Browsing by Author "Tasgetiren, M. Fatih"

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A Differential Evolution Algorithm for the Extraction of Complex Natural Resonance Frequencies of Electromagnetic Targets
(SPRINGER-VERLAG BERLIN, 2011) Mustafa Secmen; M. Fatih Tasgetiren; Secmen, Mustafa; Tasgetiren, M. Fatih; DS Huang; Y Gan; V Bevilacqua; JC Figueroa
This paper presents a differential evolution algorithm in order to find unique resonance frequencies of an electromagnetic target in the resonance scattering region. These frequencies are estimated from the roots of Laplace transform of a specially designed incident signal. The parameters of the signal are computed with an intelligent differential evolution (DE) algorithm. The algorithm searches for minimization of the scattered signal's energy in late-time region which is main fitness function in the algorithm. The proposed algorithm is demonstrated for a scattered signal of a dielectric sphere having several poles. The acquired pole results show very good agreement with theoretical expectations. Besides the differential evolution algorithm has higher accuracy as compared to a similar method which utilizes from genetic algorithm.
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 - Scopus: 8
A Differential Evolution Algorithm for the Median Cycle Problem
(IEEE, 2011) M. Fatih Tasgetiren; Quan-Ke Pan; Onder Bulut; P. N. Suganthan; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloǧlu, 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.
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: 8
Citation - Scopus: 9
A differential evolution algorithm with a variable neighborhood search for constrained function optimization
(Springer Verlag service@springer.de, 2015) M. Fatih Tasgetiren; Ponnuthurai Nagaratnam Suganthan; Sel Ozcan; Damla Kizilay; Tasgetiren, M. Fatih; Suganthan, P.N.; Kizilay, Damla; Ozcan, Sel
In this paper a differential evolution algorithm based on a variable neighborhood search algorithm (DE_VNS) is proposed in order to solve the constrained real-parameter optimization problems. The performance of DE algorithm depends on the mutation strategies crossover operators and control parameters. As a result a DE_VNS algorithm that can employ multiple mutation operators in its VNS loops is proposed in order to further enhance the solution quality. We also present an idea of injecting some good dimensional values to the trial individual through the injection procedure. In addition we also present a diversification procedure that is based on the inversion of the target individuals and injection of some good dimensional values from promising areas in the population by tournament selection. The computational results show that the simple DE_VNS algorithm was very competitive to some of the best performing algorithms from the literature. © 2015 Elsevier B.V. All rights reserved.
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: 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: 5
Citation - Scopus: 5
A Discrete Artificial Bee Colony Algorithm for the Assignment and Parallel Machine Scheduling Problem in DYO Paint Company
(IEEE, 2014) Damla Kizilay; M. Fatih Tasgetiren; Onder Bulut; Bilgehan Bostan; Kizilay, Damla; Tasgetiren, M. Fatih; Bulut, Onder; Bostan, Bilgehan
This paper presents a discrete artificial bee colony algorithm to solve the assignment and parallel machine scheduling problem in DYO paint company. The aim of this paper is to develop some algorithms to be employed in the DYO paint company by using their real-life data in the future. Currently in the DYO paint company, there exist three types of filling machines groups. These are automatic semiautomatic and manual machine groups where there are several numbers of identical machines. The problem is to first assign the filling production orders (jobs) to machine groups. Then filling production orders assigned to each machine group should be scheduled on identical parallel machines to minimize the sum of makespan and the total weighted tardiness. We also develop a traditional genetic algorithm to solve the same problem. The computational results show that the DABC algorithm outperforms the GA on set of benchmark problems we have generated.
Citation - WoS: 4
Citation - Scopus: 15
A Discrete Artificial Bee Colony Algorithm For the Economic Lot Scheduling Problem
(IEEE, 2011) M. Fatih Tasgetiren; Onder Bulut; M. Murat Fadiloglu; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat
In this study we present a discrete artificial bee colony (DABC) algorithm to solve the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. In specific our algorithm provides a cyclic production schedule of n items to be produced on a single machine such that the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers are in the form of power-of-two and under EBP approach feasibility is guaranteed with a constraint that checks if the items assigned in each period can be produced within the length of the period. For this problem which is NP-hard our DABC algorithm employs a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also fused into DABC algorithm to further enhance the solution quality. The experimental results show that the proposed algorithm is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy.
