Browsing by Author "Fatih Tasgetiren, M."

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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: 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 - WoS: 5
Citation - Scopus: 7
A Memetic Algorithm for the Bi-Objective Quadratic Assignment Problem
(ELSEVIER SCIENCE BV, 2019) Cemre Cubukcuoglu; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Liang Gao; Murat Kucukvar; Tasgetiren, M. Fatih; Kucukvar, Murat; Sariyildiz, I. Sevil; Fatih Tasgetiren, M.; Cubukcuoglu, Cemre; Sevil Sariyildiz, I.; Gao, Liang; CH Dagli; GA Suer
Recently multi-objective evolutionary algorithms (MOEAs) have been extensively used to solve multi-objective optimization problems (MOPs) since they have the ability to approximate a set of non-dominated solutions in reasonable CPU times. In this paper we consider the bi-objective quadratic assignment problem (bQAP) which is a variant of the classical QAP which has been extensively investigated to solve several real-life problems. The bQAP can be defined as having many input flows with the same distances between the facilities causing multiple cost functions that must be optimized simultaneously. In this study we propose a memetic algorithm with effective local search and mutation operators to solve the bQAP. Local search is based on swap neighborhood structure whereas the mutation operator is based on ruin and recreate procedure. The experimental results show that our bi-objective memetic algorithm (BOMA) substantially outperforms all the island-based variants of the PASMOQAP algorithm proposed very recently in the literature. (C) 2019 The Authors. Published by Elsevier Ltd.
Citation - WoS: 48
Citation - Scopus: 55
A variable iterated greedy algorithm for the traveling salesman problem with time windows
(ELSEVIER SCIENCE INC, 2014) Korhan Karabulut; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Karabulut, Korhan; Fatih Tasgetiren, M.
This paper presents a variable iterated greedy algorithm for solving the traveling salesman problem with time windows (TSPTW) to identify a tour minimizing the total travel cost or the makespan separately. The TSPTW has several practical applications in both production scheduling and logistic operations. The proposed algorithm basically relies on a greedy algorithm generating an increasing number of neighboring solutions through the use of the idea of neighborhood change in variable neighborhood search (VNS) algorithms. In other words neighboring solutions are generated by destructing a solution component and re-constructing the solution again with variable destruction sizes. In addition the proposed algorithm is hybridized with a VNS algorithm employing backward and forward 1_Opt local searches to further enhance the solution quality. The performance of the proposed algorithm was tested on several benchmark suites from the literature. Experimental results confirm that the proposed algorithm is either competitive to or even better than the best performing algorithms from the literature. Ultimately new best-known solutions are obtained for 38 out of 125 problem instances of the recently proposed benchmark suite whereas 15 out of 31 problem instances are also further improved for the makespan criterion. (C) 2014 Elsevier Inc. All rights reserved.
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.
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 - Scopus: 8
An energy-efficient single machine scheduling with release dates and sequence-dependent setup times
(Association for Computing Machinery Inc acmhelp@acm.org, 2018) Uǧur Eliiyi; M. Fatih Tasgetiren; Damla Kizilay; Hande Oztop; Quanke Pan; Kizilay, Damla; Fatih Tasgetiren, M.; Öztop, Hande; Pan, Quan-Ke; Eliiyi, Uğur
This study considers single machine scheduling with the machine operating at varying speed levels for different jobs with release dates and sequence-dependent setup times in order to examine the trade-off between makespan and total energy consumption. A bi-objective mixed integer linear programming model is developed employing this speed scaling scheme. The augmented ε-constraint method with a time limit is used to obtain a set of non-dominated solutions for each instance of the problem. An energy-efficient multi-objective variable block insertion heuristic is also proposed. The computational results on a benchmark suite consisting of 260 instances with 25 jobs from the literature reveal that the proposed algorithm is very competitive in terms of providing tight Pareto front approximations for the problem. © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 107
Citation - Scopus: 125
An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem
(Elsevier Science Inc, 2010) M. Fatih Tasgetiren; Ponnuthurai Nagaratnam Suganthan; Quanke Pan; Suganthan, P.N.; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Pan, Quan-Ke
In this paper an ensemble of discrete differential evolution algorithms with parallel populations is presented. In a single populated discrete differential evolution (DDE) algorithm the destruction and construction (DC) procedure is employed to generate the mutant population whereas the trial population is obtained through a crossover operator. The performance of the DDE algorithm is substantially affected by the parameters of DC procedure as well as the choice of crossover operator. In order to enable the DDE algorithm to make use of different parameter values and crossover operators simultaneously we propose an ensemble of DDE (eDDE) algorithms where each parameter set and crossover operator is assigned to one of the parallel populations. Each parallel parent population does not only compete with offspring population generated by its own population but also the offspring populations generated by all other parallel populations which use different parameter settings and crossover operators. As an application area the well-known generalized traveling salesman problem (GTSP) is chosen where the set of nodes is divided into clusters so that the objective is to find a tour with minimum cost passing through exactly one node from each cluster. The experimental results show that none of the single populated variants was effective in solving all the GTSP instances whereas the eDDE performed substantially better than the single populated variants on a set of problem instances. Furthermore through the experimental analysis of results the performance of the eDDE algorithm is also compared against the best performing algorithms from the literature. Ultimately all of the best known averaged solutions for larger instances are further improved by the eDDE algorithm. © 2009 Elsevier Inc. All rights reserved. © 2009 Elsevier B.V. All rights reserved.
