Browsing by Author "Kizilay, Damla"

Now showing 1 - 20 of 21

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: 3
Citation - Scopus: 3
A Novel Differential Evolution Algorithm with Q-Learning for Economical and Statistical Design of X-Bar Control Charts
(Institute of Electrical and Electronics Engineers Inc., 2020) Ahmad Abdulla Al-Buenain; Damla Kizilay; Ozge Buyukdagli; M. Fatih Tasgetiren; Kizilay, Damla; Tasgetiren, M. Fatih; Buyukdagli, Ozge; Al-Buenain, Ahmad Abdulla
This paper presents a novel differential evolution algorithm with Q-Learning (DE_QL) for the economical and statistical design of X-Bar control charts which has been commonly used in industry to control manufacturing processes. In X-Bar charts samples are taken from the production process at regular intervals for measurements of a quality characteristic and the sample means are plotted on this chart. When designing a control chart three parameters should be selected namely the sample size (n) the sampling interval (h) and the width of control limits (k). On the other hand when designing an economical and statistical design these three control chart parameters should be selected in such a way that the total cost of controlling the process should be minimized by finding optimal values of these three parameters. In this paper we develop a DE_QL algorithm for the global minimization of a loss cost function expressed as a function of three variables n h and k in an economic model of the X-bar chart. A problem instance that is commonly used in the literature has been solved and better results are found than the earlier published results. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 3
A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem
(SPRINGER-VERLAG BERLIN, 2013) M. Fatih Tasgetiren; Ozge Buyukdagli; Damla Kizilay; Korhan Karabulut; Tasgetiren, M. Fatih; Kizilay, Damla; Buyukdagli, Ozge; Karabulut, Korhan; BK Panigrahi; PN Suganthan; S Das; SS Dash
In this study we propose a populated iterated greedy algorithm with an Inver-Over operator to solve the traveling salesman problem. The iterated greedy (IG) algorithm is mainly based on the central procedures of destruction and construction. The basic idea behind it is to remove some solution components from a current solution and reconstruct them in the partial solution to obtain the complete solution again. In this paper we apply this idea in a populated manner (IGP) to the traveling salesman problem (TSP). Since the destruction and construction procedure is computationally expensive we also propose an iteration jumping to an Inver-Over operator during the search process. We applied the proposed algorithm to the well-known 14 TSP instances from TSPLIB. The computational results show that the proposed algorithm is very competitive to the recent best performing algorithms from the literature.
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.
Citation - WoS: 7
Citation - Scopus: 11
A variable block insertion heuristic for permutation flowshops with makespan criterion
(IEEE, 2017) M. Fatih Tasgetiren; Quan-Ke Pan; Damla Kizilay; Mario C. Velez-Gallego; Tasgetiren, M. Fatih; Kizilay, Damla; Pan, Quan-Ke; Velez-Gallego, Mario C.
This paper proposes a populated variable block insertion heuristic (PVBIH) algorithm for solving the permutation flowshop scheduling problem with the makespan criterion. The PVBIH algorithm starts with a minimum block size being equal to one. It removes a block from the current solution and inserts it into the partial solution randomly with a predetermined move size. A local search is applied to the solution found after several block moves. If the new solution generated after the local search is better than the current solution it replaces the current solution. It retains the same block size as long as it improves. Otherwise the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the new solution. This process is repeated until the block size reaches at the maximum block size. In addition we present a randomized profile fitting heuristic with excellent results. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed PVBIH algorithm substantially outperforms the differential evolution algorithm (NS-SGDE) recently proposed in the literature.
