Browsing by Subject "dynamic programming"

Now showing 1 - 5 of 5

Open Access
Analyzing the effect of consumer returns in a multi-period inventory system
(2012) Erikçi, İsmail
Return of a sold item by a customer becomes tremendously common situation in many industries. Increase in the amount of returned items promotes return information to be a critical factor for inventory control. Undoubtedly another critical parameter for an inventory system is the length of the review period. Effect of the review period or length of the time-bucket is amplified with returned items, because available return information at a decision point is related to the frequency of the review. In this study, we analyze the effects of these two parameters over a multiperiod inventory system where the length of a time horizon is fixed. Dynamic programming approach is used to calculate the optimal inventory positions. In dynamic programming, it is assumed that a fixed proportion of sold items are returned. Computational results are obtained to compare the effects of return information under different return proportions and period lengths. These results are used to conduct various analyses to explore the level of the advantage gained by using return information.
Open Access
Dynamic capacity adjustment for virtual-path based networks using neuro-dynamic programming
(2003) Şahin, Cem
Dynamic capacity adjustment is the process of updating the capacity reservation of a virtual path via signalling in the network. There are two important issues to be considered: bandwidth (resource) utilization and signaling traffic. Changing the capacity too frequently will lead to efficient usage of resources but has a disadvantage of increasing signaling traffic among the network elements. On the other hand, if the capacity is adjusted for the highest possible value and kept fixed for a long time period, a significant amount of bandwidth will be wasted when the actual traffic rate is small. We proposed two formulations for dynamic capacity adjustment problem. In the first formulation cost parameters are assigned for bandwidth usage and signalling, optimal solutions are reached for different values of these parameters. In the second formulation, our aim is to maximize the bandwidth efficiency with a given signaling requirement. In this formulation, a leaky bucket counter is used in order to regulate the signaling rate. We used dynamic programming and neuro-dynamic programming techniques and we applied our formulations for voice traffic scenario (voice over packet networks) and a general network architecture using flow-based Internet traffic modelling. In the Internet traffic modelling case, we tested two different control strategies: event-driven control and time-driven control. In event-driven control, capacity update epochs are selected to be the time instants of either a flow arrival or a flow departure. In time-driven control, decision epochs are selected to be the equidistant time instants and excessive amount of traffic that cannot be carried will be buffered.
Open Access
Human face detection and eye location in video using wavelets
(2006) Türkan, Mehmet
Human face detection and eye localization problems have received significant attention during the past several years because of wide range of commercial and law enforcement applications. In this thesis, wavelet domain based human face detection and eye localization algorithms are developed. After determining all possible face candidate regions using color information in a given still image or video frame, each region is filtered by a high-pass filter of a wavelet transform. In this way, edge-highlighted caricature-like representations of candidate regions are obtained. Horizontal, vertical and filter-like edge projections of the candidate regions are used as feature signals for classification with dynamic programming (DP) and support vector machines (SVMs). It turns out that the proposed feature extraction method provides good detection rates with SVM based classifiers. Furthermore, the positions of eyes can be localized successfully using horizontal projections and profiles of horizontal- and vertical-crop edge image regions. After an approximate horizontal level detection, each eye is first localized horizontally using horizontal projections of associated edge regions. Horizontal edge profiles are then calculated on the estimated horizontal levels. After determining eye candidate points by pairing up the local maximum point locations in the horizontal profiles with the associated horizontal levels, the verification is also carried out by an SVM based classifier. The localization results show that the proposed algorithm is not affected by both illumination and scale changes.
