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Yayın Optimal investment levels to eliminate inventory inaccuracy in a two-level supply chain(Istanbul Technical Univ, 2007) Uçkun, Canan; Karaesmen, Ahmet Fikri; Savaş, SelçukInventory inaccuracy is a major problem in supply chains. RFID technology is anticipated to alleviate this problem at the expense of the required hardware and software investment. For a supply chain consisting of single supplier and multiple warehouses, we investigate the optimal levels of investment in order to decrease inventory inaccuracy. The analysis yields in-sights on the relative benefits of RFID implementation depending on factors such as demand and inaccuracy variability, financial parameters and supply chain structure.Yayın An effective maintenance policy for a multi-component dynamic system using factored POMDPs(Springer Verlag, 2019-09-20) Kıvanç, İpek; Özgür Ünlüakın, DemetWith the latest advances in technology, almost all systems are getting substantially more uncertain and complex. Since increased complexity costs more, it is challenging to cope with this situation. Maintenance optimization plays a critical role in ensuring effective decision-making on the correct maintenance actions in multi-component systems. A Partially Observable Markov Decision Process (POMDP) is an appropriate framework for such problems. Nevertheless, POMDPs are rarely used for tackling maintenance problems. This study aims to formulate and solve a factored POMDP model to tackle the problems that arise with maintenance planning of multi-component systems. An empirical model consisting of four partially observable components deteriorating in time is constructed. We resort to Symbolic Perseus solver, which includes an adapted variant of the point-based value iteration algorithm, to solve the empirical model. The obtained maintenance policy is simulated on the empirical model in a finite horizon for many replications and the results are compared to the other predefined maintenance policies. Drawing upon the policy results of the factored representation, we present how factored POMDPs offer an effective maintenance policy for the multi-component systems.Yayın Maintenance policy simulation for a factored partially observable system(The Society for Modeling and Simulation International, 2019-07) Özgür Ünlüakın, Demet; Kıvanç, İpekTaking maintenance decisions is one of the well-known stochastic sequential decision problems under uncertainty. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for such problems. Nevertheless, POMDPs are rarely used for tackling maintenance problems of multi-component systems because their state spaces grow exponentially with the increasing number of components. Factored representations have been proposed for POMDPs taking advantage of the factored structure already available in the nature of the problem. Our aim in this study is to show how to formulate a factored POMDP model for the maintenance problem of a multi-component dynamic system and how to simulate and evaluate the obtained policy before implementing it in real life. The sensitivity of the methodology is analyzed under several cost values, and the methodology is compared to other predefined policies. The results show that the policies generated via the POMDP solver perform better than the predefined policies.Yayın Performance analysis of an aggregation and disaggregation solution procedure to obtain a maintenance plan for a partially observable multi-component system(Elsevier Sci Ltd, 2017-11) Özgür Ünlüakın, Demet; Bilgiç, TanerWe analyze the performance of an aggregation and disaggregation procedure in giving the optimal maintenance decisions for a multi-component system under partial observations in a finite horizon. The components deteriorate in time and their states are hidden to the decision maker. Nevertheless, it is possible to observe signals about the system status and to replace components in each period. The aim is to find a cost effective replacement plan for the components in a given time horizon. The problem is formulated as a partially observable Markov decision process (POMDP). We aggregate states and actions in order to reduce the problem space and obtain an optimal aggregate policy which we disaggregate by simulating it using dynamic Bayesian networks (DBN). The procedure is statistically compared to an approximate POMDP solver that uses the full state space information. Cases where aggregation performs relatively better are isolated and it is shown that k-out-of-n systems belong to this class.Yayın Statistical analysis of bus transportation networks of Istanbul(World Scientific Publishing Co Pte Ltd, 2016) Çoban, Veysel; Atan, Sabri TankutTransportation networks such as railway, airport and bus networks are the real-world networks whose inherent statistical properties characterize and differentiate the networks. In order to understand the network characteristics of bus transportation networks (BTNs) of Istanbul, we analyzed its network properties such as degree distributions, clustering coefficients and assortativity. BTNs of Istanbul is defined into three networks as the existence and nonexistence of the metrobus and existence of third- bridge. They are also graphically represented within C-, L- and P-Space topologies that are defined with the connection of the bus stops or routes. Statistical results obtained from network properties reflected the characteristics of the BTNs of Istanbul and give an information about the effects of the metrobus lines and third bridge on the BTNs in Istanbul.Yayın Re-mining positive and negative association mining results(Springer-Verlag Berlin, 2010) Demiriz, Ayhan; Ertek, Gürdal; Atan, Sabri Tankut; Kula, UfukPositive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, separately or simultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pYayın Interdependent network restoration: On the value of information-sharing(Elsevier Science BV, 2015-07-01) Sharkey, Thomas C.; Çavdaroğlu, Burak; Nguyen, Huy; Holman, Jonathan; Mitchell, John E. M.; Wallace, William AlWe consider restoring multiple interdependent infrastructure networks after a disaster damages components in them and disrupts the services provided by them. Our particular focus is on interdependent infrastructure restoration (IIR) where both the operations and the restoration of the infrastructures are linked across systems. We provide new mathematical formulations of restoration interdependencies in order to incorporate them into an interdependent integrated network design and scheduling (IINDS) problem. The IIR efforts resulting from solving this IINDS problem model a centralized decision-making environment where a single decision-maker controls the resources of all infrastructures. In reality, individual infrastructures often determine their restoration efforts in an independent, decentralized manner with little communication among them. We provide algorithms to model various levels of decentralization in IIR efforts. These algorithms are applied to realistic damage scenarios for interdependent infrastructure systems in order to determine the loss in restoration effectiveness resulting from decentralized decision-making. Our computational tests demonstrate that this loss can be greatly mitigated by having infrastructures share information about their planned restoration efforts.
