Sharif Digital Repository

The goal of this paper is to develop a model to integrate "pricing" and "advertisement" for short life cycle products, such as branded fashion clothing products. To achieve this goal, we apply the concept of "Dynamic Pricing". There are two classes of advertisements, for the brand (regardless of product) and for a particular product. Advertising the brand affects the demand and price of all the products. Thus, the model considers all these products in relation with each other. We develop two different methods to integrate both types of advertisement and pricing. The first model is developed within the framework of dynamic programming. However, due to the complexity of the model, this method... 

Coordinated operation of hydro-thermal power systems helps reduce generation costs. The Hydro-Thermal Coordinating Model (HTCOM) software package, as one of these models, determines long-term operational policies for a hydro-thermal power system with multipurpose reservoirs. The CLT was incorporated within this framework to ensure a logical time-based run-time, with no serious decrease in precision. HTCOM, based on both its theoretical background and its application in the KWPA system, has shown that it can handle real world problems successfully, with more reliable outcomes. Many models have also been developed to optimize the planning of hydro-thermal systems. HTCOM was developed and... 

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this... 

This paper presents a simulation-based optimization approach for railway timetabling, which is made interesting by the need for trains to stop periodically to allow passengers to pray. The developed framework is based on integration of a simulation model and an evolutionary path re-linking algorithm with the capability of scheduling trains, subject to the capacity constraints in order to minimize the total waiting times. A customized deadlock avoidance method has been developed which is based on a conditional capacity allocation. The proposed look-ahead deadlock avoidance approach is effective and easy to implement in the simulation model. A case study of the Iranian Railway (RAI) is... 

Response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. The main idea of RSM is to use a set of designed experiments to obtain an optimal response. RSM tries to simplify the original problem through some polynomial estimation over small sections of the feasible area, elaborating on optimum provision through a well known optimization technique, say Gradient Method. As the real world problems are usually very complicated, polynomial estimation may not perform well in providing a good representation of the objective function. Also, the main problem of the Gradient Method, getting trapped in local minimum (maximum),... 

Identification of the change point in multi-attribute processes make the corrective measures to be employed sooner. This of course results in considerable amount of time and money savings. In real-world problems, since most of the process changes are due to instantaneous causes (step-change), a new method is proposed in this paper to derive the maximum likelihood estimator of the time of a step-change in the mean vector of multi-attribute processes. The results of a performance study based on a numerical example are encouraging  

The hierarchical structure of real-world problems has resulted in a focus on hierarchical frameworks in the reinforcement learning paradigm. Preparing mechanisms for automatic discovery of macro-actions has mainly concentrated on subgoal discovery methods. Among the proposed algorithms, those based on graph partitioning have achieved precise results. However, few methods have been shown to be successful both in performance and also efficiency in terms of time complexity of the algorithm. In this paper, we present a SCC-based subgoal discovery algorithm; a graph theoretic approach for automatic detection of subgoals in linear time. Meanwhile a parameter tuning method is proposed to find the... 

Employing deep learning makes it possible to learn high-level features from raw data, resulting in more precise models. On the other hand, submodularity makes the solution scalable and provides the means to guarantee a lower bound for its performance. In this paper, a deep submodular network (DSN) is introduced, which is a deep network meeting submodularity characteristics. DSN lets modular and submodular features to participate in constructing a tailored model that fits the best with a problem. Various properties of DSN are examined and its learning method is presented. By proving that cost function used for learning process is a convex function, it is concluded that minimization can be... 

Various control charts have been proposed to monitor generalized linear pro les in Phase II. However, the robustness of the proposed methods in detecting di erent types and especially di erent directions of changes is not well-studied in the literature. In real-world applications, di erent kinds of change such as drift and multiple changes are likely to occur, which can be isotonic (increasing) or antitonic (decreasing). This paper studies the robustness of the Rao Score Test (RST) method, T2, and Multivariate Exponential Weighted Moving Average (MEWMA) in di erent types, drift and multiple, and directions of changes. The RST method also bene ts from a change-point detection approach whose... 

Data streams have some unique properties which make them applicable in precise modeling of many real data mining applications. The most challenging property of data streams is the occurrence of "concept drift". Recurring concepts is a type of concept drift which can be seen in most of real world problems. Detecting recurring concepts makes it possible to exploit previous knowledge obtained in the learning process. This leads to quick adaptation of the learner whenever a concept reappears. In this paper, we propose a learning algorithm called Pool and Accuracy based Stream Classification with some variations, which takes the advantage of maintaining a pool of classifiers to track recurring... 

The economic production quantity (EPQ) model is often used in manufacturing environments to assist firms in determining the optimal production lot size that minimizes the overall production-inventory costs. While there are some unrealistic assumptions in the EPQ model that limit its real-world applications, in this research, some of these assumptions such as (1) infinite availability of warehouse space, (2) all of the produced items being perfect, and (3) the existence of one product type are relaxed. In other words, we develop a multi-product EPQ model in which there are some imperfect items of different product types being produced such that reworks are allowed and that there is a... 

Single floor facility layout problem (FLP) is related to finding the arrangement of a given number of departments within a facility; while in multi-floor FLP, the departments should be imbedded in some floors inside the facility. The significant influence of layout design on the effectiveness of any organization has turned FLP into an important issue. This paper presents a three- (two-) stage mathematical programming method to find competitive solutions for multi- (single-) floor problems. At the first stage, the departments are assigned to the floors through a mixed integer programming model (the single floor version does not require this stage). At the second stage, a nonlinear programming... 

Today, many students are study computer science. Although they study different courses, they rarely do homework or projects related to real world problems. In this paper, we design a project to show some of the problems which are occured during implementing a real word project to the students. In this project, the students are asked to implement the FFT (Fast Fourier Transform) algorithm on a personal computer and then port the program to a smartphone using J2ME (Java 2 Micro Edition) programming language. By doing this project, the undergraduate computer science students will become familiar with the limitations of small devices such as smartphones. They also learn the process of porting a...