Citation - WoS: 2
A Variable Block Insertion Heuristic for Single Machine with Release Dates and Sequence Dependent Setup Times for Makespan Minimization
(IEEE, 2019) Jiaxin Fan; Damla Kizilay; Hande Oztop; Kizilay, Damla; Oztop, Hande; Fan, Jiaxin
This paper is concerned with solving the single machine scheduling problem with release dates and sequence-dependent setup times in order to minimize the makespan. For this purpose a variable block insertion heuristic (VBIH) algorithm is applied to the problem. The VBIH algorithm performs block moves on a given solution. Mainly it removes a block of jobs with a given size from the solution and inserts the block into the best position of the partial solution. Furthermore we present a novel profile-fitting constructive heuristic for the problem. We evaluate the performance of the VBIH algorithm by comparisons with the beam search heuristic and the iterated greedy algorithm from the literature. Extensive computational results on the benchmark suite consisting of 900 instances from the literature show that the proposed VBIH algorithm is very competitive to the recent beam search heuristic and iterated greedy algorithm from the literature.
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 - 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: 31
Citation - Scopus: 33
An evolution strategy approach for the distributed blocking flowshop scheduling problem
(Elsevier Ltd, 2022) Korhan Karabulut; Damla Kizilay; M. Fatih Tasgetiren; Liang Gao; Levent Kandiller; Kizilay, Damla; Tasgetiren, M. Fatih; Gao, Liang; Karabulut, Korhan; Kandiller, Levent
Scheduling in distributed production environments has become common in recent years since the advantages of multi factory manufacturing have been growing. This paper examines the distributed blocking flowshop scheduling problem (DBFSP) to minimize the makespan. Two different mathematical models namely a mixed-integer programming model and a constraint programming model were proposed to solve the considered problem to optimality. Due to the NP-Hard nature of the problem large-size instances cannot be solved by the mathematical models and an evolutionary algorithm was proposed. Three different NEH-based heuristics were used and the first three solutions are included in the initial population whereas the rest is constructed randomly. The offspring population is generated by the self-adaptive destruction and construction (DC) procedure of the iterated greedy algorithm. Self-adaptive DC procedure is achieved by the evolution strategy approach. In the local search part of the algorithm a variable local search with three neighborhood structures was applied to the solution obtained by the DC procedure. The developed mathematical models initially verified the performance of the metaheuristic algorithm by using small instances. Then the proposed algorithm was tested on the benchmark suite from the literature. The computational results indicate that the proposed algorithm outperforms the other metaheuristic algorithms from the literature. Finally the solutions of the 156 best so far were obtained by the proposed algorithm which is more effective than the existing state-of-the-art methods. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 42
Citation - Scopus: 45
An evolution strategy approach for the distributed permutation flowshop scheduling problem with sequence-dependent setup times
(Elsevier Ltd, 2022) Korhan Karabulut; Hande Oztop; Damla Kizilay; M. Fatih Tasgetiren; Levent Kandiller; Kizilay, Damla; Tasgetiren, M. Fatih; Karabulut, Korhan; Oztop, Hande; Kandiller, Levent
This paper considers a distributed permutation flowshop scheduling problem with sequence-dependent setup times (DPFSP-SDST) to minimize the maximum completion time among the factories. The global economy has enabled large companies to have distributed production centers to become widespread and effective production scheduling between these centers plays a vital role in the competitiveness of companies. To provide effective scheduling for the DPFSP-SDST we propose a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model which is presented for the first time in literature to the best of our knowledge. As the CP has become a solid competitor to the MILP in the literature this study aims to exploit the effectiveness of CP to solve such a complex DPFSP-SDST. Since the problem is NP-hard we also offer an evolution strategy (ES_en) algorithm that employs a self-adaptive scheme to obtain high-quality solutions in a short time. A ruin-and-recreate procedure is also embedded into the developed ES_en. We calibrate the parameters of the proposed ES_en using a design of experiment approach. We also compare the proposed ES_en algorithm's performance with three state-of-the-art metaheuristic algorithms from the literature i.e. the IG2S (a variant of an iterated greedy algorithm with NEH2_en initialization) IGR (another variant of an iterated greedy algorithm with a restart scheme) and discrete artificial bee colony (DABC) algorithm. A detailed computational experiment is carried out to evaluate the performance of the mathematical