
Citation - WoS: 201
Citation - Scopus: 241
A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops
(Elsevier Science Inc, 2011) M. Fatih Tasgetiren; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Angela Hsiang Ling Chen; Tasgetiren, M. Fatih; Suganthan, P.N.; Pan, Quan-Ke; Chen, Angela H-L
Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. Iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches as well. © 2011 Elsevier Inc. All rights reserved. © 2011 Elsevier B.V. All rights reserved.
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: 53
Citation - Scopus: 62
A green scheduling algorithm for the distributed flowshop problem
(Elsevier Ltd, 2021) Yuanzhen Li; Quanke Pan; Kaizhou Gao; M. Fatih Tasgetiren; Biao Zhang; Junqing Li; Tasgetiren, M. Fatih; Li, Jun-Qing; Li, Yuan-Zhen; Pan, Quan-Ke; Gao, Kai-Zhou; Zhang, Biao
In recent years sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile affected by the intensification of market competition and economic globalization distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation flowshop (EEDPFSP) with the criteria of minimizing both total flow time and total energy consumption. Considering the distributed and multi-objective optimization complexity an improved NSGAII algorithm (INSGAII) is proposed. First we analyze the problem-specific characteristics and designed new operators based on the knowledge of the problem. Second four constructive heuristic algorithms are proposed to produce high-quality initial solutions. Third inspired by the artificial bee colony algorithm we propose a new colony generation method using the operators designed. Fourth a local intensification is designed for exploiting better non-dominated solutions. The influence of parameter settings is investigated by experiments to determine the optimal parameter configuration of the INSGAII. Finally a large number of computational tests and comparisons have been carried out to verify the effectiveness of the proposed INSGAII in solving EEDPFSP. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 125
Citation - Scopus: 158
A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem
(Pergamon-Elsevier Science Ltd, 2011) Ling Wang; Quanke Pan; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Pan, Quan-Ke; Wang, Ling
This paper proposes a hybrid modified global-best harmony search (hmgHS) algorithm for solving the blocking permutation flow shop scheduling problem with the makespan criterion. First of all the largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Second an efficient initialization scheme based on the Nawaz-Enscore-Ham (NEH) heuristic is presented to construct the initial harmony memory with a certain level of quality and diversity. Third harmony search is employed to evolve harmony vectors in the harmony memory to perform exploration whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. Moreover a new pitch adjustment rule is developed to well inherit good structures from the global-best harmony vector. Computational simulations and comparisons demonstrated the superiority of the proposed hybrid harmony search algorithm in terms of solution quality. © 2011 Elsevier Ltd. All rights reserved. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 88
Citation - Scopus: 105
A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem
(Pergamon-Elsevier Science Ltd, 2011) Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Jing Liang; M. Fatih Tasgetiren; Liang, J. J.; Suganthan, P. N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
In this paper a local-best harmony search (HS) algorithm with dynamic sub-harmony memories (HM) namely DLHS algorithm is proposed to minimize the total weighted earliness and tardiness penalties for a lot-streaming flow shop scheduling problem with equal-size sub-lots. First of all to make the HS algorithm suitable for solving the problem considered a rank-of-value (ROV) rule is applied to convert the continuous harmony vectors to discrete job sequences and a net benefit of movement (NBM) heuristic is utilized to yield the optimal sub-lot allocations for the obtained job sequences. Secondly an efficient initialization scheme based on the NEH variants is presented to construct an initial HM with certain quality and diversity. Thirdly during the evolution process the HM is dynamically divided into many small-sized sub-HMs which evolve independently so as to balance the fast convergence and large diversity. Fourthly a new improvisation scheme is developed to well inherit good structures from the local-best harmony vector in the sub-HM. Meanwhile a chaotic sequence to produce decision variables for harmony vectors and a mutation scheme are utilized to enhance the diversity of the HM. In addition a simple but effective local search approach is presented and embedded in the DLHS algorithm to enhance the local searching ability. Computational experiments and comparisons show that the proposed DLHS algorithm generates better or competitive results than the existing hybrid genetic algorithm (HGA) and hybrid discrete particle swarm optimization (HDPSO) for the lot-streaming flow shop scheduling problem with total weighted earliness and tardiness criterion. © 2010 Elsevier Ltd. All rights reserved. © 2011 Elsevier B.V. All rights reserved.
Citation - WoS: 62
Citation - Scopus: 72
A local-best harmony search algorithm with dynamic subpopulations
(TAYLOR & FRANCIS LTD, 2010) Quan-Ke Pan; P. N. Suganthan; J. J. Liang; M. Fatih Tasgetiren; Liang, J. J.; Suganthan, P. N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that overall the proposed DLHS algorithm is more effective or at least competitive in finding near-optimal solutions compared with state-of-the-art harmony search variants.