Open Access
Independent task assignment for heterogeneous systems
(2013) Tabak, E Kartal
We study the problem of assigning nonuniform tasks onto heterogeneous systems. We investigate two distinct problems in this context. The first problem is the one-dimensional partitioning of nonuniform workload arrays with optimal load balancing. The second problem is the assignment of nonuniform independent tasks onto heterogeneous systems. For one-dimensional partitioning of nonuniform workload arrays, we investigate two cases: chain-on-chain partitioning (CCP), where the order of the processors is specified, and chain partitioning (CP), where processor permutation is allowed. We present polynomial time algorithms to solve the CCP problem optimally, while we prove that the CP problem is NP complete. Our empirical studies show that our proposed exact algorithms for the CCP problem produce substantially better results than the state-of-the-art heuristics while the solution times remain comparable. For the independent task assignment problem, we investigate improving the performance of the well-known and widely used constructive heuristics MinMin, MaxMin and Sufferage. All three heuristics are known to run in O(KN2 ) time in assigning N tasks to K processors. In this thesis, we present our work on an algorithmic improvement that asymptotically decreases the running time complexity of MinMin to O(KN log N) without affecting its solution quality. Furthermore, we combine the newly proposed MinMin algorithm with MaxMin as well as Sufferage, obtaining two hybrid algorithms. The motivation behind the former hybrid algorithm is to address the drawback of MaxMin in solving problem instances with highly skewed cost distributions while also improving the running time performance of MaxMin. The latter hybrid algorithm improves the running time performance of Sufferage without degrading its solution quality. The proposed algorithms are easy to implement and we illustrate them through detailed pseudocodes. The experimental results over a large number of real-life datasets show that the proposed fast MinMin algorithm and the proposed hybrid algorithms perform significantly better than their traditional counterparts as well as more recent state-of-the-art assignment heuristics. For the large datasets used in the experiments, MinMin, MaxMin, and Sufferage, as well as recent state-of-the-art heuristics, require days, weeks, or even months to produce a solution, whereas all of the proposed algorithms produce solutions within only two or three minutes. For the independent task assignment problem, we also investigate adopting the multi-level framework which was successfully utilized in several applications including graph and hypergraph partitioning. For the coarsening phase of the multi-level framework, we present an efficient matching algorithm which runs in O(KN) time in most cases. For the uncoarsening phase, we present two refinement algorithms: an efficient O(KN)-time move-based refinement and an efficient O(K2N log N)-time swap-based refinement. Our results indicate that multi-level approach improves the quality of task assignments, while also improving the running time performance, especially for large datasets. As a realistic distributed application of the independent task assignment problem, we introduce the site-to-crawler assignment problem, where a large number of geographically distributed web servers are crawled by a multi-site distributed crawling system and the objective is to minimize the duration of the crawl. We show that this problem can be modeled as an independent task assignment problem. As a solution to the problem, we evaluate a large number of state-of-the-art task assignment heuristics selected from the literature as well as the improved versions and the newly developed multi-level task assignment algorithm. We compare the performance of different approaches through simulations on very large, real-life web datasets. Our results indicate that multi-site web crawling efficiency can be considerably improved using the independent task assignment approach, when compared to relatively easy-to-implement, yet naive baselines.
Open Access
Stochastic lot sizing problems under monopoly
(2009) Yanıkoğlu, İhsan
In this thesis, we study stochastic lot sizing problems under monopoly. We consider production planning of a single item using uncapacitated resources over a multi-period time horizon. The demand uncertainty is modeled via a scenario tree structure. Each node of the tree corresponds to a scenario of demand realization with an associated probability. We first consider the stochastic lot sizing problem under monopoly (SLS), which addresses the period based production plan of a manufacturer with uncertain demands and a monopolistic supplier. We propose an exact dynamic programming algorithm to solve the SLS problem in polynomial time. The second problem we consider, the stochastic lot sizing problem with extra ordering (SLSE), is based on two-stage stochastic programming. In addition to the period based production decision variables of the SLS model, there exist scenario based extra ordering decision variables in the problem setting of SLSE. We develop two families of valid inequalities for the feasible region of the introduced SLSE model. The required separation algorithms of both valid inequalities are presented along with their implementations with branch-and-cut algorithm in solving SLSE. An extensive computational analysis with branch-and-cut algorithms shows the effectiveness of these inequalities.