Citation - WoS: 103
Citation - Scopus: 129
Dynamic multi-swarm particle swarm optimizer with harmony search
(PERGAMON-ELSEVIER SCIENCE LTD, 2011) S. -Z. Zhao; P. N. Suganthan; Quan-Ke Pan; M. Fatih Tasgetiren; Suganthan, P.N.; Tasgetiren, M. Fatih; Zhao, S.-Z.; Fatih Tasgetiren, M.; Pan, Quan-Ke
In this paper the dynamic multi-swarm particle swarm optimizer (DMS-PSO) is improved by hybridizing it with the harmony search (HS) algorithm and the resulting algorithm is abbreviated as DMS-PSO-HS. We present a novel approach to merge the HS algorithm into each sub-swarm of the DMS-PSO. Combining the exploration capabilities of the DMS-PSO and the stochastic exploitation of the HS the DMS-PSO-HS is developed. The whole DMS-PSO population is divided into a large number of small and dynamic sub-swarms which are also individual HS populations. These sub-swarms are regrouped frequently and information is exchanged among the particles in the whole swarm. The DMS-PSO-HS demonstrates improved on multimodal and composition test problems when compared with the DMS-PSO and the HS. (C) 2010 Elsevier Ltd. 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: 9
Citation - Scopus: 14
Iterated greedy algorithms for the hybrid flowshop scheduling with total flow time minimization
(Association for Computing Machinery Inc acmhelp@acm.org, 2018) Hande Oztop; D. T. Eliiyi; M. Fatih Tasgetiren; Quanke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Öztop, Hande; Pan, Quan-Ke; Eliiyi, Deniz Türsel
1 The hybrid flosshop scheduling problem (HFSP) has been extensively studied in the literature due to its complexity and real-life applicability. Various exact and heuristic algorithms have been developed for the HFSP and most consider makespan as the only criterion. The studies on HFSP sith the objective of minimizing total flos time have been rather limited. This paper presents a mathematical model and efficient iterated greedy algorithms IG and IGALL for the HFSP sith total flos time criterion. In order to evaluate the performance of the proposed IG algorithms the sell-knosn HFSP benchmark suite from the literature is used. As the problem is NP-hard the proposed mathematical model is solved for all 87 instances under a time limit on CPLEX. Optimal results are obtained for some of these instances. The performance of the IG algorithms is measured by comparisons sith these time-limited CPLEX results of the mathematical model. Computational results shos that the proposed IG algorithms perform very sell in terms of solution time and quality. To the best of our knosledge for the first time in the literature the results of flos time criterion have been reported for the HFSP benchmark suite. © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 3
Lagrangian heuristic for scheduling a steelmaking-continuous casting process
(IEEE, 2013) Kun Mao; Quanke Pan; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Mao, Kun; Pan, Quanke
One of the biggest bottlenecks in iron and steel production is the steelmaking-continuous casting (SCC) process which consists of steel-making re ning and continuous casting. The production scheduling of SCC is a complex hybrid owshop (HFS) scheduling with following features: job grouping and precedence constraints no dead time inside the same group of jobs setup time constraints on the casters. A mixed-integer programming (MIP) model is established with the objective of minimizing the total weighted penalties of the earliness/tardiness and the job waiting. Through relaxing the operation precedence constraints to the objective function the relaxed problem can be decomposed into to smaller subproblems each of which corresponds a speciCE stage. A new dynamic programming algorithm is developed for solving the subproblems which are parallel machine scheduling problem with objective of minimizing total weighted completion time where the weights of jobs may be negative. The Lagrangian dual problem is solved by an improved subgradient level algorithm which can guarantee global convergence. A novel heuristic is presented to adjust subproblem solutions to obtain a feasible schedule. The computational results demonstrate that the propose LR approach can generate a high quality schedule within an acceptable computation time.