models (MILP and CP) and the heuristic algorithms (ES_en IG2S IGR and DABC). A comprehensive benchmark set is generated for the DPFSP-SDST from the well-known PFSP instances from the literature considering various combinations of jobs machines factories and SDST settings resulting in 2880 benchmark instances. For 216 out of 240 small-size instances optimal results are reported by solving the proposed MILP and CP models whereas time-limited model results are reported for the rest. The computational results show that the CP model outperforms the MILP model in terms of the solution time for small-size instances. Initially the performance of the heuristic algorithms is verified concerning the optimal results on small-size instances. Then the performance of the heuristic algorithms is evaluated for large instances. ES_en algorithm significantly outperforms the IG2S IGR and DABC algorithms for solving large instances. The computational results show that the proposed ES_en algorithm is robust and provides good-quality solutions for the DPFSP-SDST in a short computational time. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 16
Citation - Scopus: 19
An Iterated Greedy Algorithm for the Hybrid Flowshop Problem with Makespan Criterion
(IEEE, 2014) Damla Kizilay; M. Fatih Tasgetiren; Quan-Ke Pan; Ling Wang; Kizilay, Damla; Tasgetiren, M. Fatih; Pan, Quan-Ke; Hu, XiaoLu; Wang, Ling; Chen, Shuai
The main contribution of this paper is to present some novel constructive heuristics for the the hybrid flowshop scheduling (HFS) problem with the objective of minimizing the makespan for the first time in the literature. We developed the constructive heuristics based the profile fitting heuristic by exploiting the waiting time feature of the HFS problem. In addition we also developed an IG algorithm with a simple insertion based local search for the first time in the literature too. The benchmark suite developed for the HFS problem are used to test the performance of the constructive heuristics and the IG algorithm. The computational results show that constructive heuristics developed were able to further improve the traditional NEH heuristics for the HFS problem with makespan criterion. Furthermore with a very short CPU times of 50nm miliseconds the performance of the IG algorithm was very competitive to the PSO and AIS algorithms that were run for 1600 seconds.
Citation - WoS: 13
Citation - Scopus: 19
Constraint and mathematical programming models for integrated port container terminal operations
(Springer Verlag service@springer.de, 2018) Damla Kizilay; D. T. Eliiyi; Pascal van Hentenryck; Kizilay, Damla; Van Hentenryck, Pascal; Eliiyi, Deniz Tursel; W.-J. van Hoeve
This paper considers the integrated problem of quay crane assignment quay crane scheduling yard location assignment and vehicle dispatching operations at a container terminal. The main objective is to minimize vessel turnover times and maximize the terminal throughput which are key economic drivers in terminal operations. Due to their computational complexities these problems are not optimized jointly in existing work. This paper revisits this limitation and proposes Mixed Integer Programming (MIP) and Constraint Programming (CP) models for the integrated problem under some realistic assumptions. Experimental results show that the MIP formulation can only solve small instances while the CP model finds optimal solutions in reasonable times for realistic instances derived from actual container terminal operations. © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 99
Citation - Scopus: 113
Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion
(Elsevier Ltd, 2017) M. Fatih Tasgetiren; Damla Kizilay; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Tasgetiren, M. Fatih; Kizilay, Damla; Suganthan, P.N.; Pan, Quan-Ke
Recently iterated greedy algorithms have been successfully applied to solve a variety of combinatorial optimization problems. This paper presents iterated greedy algorithms for solving the blocking flowshop scheduling problem (BFSP) with the makespan criterion. Main contributions of this paper can be summed up as follows. We propose a constructive heuristic to generate an initial solution. The constructive heuristic generates better results than those currently in the literature. We employ and adopt well-known speed-up methods from the literature for both insertion and swap neighborhood structures. In addition an iteration jumping probability is proposed to change the neighborhood structure from insertion neighborhood to swap neighborhood. Generally speaking the insertion neighborhood is much more effective than the swap neighborhood for the permutation flowshop scheduling problems. Instead of considering the use of these neighborhood structures in a framework of the variable neighborhood search algorithm two powerful local search algorithms are designed in such a way that the search process is guided by an iteration jumping probability determining which neighborhood structure will be employed. By doing so it is shown that some additional enhancements can be achieved by employing the swap neighborhood structure with a speed-up method without jeopardizing the effectiveness of the insertion neighborhood. We also show that the performance of the iterated greedy algorithm significantly depends on the speed-up method employed. The parameters of the proposed iterated greedy algorithms are tuned through a design of experiments on randomly generated benchmark instances. Extensive computational results on Taillard's well-known benchmark suite show that the iterated greedy algorithms with speed-up methods are equivalent or superior to the best performing algorithms from the literature. Ultimately 85 out of 120 problem instances are further improved with substantial margins. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 5
