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Oğuz, Ceyda

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Ceyda

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Researcher Of Publications: search.filters.isAuthorOfPublication.8b70c72a-a899-4dad-894d-d10ed4c10c25

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Now showing 1 - 10 of 28
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    Solution approaches for simultaneous scheduling of jobs and operators on parallel machines
    (Gazi Üniversitesi, 2012) Edis, Emrah B.; Özkarahan, Irem; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033
    Production scheduling and machine maintenance are two inseparable operational issues in multistage production systems. Previous studies attempted to deal with this issue by simplifying this problem due to the degradation uncertainties of the machines, ignoring the substantial interactions between these two tasks and leading to less efficiency of the entire production system. In this study, we fill the gap and formulate the joint optimization problem with more emphasis on the interaction between job scheduling and maintenance for a series-parallel multistage production system. Specifically, a mixed-effect degradation model is proposed to leverage the underlying interaction between job scheduling and machine maintenance. To efficiently solve this joint problem, several properties from this formulation have been derived. A two-phase method considering condition-based information, with a proactive algorithm for local intensification and a condition-based workload reallocation strategy & maintenance strategy, is then developed to address the uncertainties from the machine degradation status. A numerical study is finally borrowed to demonstrate the higher production efficiency achieved by applying the proposed method, compared with other benchmarks. —This study is motivated by a practical scenario where both job allocation and maintenance need to be determined simultaneously in the multistage production system by the operators to achieve time and cost efficiency. We focus on developing a new scheme that job scheduling and machine maintenance are able to be conducted simultaneously. Two issues are noteworthy to better implement this scheme. First, for characterizing the interaction between scheduling and maintenance, the data collected in real-time can provide a sufficient basis for the degradation path, and the production parameters can be acquired from real practice. Second, this scheme can be offered to help decision-making by a two-phase solution framework given the condition-based information during the production process. Specifically, an appropriate job allocation planning can be obtained offline in the first phase of the proposed two-phase solution framework under a limited computing resource. Meanwhile, a condition-based adjustment strategy in the second phase can update the solution based on the in-situ condition information collected from the data platform to achieve higher production efficiency.
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    Generalized order acceptance and scheduling problem with batch delivery: models and metaheuristics
    (Pergamon-Elsevier Science Ltd, 2021) N/A; Department of Industrial Engineering; Tarhan, İstenç; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6033
    This paper addresses an extended version of the generalized order acceptance and scheduling problem by including the logistics aspects into the production scheduling decisions. While order acceptance and scheduling feature of the problem includes the joint decision of which orders to accept and how to schedule them due to the limited capacity in production environment and due to the order delivery time requirements for the customers, logistics aspect of the problem entails the decision of how to batch the accepted orders for the delivery in conjunction with the production scheduling. The objective is to maximize the net revenue in line with the literature of order acceptance and scheduling problem. We first present a mixed integer linear programming and a constraint programming model for this problem. To tackle large size problem instances in which these models fail, we propose an iterated local search algorithm using a new local search scheme. To evaluate the performance of the proposed local search scheme, a variant of this algorithm is developed which replaces the relevant scheme with tabu search. Computational results show that the proposed models achieve small optimality gaps for the small size problems, but their performances deteriorate significantly as the problem size enlarges. For the large size problem instances, the iterated local search algorithm using the proposed local search scheme achieves smaller optimality gaps compared to the one with the tabu search algorithm.
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    Constraint programming approach to quay crane scheduling problem
    (Pergamon-Elsevier Science Ltd, 2013) N/A; N/A; Department of Industrial Engineering; Ünsal, Özgür; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 328856; 6033
    This study presents a constraint programming (CP) model for the quay crane scheduling problem (QCSP), which occurs at container terminals, with realistic constraints such as safety margins, travel times and precedence relations. Next, QCSP with time windows and integrated crane assignment and scheduling problem, are discussed. The performance of the CP model is compared with that of algorithms presented in QCSP literature. The results of the computational experiments indicate that the CP model is able to produce good results while reducing the computational time, and is a robust and flexible alternative for different types of crane scheduling problems.
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    DNA sequencing by hybridization via genetic search
    (2006) Blazewicz, Jacek; Swiercz, Aleksandra; Weglarz, Jan; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033
    An innovative approach to DNA sequencing by hybridization utilizes isothermic oligonucleotide libraries. In this paper, we demonstrate the utility of a genetic algorithm for the combinatorial portion of this new approach by incorporating characteristics of DNA sequencing by hybridization in addition to isothermic oligonucleotide libraries. Specialized crossover and mutation operators were developed for this purpose. After initial experiments for parameter adjustment, the performance of the genetic algorithm approach was evaluated with respect to previous methods in the literature. The results indicate that the proposed new approach is superior to previous approaches. The proposed new crossover operator that inherits some features of the structured weighted combinations might also be of value for some other combinatorial problems, including the traveling salesman problem.
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    Order acceptance and scheduling decisions in make-to-order systems
