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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Business AdministrationSubject: search.filters.subject.Applied mathematics

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Now showing 1 - 3 of 3
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    Optimizing digital twin synchronization in a finite horizon
    (IEEE, 2022) Matta, Andrea; Department of Business Administration; Tan, Barış; Department of Business Administration; College of Administrative Sciences and Economics
    Given the tendency to increase the complexity of digital twins to capture a manufacturing system in the most detailed way, synchronizing and using a complex digital twin with the real-time data may require significant resources. We define the optimal synchronization problem to operate the digital twins in the most effective way by balancing the trade-off between improving the accuracy of the simulation prediction and using more resources. We formulate and solve the optimal synchronization problem for a special case. We analyze the characteristics of the state-dependent and state-independent optimal policies that indicate when to synchronize the simulation at each decision epoch. Our numerical experiments show that the number of synchronizations decreases with the synchronization cost and with the system variability. Furthermore, a lower average number of synchronizations can be achieved by using a state-dependent policy.
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    Optimization applications in scheduling theory - introduction and an overview
    (Springer, 1996) Kouvelis, P.; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819
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    Computing the nadir point for multiobjective discrete optimization problems
    (Springer, 2015) N/A; N/A; Department of Business Administration; Kirlik, Gökhan; Sayın, Serpil; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 6755
    We investigate the problem of finding the nadir point for multiobjective discrete optimization problems (MODO). The nadir point is constructed from the worst objective values over the efficient set of a multiobjective optimization problem. We present a new algorithm to compute nadir values for MODO with objective functions. The proposed algorithm is based on an exhaustive search of the -dimensional space for each component of the nadir point. We compare our algorithm with two earlier studies from the literature. We give numerical results for all algorithms on multiobjective knapsack, assignment and integer linear programming problems. Our algorithm is able to obtain the nadir point for relatively large problem instances with up to five-objectives.