Mathematical models for the periodic vehicle routing problem with timewindows and time spread constraints
(Ramazan Yaman, 2021) Damla Kizilay; Hande Öztop; Zeynel Abidin ÇİL; Kizilay, Damla; Öztop, Hande; Çil, Zeynel Abidin
The periodic vehicle routing problem (PVRP) is an extension of the well-knownvehicle routing problem. In this paper the PVRP with time windows and timespread constraints (PVRP-TWTS) is addressed which arises in the high-valueshipment transportation area. In the PVRP-TWTS period-specific demands of thecustomers must be delivered by a fleet of heterogeneous capacitated vehicles overthe several planning periods. Additionally the arrival times to a customer shouldbe irregular within its time window over the planning periods and the waiting timeis not allowed for the vehicles due to the security concerns. This study proposes novel mixed-integer linear programming (MILP) and constraint programming(CP) models for the PVRP-TWTS. Furthermore we develop several validinequalities to strengthen the proposed MILP and CP models as well as a lowerbound. Even though CP has successful applications for various optimizationproblems it is still not as well-known as MILP in the operations research field.This study aims to utilize the effectiveness of CP in solving the PVRP-TWTS. This study presents a CP model for PVRP-TWTS for the first time in the literature to the best of our knowledge. Having a comparison of the CP and MILP models can help in providing a baseline for the problem. We evaluate the performance ofthe proposed MILP and CP models by modifying the well-known benchmark setfrom the literature. The extensive computational results show that the CP modelperforms much better than the MILP model in terms of the solution quality. 4
Citation - Scopus: 6
Optimization of Costs in Empty Container Repositioning
(Springer Science and Business Media Deutschland GmbH, 2020) Mehmet Yasin Göçen; Öykü Çağlar; Elif Ercan; Damla Kizilay; Ercan, Elif; Kizilay, Damla; Göçen, Mehmet Yasin; Çağlar, Öykü; M.N. Osman Zahid , R. Abd. Aziz , A.R. Yusoff , N. Mat Yahya , F. Abdul Aziz , M. Yazid Abu , N.M. Durakbasa , M.G. Gençyilmaz
In this study the empty container repositioning problem in a liner shipping company is considered. The research includes necessary information about the company and maritime transportation highlighting the importance of empty container storage. As the worldwide containerized trade increases the container traffic increases. Consequently the surplus containers are repositioned to locations where they are required which causes high costs. Therefore in the existing system the company deals with higher prices. In this study the storage location of the empty containers which are coming from the vessels to the ports are determined according to the different cost policies of the warehouses. Thus our problem is defined as an empty container repositioning problem. The primary objective of this study is to build an operations-research based decision-making system in order to minimize the total cost regarding changing prices of warehouses and transportation cost of containers. In the decision-making process this study will help the company to overcome the complexity about repositioning empty containers regarding the container size storage day of the containers changing inventory holding costs of the warehouses and the transportation costs of the containers from port to the corresponding warehouses. Taking into consideration all these parts of the problem this study will eventually help in reducing inventory holding and transportation costs of the empty container storage. As a solution methodology the mixed integer linear programming model is proposed and optimal solutions are obtained in a concise time. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 5
Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms
(Growing Science, 2024) M. Fatih Tasgetiren; Damla Kizilay; Levent Kandiller; Tasgetiren, M. Fatih; Kizilay, Damla; Kandiller, Levent
This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters mainly the destruction size and temperature scale factor adaptively during the search process. Besides IGQ algorithms two different mathematical modeling tech-niques. One of these techniques is the constraint programming (CP) model which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms. © 2024 Elsevier B.V. All rights reserved.