    (Elsevier Science Bv, 2010) Yalçın, Zehra Bilgintuerk; Department of Industrial Engineering; Department of Industrial Engineering; Oğuz, Ceyda; Salman, Fatma Sibel; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 6033; 178838
    We examine simultaneous order acceptance and scheduling decisions where the orders are defined by their release dates, due dates, deadlines, processing times, sequence dependent setup times and revenues in a single machine environment. The objective is to maximize total revenue, where the revenue from an order is a function of its tardiness and deadline. We give an MILP formulation which can be solved to optimality up to 15 orders. We develop three heuristic algorithms to solve large sized problems. Computational tests indicate that the proposed algorithms are both computationally efficient and effective even for instances up to 300 orders.
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    Berth and quay crane allocation: a moldable task scheduling model
    (Taylor & Francis, 2011) Blazewicz, Jacek; Cheng, T. C. E.; Machowiak, Maciej; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033
    We study the problem of allocating berths to incoming ships and assigning the necessary quay cranes to the ships at a port container terminal. We formulate the problem as the moldable task scheduling problem by considering the tasks as ships and processors as quay cranes assigned to the ships based on the observation that the berthing duration of a ship depends on the number of quay cranes allocated to it. In the model, the processing speed of a task is considered to be a non-linear function of the number of processors allocated to it. We present a suboptimal algorithm that obtains a feasible solution to the discrete version of the problem from the continuous version, that is, where the tasks may require fractional quantities of the resources. We conducted computational experiments to evaluate the performance of the algorithm. The computational results show that the average behaviour of the algorithm is very good.
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    A hyper-heuristic approach to sequencing by hybridization of DNA sequences
    (Springer, 2013) Blazewicz, Jacek; Burke, Edmund K.; Kendall, Graham; Mruczkiewicz, Wojciech; Swiercz, Aleksandra; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033
    In this paper we investigate the use of hyper-heuristic methodologies for predicting DNA sequences. In particular, we utilize Sequencing by Hybridization. We believe that this is the first time that hyper-heuristics have been investigated in this domain. A hyper-heuristic is provided with a set of low-level heuristics and the aim is to decide which heuristic to call at each decision point. We investigate three types of hyper-heuristics. Two of these (simulated annealing and tabu search) draw their inspiration from meta-heuristics. The choice function hyper-heuristic draws its inspiration from reinforcement learning. We utilize two independent sets of low-level heuristics. The first set is based on a previous tabu search method, with the second set being a significant extension to this basic set, including utilizing a different representation and introducing the definition of clusters. The datasets we use comprises two randomly generated datasets and also a publicly available biological dataset. In total, we carried out experiments using 70 different combinations of heuristics, using the three datasets mentioned above and investigating six different hyper-heuristic algorithms. Our results demonstrate the effectiveness of a hyper-heuristic approach to this problem domain. It is necessary to provide a good set of low-level heuristics, which are able to both intensify and diversify the search but this approach has demonstrated very encouraging results on this extremely difficult and important problem domain.
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    Performance of local search heuristics on scheduling a class of pipelined multiprocessor tasks
    (Pergamon-Elsevier Science Ltd, 2005) Ercan, MF; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033
    This paper presents the evaluation of the solution quality of heuristic algorithms developed for scheduling multiprocessor tasks for a class of multiprocessor architectures designed to exploit temporal and spatial parallelism simultaneously. More specifically, we deal with multi-level or partitionable architectures where MIMD parallelism and multiprogramming support are the two main characteristics of the system. We investigate scheduling a number of pipelined multiprocessor tasks with arbitrary processing times and arbitrary processor requirements in this system. The scheduling problem consists of two interrelated sub-problems, which are finding a sequence of pipelined multiprocessor tasks on a processor and finding a proper mapping of tasks to the processors that are already being sequenced. For the solution of the second problem, various techniques are available. However, the problem remains of generating a feasible sequence for the pipelined operations. We employed three well-known local search heuristic algorithms that are known to be robust methods applicable to various optimization problems. These are Simulated Annealing, Tabu Search, and Genetic Algorithms. We then conduct computational experiments and evaluate the reduction achieved in completion time by each heuristic. We have also compared the results with well-known simple list-based heuristics. (c) 2005 Elsevier Ltd. .
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    Hybrid adaptive large neighborhood search for the optimal statistic median problem
    (Pergamon-Elsevier Science Ltd, 2012) Katterbauer, Klemens; Department of Industrial Engineering; Department of Industrial Engineering; Oğuz, Ceyda; Salman, Fatma Sibel; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 6033; 178838
    In this paper, the problem of maximizing the median of a convex combination of vectors having important applications in finance is considered. The objective function is a highly nonlinear, nondifferentiable function with many local minima and the problem was shown to be APX hard. We present two hybrid Large Neighborhood Search algorithms that are based on mixed-integer programs and include a time limit for their running times. We have tested the algorithms on three testbeds and showed their superiority compared to other state-of-the-art heuristics for the considered problem. Furthermore, we achieved a significant reduction in running time for large instances compared to solving it exactly while retaining high quality of the solutions returned.
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    Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning
    (Oxford University Press (OUP), 2019) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Dereli, Onur; Oğuz, Ceyda; Gönen, Mehmet; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 6033; 237468
    Motivation: Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. Results: We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used).