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: 5
Citation - Scopus: 11
A Discrete Artificial Bee Colony Algorithm for the Traveling Salesman Problem with Time Windows
(IEEE, 2012) Korhan Karabulut; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Karabulut, Korhan
This paper presents a discrete artificial bee colony algorithm (DABC) for solving the traveling salesman problem with time windows (TSPTW) in order to minimize the total travel cost of a given tour. TSPTW is a difficult optimization problem arising in both scheduling and logistic applications. The proposed DABC algorithm basically relies on the destruction and construction phases of iterated greedy algorithm to generate neighboring food sources in a framework of ABC algorithm. In addition it also relies on a classical 1-opt local search algorithm to further enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is very competitive to or even better than the best performing algorithms from the literature.
Citation - WoS: 15
Citation - Scopus: 22
A discrete event simulation procedure for validating programs of requirements: The case of hospital space planning
(ELSEVIER, 2020) Cemre Cubukcuoglu; Pirouz Nourian; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Nourian, Pirouz; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre
This paper introduces a Discrete-Event Simulation (DES) tool developed as a parametric CAD program for validating a program of requirements (PoR) for hospital space planning. The DES model simulates the procedures of processing of patients treated by doctors calculating patient throughput and patient waiting times based on the number of doctors patient arrivals and treatment times. In addition the tool is capable of defining space requirements by taking hospital design standards into account. Using this tool what-if scenarios and assumptions on the PoR about space planning can be tested and/or validated. The tool is ultimately meant for reducing patient waiting times and/or increasing patient throughput by checking the match of the layout of a hospital with respect to its procedural operations. This tool is envisaged to grow into a toolkit providing a methodological framework for bringing Operations Research into Architectural Space Planning. The tool is implemented in Python for Grasshopper (GH) a plugin of Rhinoceros CAD software using the SimPy library. (C) 2020 The Authors. Published by Elsevier B.V.
Citation - Scopus: 5
A discrete harmony search algorithm for the economic lot scheduling problem with power of two policy
(IEEE, 2012) M. Fatih Tasgetiren; Önder Bulut; Mehmet Murat Fadiloglu; Murat Fadiloglu, M.; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat
In this paper we present a problem specific discrete harmony search (DHS) algorithms to solve the economic lot scheduling problem (ELSP) under the extended basic period (EBP) approach and power-of-two (PoT) policy. In particular DHS algorithms generate a cyclic production schedule consisting of n items to be produced on a single machine where the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers take the form of PoT which restricts the search space but provides good solution qualities. Under the EBP approach feasibility is guaranteed with a constraint checking whether or not the items assigned in each period can be produced within the length of the period. For this restricted problem which is still NP-hard the proposed DHS algorithms employ a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also hybridized with DHS algorithms to further enhance the solution quality. The experimental results show that the proposed algorithms are very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy. © 2012 IEEE. © 2012 Elsevier B.V. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 8
A Dynamic Berth Allocation Problem with Priority Considerations under Stochastic Nature
(SPRINGER-VERLAG BERLIN, 2012) Evrim Ursavas Guldogan; Onder Bulut; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Guldogan, Evrim Ursavas; Bulut, Onder; D Huang; Y Gan; P Gupta; MM Gromiha
Stochastic nature of vessel arrivals and handling times adds to the complexity of the well-known NP-hard berth allocation problem. To aid real decision-making under customer differentiations a dynamic stochastic model designed to reflect different levels of vessel priorities is put forward. For exponential interarrival and handling times a recursive procedure to calculate the objective function value is proposed. To reveal the characteristics of the model numerical experiments based on heuristic approaches are conducted. Solution procedures based on artificial bee colony and genetic algorithms covering both global and local search features are launched to improve the solution quality. The practical inferences led by these approaches are shown to be helpful for container terminals faced with multifaceted priority considerations.
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.