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Publication Metadata only A bi-objective model for design and analysis of sustainable intermodal transportation systems: A case study of Turkey(Taylor & Francis Ltd, 2019) Reşat, Hamdi Giray; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a mixed-integer linear optimisation model to analyse the intermodal transportation systems in the Turkish transportation industry. The solution approach includes mathematical modelling, data analysis from real-life cases and solving the resulting mathematical programming problem to minimise total transportation cost and carbon dioxide emissions by using two different exact solution methods in order to find the optimal solutions. The novel approach of this paper generates Pareto solutions quickly and allows the decision makers to identify sustainable solutions by using a newly developed solution methodology for bi-objective mixed-integer linear problems in real-life cases.Publication Metadata only A decision support framework for evaluating revenue performance in sequential purchase contexts(Elsevier Science Bv, 2017) Öztürk, O. Cem; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819This paper studies the product ordering problem in sequential purchase contexts where sellers aim to maximize their revenue faced with budget constrained buyers. We propose a multi-layered decision support framework that combines empirical data with simulation, optimization, and econometric methods to address this problem. Our framework allows sellers to: (i) compare revenue performances of limited information sequencing strategies, (ii) quantify benchmark revenue levels that can be achieved via the optimal sequence based on detailed buyer information, (iii) determine the costs of limited information and strategic buyers to the seller, and (iv) identify the moderators of sequencing strategy performance. We illustrate our framework through two applications in a business-to-business used-car auction setting. Contrary to previous studies reporting practitioners’ tendency to sequence items from the lowest value to the highest, our results suggest that the best-performing limited information sequencing strategy depends on buyers’ bidding behavior. We also find that the revenue difference between the optimal sequence and a limited information sequencing strategy can be substantial. Our results show that a significant portion of this revenue difference is associated with the seller’s limited information on buyers’ budgets and product valuations. Our applications also provide various sensitivity analyses and develop new propositions on the moderators of the relationship between the seller’s revenue and sequencing strategies.Publication Metadata only A decomposition model for continuous materials flow production systems(Taylor & Francis, 1997) Yeralan, Sencer; Department of Business Administration; N/A; Tan, Barış; Faculty Member; N/A; Department of Business Administration; College of Administrative Sciences and Economics; N/A; 28600; N/AThis study presents a general and flexible decomposition method for continuous materials flow production systems. The decomposition method uses the station model developed in the first part of this study (Yeralan and Tan 1997). The decomposition method is an iterative method. At each iteration the input and output processes of the station model are matched to the most recent solutions of the adjacent stations. The procedure terminates when the solutions converge and the conservation of materials flow is satisfied. The decomposition method does not alter the station parameters such as the breakdown, repair, and service rates. This method can be used to analyse a wide variety of production systems built from heterogeneous stations. The properties of the decomposition method are studied for the series arrangement of workstations. The convergence and uniqueness of the decomposition method are discussed. The method is compared to other approximation methods. The complexity of the decomposition method is empirically investigated and is shown to be in the order of N-2 where N is the number of stations in the line, irrespective of the buffer capacities.Publication Metadata only 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; 6033In 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.Publication Metadata only A long-range dependent workload model for packet data traffic(Inst Operations Research Management Sciences, 2004) Department of Mathematics; Çağlar, Mine; Faculty Member; Department of Mathematics; College of Sciences; 105131We consider a probabilistic model for workload input into a telecommunication system. It captures the dynamics of packet generation in data traffic as well as accounting for long-range dependence and self-similarity exhibited by real traces. The workload is found by aggregating the number of packets, or their sizes, generated by the arriving sessions. The arrival time, duration, and packet-generation process of a session are all governed by a Poisson random measure. We consider Pareto-distributed session holding times where the packets are generated according to a compound Poisson process. For this particular model, we show that the workload process is long-range dependent and fractional Brownian motion is obtained as a heavy-traffic limit. This yields a fast synthesis algorithm for generating packet data traffic as well as approximating fractional Brownian motion.Publication Open Access A machine learning approach for implementing data-driven production control policies(Taylor _ Francis, 2021) Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AGiven the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.Publication Metadata only A Markov modulated Poisson model for software reliability(Elsevier, 2013) Landon, Joshua; Soyer, Refik; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631In this paper, we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures. The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.Publication Metadata only A matheuristic for the generalized order acceptance and scheduling problem(Elsevier, 2022) 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; 6033In make-to-order production systems, manufacturer can have limited capacity and due to the order de-livery time requirements, it may not be possible to accept all orders. This leads to the order acceptance and scheduling problem with release times and sequence dependent setup times that determines which orders to accept and how to schedule them simultaneously to maximize the revenue (GOAS). The aim of this study is to develop an effective and efficient solution methodology for the GOAS problem. To achieve this aim, we develop a mixed integer linear programming model, a constraint programming model, and a matheuristic algorithm that consists of a time-bucket based mixed integer linear programming model, a variable neighborhood search algorithm and a tabu search algorithm. Computational results show that the proposed matheuristic outperforms both the proposed exact models and previous state-of-the-art al-gorithms developed for the GOAS problem. The boundary of optimally solved instance size is pushed further and near optimal solutions are obtained in reasonable time for instances falling beyond this boundary.Publication Metadata only A mixed-integer programming approach to multi-class data classification problem(Elsevier Science Bv, 2006) Department of Industrial Engineering; Department of Industrial Engineering; Yüksektepe, Fadime Üney; Türkay, Metin; Researcher; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 108243; 24956This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer programming model is developed for representing existence of hyper-boxes and their boundaries. In addition, the relationships among the discrete decisions in the model are represented using propositional logic and then converted to their equivalent integer constraints using Boolean algebra. The proposed approach for multi-class data classification is illustrated on an example problem. The efficiency of the proposed method is tested on the well-known IRIS data set. The computational results on the illustrative example and the IRIS data set show that the proposed method is accurate and efficient on multi-class data classification problems.Publication Metadata only A newsvendor problem with markup pricing in the presence of within-period price fluctuations(Elsevier, 2022) Canyakmaz, Caner; Department of Industrial Engineering; Department of Industrial Engineering; Özekici, Süleyman; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 32631; 3579We consider a single-item single-period joint inventory management and pricing problem of a retailer selling an item that has selling price uncertainties. Unlike most of the literature on the newsvendor problem, we assume that price-dependent demand arrives randomly according to a stochastic arrival process whose rate depends on the fluctuating market input price process. The retailer's problem is to choose the order quantity and a proportional price markup over the input price to maximize the expected profit. This setting is mostly encountered by retailers that trade in different currencies or have to purchase and convert commodities for seasonal sales. For this setting, we characterize both the optimal inventory and markup levels. We present monotonicity properties of the expected profit function with respect to each decision variable. We also show that more volatile input price processes lead to lower expected profits.