Citation - WoS: 13
Citation - Scopus: 15
A memetic algorithm with a variable block insertion heuristic for single machine total weighted tardiness problem with sequence dependent setup times
(Institute of Electrical and Electronics Engineers Inc., 2016) M. Fatih Tasgetiren; Quanke Pan; Yucel Yilmaz Ozturkoglu; Angela Hsiang Ling Chen; Tasgetiren, M. Fatih; Pan, Quan-Ke; Ozturkoglu, Yucel; Chen, Angela H. L.
In this paper a memetic algorithm with a variable block insertion heuristic is presented to solve the single machine total weighted tardiness problem with sequence dependent setup times. Together with the traditional insertion neighborhood structure the memetic algorithm is combined with a variable block insertion heuristic in which a block of jobs are removed from a sequence and then inserted into all possible positions of the partial sequence. For this purpose we devise a variable neighborhood descent algorithm to incorporate different block insertion heuristics having different block sizes. We also employ a simulated annealing type of acceptance criterion to diversify the population. To evaluate its performance the memetic algorithm is tested on a set of benchmark instances from the literature. The analyses of experimental results have shown highly effective performance of the memetic algorithm against the best performing algorithms from the literature. The proposed memetic algorithm was able to find 98 out 120 optimal solutions within reasonable CPU times. © 2017 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: 11
Citation - Scopus: 12
A multi-objective self-adaptive differential evolution algorithm for conceptual high-rise building design
(Institute of Electrical and Electronics Engineers Inc., 2016) Berk Ekici; Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Quanke Pan; Ekici, Berk; Sariyildiz, Sevil; Chatzikonstantinou, Ioannis; Tasgetiren, M. Fatih; Pan, Quan-Ke
This paper presents a multi-objective self-adaptive differential evolution algorithm to solve the form-finding problem of high-rise building design in the conceptual phase. The aim of the research is to reach suitable high-rise design alternatives for hard and soft objectives which are construction cost per square meter structural displacement and visual perception of the spaces from the inside out subject to several constraints that are related with both high-rise construction regulations and profitability of the spaces. We formulate the problem as a multi-objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem we developed two different optimization algorithms namely a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a Self-Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non-dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA-II algorithm. © 2017 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
A New Heuristic for PCBs Grouping Problem with Setup Times
(IEEE Computer Society help@computer.org, 2020) Jiangping Huang; Quanke Pan; M. Fatih Tasgetiren; Yingying Huang; Tasgetiren, M Fatih; Huang, Ying-Ying; Pan, Quan-Ke; Huang, Jiang-Ping; J. Fu , J. Sun
In this paper we present a new heuristic to divide a batch of printed circuit boards (PCBs) into subgroups to save the setup time for loading and unloading components from the assembly machine. In the heuristic we propose several concepts about similarity to make the number of groups as few as possible. To better show the relationship between the PCB types and the component types of a group we introduce a new solution representation. In addition considering the characteristics of the PCBs grouping problem (PGP) a method for pairing PCBs is presented. With the PCB pairs an iterative scheme is applied to start a new group. We try the rest PCBs one by one according to the similarity between it and the PCB group. Finally the experiments and comparisons show the good performance of the proposed heuristic. © 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: 12
Citation - Scopus: 17
A Populated Local Search with Differential Evolution for Blocking Flowshop Scheduling Problem
(IEEE, 2015) M. Fatih Tasgetiren; Quan-Ke Pan; Damla Kizilay; Gursel Suer; Tasgetiren, M. Fatih; Kizilay, Damla; Pan, Quan-Ke; Suer, Gursel
This paper presents a populated local search algorithm through a differential evolution algorithm for solving the blocking flowshop scheduling problem under makespan criterion. Iterated greedy and iterated local search algorithms are simple but extremely effective in solving scheduling problems. However these two algorithms have some parameters to be tuned for which it requires a design of experiments with expensive runs. In this paper we propose a novel multi-chromosome solution representation for both local search and differential evolution algorithm which is responsible for providing the parameters of IG and ILS algorithms. In other words these parameters are learned by the differential evolution algorithm in order to guide the local search process. We also present the greedy randomized adaptive search procedure (GRASP) for the problem on hand. The performance of the populated local search algorithm with differential evolution algorithm and the GRASP heuristic is tested on Taillard's benchmark suite and compared to the best performing algorithms from the literature. Ultimately 90 out of 120 problem instances are further improved.