Research Outputs
Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
Browse
18 results
Search Results
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 novel approach to tube design via von Mises probability distribution(Taylor & Francis Ltd) Subay, Şehmuz Ali; N/A; N/A; N/A; Department of Mechanical Engineering; Oral, Atacan; Subaşı, Ömer; Öztürk, Çağlar; Lazoğlu, İsmail; PhD Student; Researcher; PhD student; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; N/A; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 179391Discharge tube is a critical component in a reciprocating compressor that carries the refrigerant. It also transmits vibrations from compressor body to housing, making the design of tube a complex engineering problem combining static, modal and flow behaviour. This study proposes a novel design algorithm for discharge tube, to decrease the dependency on the trial-and-error approach commonly used by manufacturers. The computational approach creates a tube that connects the inlet and outlet using von Mises probability distribution. The created geometries are checked for static and dynamic properties using FEA. The algorithm continues until a candidate design passes the imposed thresholds. The candidate designs perform similarly to benchmark in evaluated aspects, demonstrating promising results. The presented algorithm is successful in generating alternative tube designs from scratch and can accommodate varying requirements. The main novelty of this study is the development of a comprehensive decision algorithm that considers multiple engineering parameters simultaneously.Publication Metadata only An exact algorithm for integrated planning of operations in dry bulk terminals(Pergamon-Elsevier Science Ltd, 2019) 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; 328856; 6033We consider integrated planning problem of export dry bulk terminals. This problem consists of three important operations: (i) berth allocation, (ii) reclaimer scheduling, and (iii) stockyard allocation, and includes tidal time windows, multiple stocking pads and non-crossing of reclaimers. We exploit relationships among these operations to decompose this complex problem and propose a logic-based Benders decomposition algorithm. Master and subproblems are modeled with mixed-integer programming and constraint programming, respectively, such that complementary strengths of these programming paradigms are utilized. Computational experiments show that the proposed method can effectively solve the integrated problem for up to two weeks of planning horizon.Publication Metadata only Emergency facility location under random network damage: insights from the Istanbul case(Pergamon-Elsevier Science Ltd, 2015) Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Yücel, Eda; Faculty Member; PhD Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 178838; 235501Damage to infrastructure, especially to highways and roads, adversely affects accessibility to disaster areas. Predicting accessibility to demand points from the supply points by a systematic model would lead to more effective emergency facility location decisions. To this effect, we model the spatial impact of the disaster on network links by random failures with dependency such that failure of a link induces failure of nearby links that are structurally more vulnerable. For each demand point, a set of alternative paths is generated from each potential supply point so that the shortest surviving path will be used for relief transportation after the disaster. The objective is to maximize the expected demand coverage within a specified distance over all possible network realizations. To overcome the computational difficulty caused by extremely large number of possible outcomes, we propose a tabu search heuristic that evaluates candidate solutions over a sample of network scenarios. The scenario generation algorithm that represents the proposed distance and vulnerability based failure model is the main contribution of our study. The tabu search algorithm is applied to Istanbul earthquake preparedness case with a detailed analysis comparing solutions found in no link failure, independent link failure, and dependent link failure cases. The results show that incorporating dependent link failures to the model improves the covered demand percentages significantly.Publication Metadata only Flexibility structure and capacity design with human resource considerations(Wiley, 2015) Department of Business Administration; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Karaesmen, Zeynep Akşin; Çakan, Nesrin; Karaesmen, Fikri; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 4534; N/A; 3579; 32863Most service systems consist of multidepartmental structures with multiskill agents that can deal with several types of service requests. The design of flexibility in terms of agents' skill sets and assignments of requests is a critical issue for such systems. The objective of this study was to identify preferred flexibility structures when demand is random and capacity is finite. We compare structures recommended by the flexibility literature to structures we observe in practice within call centers. To enable a comparison of flexibility structures under optimal capacity, the capacity optimization problem for this setting is formulated as a two-stage stochastic optimization problem. A simulation-based optimization procedure for this problem using sample-path gradient estimation is proposed and tested, and used in the subsequent comparison of the flexibility structures being studied. The analysis illustrates under what conditions on demand, cost, and human resource considerations, the structures found in practice are preferred.Publication Metadata only Hedging demand and supply risks in the newsvendor model(Springer, 2015) N/A; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Okyay, Hayrettin Kaan; Karaesmen, Fikri; Özekici, Süleyman; Master Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 3579; 32631We consider a single-period inventory model where there are risks associated with the uncertainty in demand as well as supply. Furthermore, the randomness in demand and supply is correlated with the financial markets. Recent literature provides ample evidence on this issue. The inventory manager may then exploit this correlation and manage his risks by investing in a portfolio of financial instruments. The decision problem, therefore, includes not only the determination of the optimal ordering policy, but also the selection of the optimal portfolio at the same time. We analyze this problem in detail and provide a risk-sensitive approach to inventory management where one considers both the mean and the variance of the resulting cash flow. The analysis results in some interesting and explicit characterizations on the structure of the optimal policy.Publication Metadata only Hub network design problem with capacity, congestion, and stochastic demand considerations(Informs, 2023) Bayram, Vedat; Farham, M. Saleh; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of EngineeringOur study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach's performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system.Publication Metadata only Mean time to failure and availability of semi-Markov missions with maximal repair(Elsevier Science Bv, 2010) N/A; Department of Industrial Engineering; Çekyay, Bora; Özekici, Süleyman; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; 110204; 32631We analyze mean time to failure and availability of semi-Markov missions that consist of phases with random sequence and durations. It is assumed that the system is a complex one with nonidentical components whose failure properties depend on the mission process. The stochastic structure of the mission is described by a Markov renewal process. We characterize mean time to failure and system availability under the maximal repair policy where the whole system is replaced by a brand new after successfully completing a phase before the next phase starts. Special cases involving Markovian missions are also considered to obtain explicit formulas. (C) 2010 Elsevier B.V. All rights reserved.Publication Metadata only Mean-variance newsvendor model with random supply and financial hedging(Taylor and Francis Inc, 2015) N/A; Department of Industrial Engineering; Tekin, Müge; Özekici, Süleyman; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 32631In this paper, we follow a mean-variance (MV) approach to the newsvendor model. Unlike the risk-neutral newsvendor that is mostly adopted in the literature, the MV newsvendor considers the risks in demand as well as supply. We further consider the case where the randomness in demand and supply is correlated with the financial markets. The MV newsvendor hedges demand and supply risks by investing in a portfolio composed of various financial instruments. The problem therefore includes both the determination of the optimal ordering policy and the selection of the optimal portfolio. Our aim is to maximize the hedged MV objective function. We provide explicit characterizations on the structure of the optimal policy. We also present numerical examples to illustrate the effects of risk-aversion on the optimal order quantity and the effects of financial hedging on risk reduction.Publication Metadata only Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity(Elsevier Science Bv, 2017) Department of Industrial Engineering; Department of Industrial Engineering; Akbari, Vahid; Salman, Fatma Sibel; Teaching Faculty; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; N/A; 178838After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions. (C) 2016 Elsevier B.V. All rights reserved.