A DSS-Based Dynamic Programming for Finding Optimal Markets Using Neural Networks and Pricing
One of the substantial challenges in marketing efforts is determining optimal markets, specifically in market segmentation. The problem is more controversial in electronic commerce and electronic marketing. Consumer behaviour is influenced by different factors and thus varies in different time periods. These dynamic impacts lead to the uncertain behaviour of consumers and therefore harden the target market determination. Real time decision making is a crucial task for obtaining competitive advantage. Decision Support Systems (DSSs) can be an appropriate process for taking real time decisions. DSSs are classified as information system based computational systems helping in decision making supporting business decision making and facilitate data collection and processing within market analysis. In this paper, different markets exist that are supplied by a producer. The producers need to find out which markets provide more profits for more marketing focuses. All consumers’ transactions are recorded in databases as unstructured data. Then, neural network is employed for large amount of data processing. Outputs are inserted to an economic producer behaviour mathematical model and integrated with a proposed dynamic program to find the optimal chain of markets. The sensitivity analysis is performed using pricing concept. The applicability of the model is illustrated in a numerical example.
Finding the Bayesian network that maximizes a score function is known as structure learning or structure discovery. Most approaches use local search in the space of acyclic digraphs, which is prone to local maxima. Exhaustive enumeration requires super-exponential time. In this paper we describe a "merely" exponential space/time algorithm for finding a Bayesian network that corresponds to a glo...متن کامل
Finding the Bayesian network that maximizes a score function is known as structure learning or structure discovery. Most approaches use local search in the space of acyclic digraphs, which is prone to local maxima. Exhaustive enumeration requires super-exponential time. In this paper we describe a “merely” exponential space/time algorithm for finding a Bayesian network that corresponds to a glo...متن کامل
We develop a system for managing inventory for Jeppesen Sanderson, Inc. — a major provider of aviation information products. The problem involves determining order quantities for charts used in flight manuals. These charts contain essential safety information that changes frequently, making standard methods for inventory management ineffective. We formulate the problem as a dynamic programming ...متن کامل
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
ESTIMATION OF INVERSE DYNAMIC BEHAVIOR OF MR DAMPERS USING ARTIFICIAL AND FUZZY-BASED NEURAL NETWORKS
In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...متن کامل
The parameter values used for the Growing Neural Gas (GNG) algorithm are generally determined empirically. This requires long calculation times and may lead to values which are not optimized for the data set they are being used with. The present work proposes the use of Evolutionary Algorithms to optimize these parameter values. During the optimization process, GNG networks are created with the...متن کامل
دوره 14 شماره 1
صفحات 87- 106
تاریخ انتشار 2021-01-01
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