Citation - WoS: 79
Citation - Scopus: 94
Metaheuristic algorithms for the hybrid flowshop scheduling problem
(PERGAMON-ELSEVIER SCIENCE LTD, 2019) Hande Oztop; M. Fatih Tasgetiren; Deniz Tursel Eliiyi; Quan-Ke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Öztop, Hande; Pan, Quan-Ke; Eliiyi, Deniz Türsel
The hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature as it has many real-life applications in industry. Even though many solution approaches have been presented for the HFSP with makespan criterion studies on HFSP with total flow time minimization have been rather limited. This study presents a mathematical model four variants of iterated greedy algorithms and a variable block insertion heuristic for the HFSP with total flow time minimization. Based on the well-known NEH heuristic an efficient constructive heuristic is also proposed and compared with NEH. A detailed design of experiment is carried out to calibrate the parameters of the proposed algorithms. The HFSP benchmark suite is used for evaluating the performance of the proposed methods. As there are only 10 large instances in the current literature further 30 large instances are proposed as new benchmarks. The developed model is solved for all instances on CPLEX under a time limit and the performances of the proposed algorithms are assessed through comparisons with the results from CPLEX and the two best-performing algorithms in literature. Computational results show that the proposed algorithms are very effective in terms of solution time and quality. Additionally the proposed algorithms are tested on large instances for the makespan criterion which reveal that they also perform superbly for the makespan objective. Especially for instances with 30 jobs the proposed algorithms are able to find the current incumbent makespan values reported in literature and provide three new best solutions. (C) 2019 Elsevier Ltd. All rights reserved.
Citation - WoS: 6
Citation - Scopus: 10
Multi-performance based computational model for the cuboid open traveling salesman problem in a smart floating city
(PERGAMON-ELSEVIER SCIENCE LTD, 2021) Ayca Kirimtat; Ondrej Krejcar; M. Fatih Fatih Tasgetiren; Enrique Herrera-Viedma; Krejcar, Ondrej; Herrera-Viedma, Enrique; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Kirimtat, Ayca
The term ?smart city? has been emerged as a novel solution to uphold the useless urban areas and the term has taken the advantage of sustainable and environmental resources. On the other hand the term ?floating city? has been studied for just only a few years as alternative living spaces for humanity across the world since land scarcity has already begun. Therefore in this research we propose multi-objective optimization algorithms to obtain the Pareto front solutions for the cuboid open traveling salesman problem (COTSP) in a ?smart floating city? context. Given n nodes and the distances between each pair of nodes the COTSP in this paper aims to find the shortest possible tour with a traveling distance that starts from the depot (i.e. node 1) and visits each node exactly once without needing to return to the depot. As known a cuboid has height length and depth and the COTSP defines its x y z coordinates as a cuboid corresponding to height length and depth. In addition to the traveling distance the platform (building breakwaters) cost is measured by the z coordinates (depths) of the nodes/platforms that represent both the platforms below the sea level. Note that unlike the traditional TSP it has a variable seed number and a variable number of nodes/platforms in each solution. The paper aims to find the Pareto front solutions by minimizing the traveling distance and platform cost of the infrastructures below the sea level simultaneously. We develop a multi-objective self-adaptive differential evolution (MOJDE) algorithm a nondominated sorting genetic algorithm (NSGAII) and a harmony search (MOHS) algorithm to solve the problem in such a way that we minimize the traveling distance while minimizing the platform cost simultaneously. All algorithms are compared to each other. The computational results show that the MOJDE and NSGAII algorithms outperform the MOHS algorithm in terms of commonly used performance measures from the literature.