Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

/pdf/the-theory-of-industrial-organization-19bejph0ej.pdf

The Theory of Industrial Organization

https://findresearcher.sdu.dk:8443/ws/files/116118561/reverse_logistics.pdf

Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future

The work is giving estimations of the discrepancy between solutions of the initial and the homogenized problems for a one{dimensional second order elliptic operators with random coeecients satisfying strong or uniform mixing conditions. We obtain several sharp estimates in terms of the corresponding mixing coeecient. Abstract. In the theory of homogenisation it is of particular interest to determine the classes of problems which are stable on taking the homogenisation limits. A notable situation where the limit enlarges the class of original problems is known as memory (nonlocal) eeects. A number of results in that direction has been obtained for linear problems. Tartar (1990) innitiated the study of the eeective equation corresponding to nonlinear equation: @ t u n + a n u 2 n = f: Signiicant progress has been hampered by the complexity of required computations needed in order to obtain the terms in power{series expansion. We propose a method which overcomes that diiculty by introducing graphs representing the domain of integration of the integrals in each term. The graphs are relatively simple, it is easy to calculate with them and they give us a clear image of the form of each term. The method allows us to discuss the form of the eeective equation and the convergence of power{series expansions. The feasibility of our method for other types of nonlinearities will be discussed as well.

Applied Mathematics and Computation

This paper aims to deal with optimal pricing and production tactics for a bi-echelon green supply chain, including a producer and a vendor in presence of three various scenarios. Demand depends on a price, refund and quality where the producer controls quality and the vendor proposes a refund policy to purchasers to encourage them to order more.,In the first scenario, the members seek to optimize their optimum decision variables under a centralized decision-making method while in the second scenario, a decentralized system is assumed where the members make a decision about variables and profits under a non-cooperative game. In the third scenario, a cost-sharing agreement is concluded between the members to provide a high-quality item to the purchasers.,The performance of the proposed model is investigated by illustrating a numerical example. A sensitivity analysis of some key parameters has been done to study the effect of the changes on the optimal values of the decision variables and profits. From sensitivity analysis, the real features are observed and mentioned in this section.,This research examines the behavior of partners in a green supply chain facing with a group of purchasers whose demand is the function of a price, greenery degree and refund rate. This proposed mathematical model is developed and analyzed which has an implication in supply chain model.

Manufacturing and selling tactics for a green supply chain under a green cost sharing and a refund agreement

This paper investigates a hill type economic production-inventory quantity (EPIQ) model with variable lead-time, order size and reorder point for uncertain demand. The average expected cost function is formulated by trading off costs of lead-time, inventory, lost sale and partial backordering. Due to the nature of the demand function, the frequent peak (maximum) and valley (minimum) of the expected cost function occur within a specific range of lead time. The aim of this paper is to search the lowest valley of all the valley points (minimum objective values) under fuzzy stochastic demand rate. We consider Intuitionistic fuzzy sets for the parameters and used Intuitionistic Fuzzy Aggregation Bonferroni mean for the defuzzification of the hill type EPIQ model. Finally, numerical examples and graphical illustrations are made to justify the model.

The ( p , q , r , l ) model for stochastic demand under Intuitionistic fuzzy aggregation with Bonferroni mean

The inventory literature is replete with claims on business strategy to maximize operational efficiency and customers' goodwill regarding the quality of products. A service system is composed of a number of entities, such as inventory of resources, trade credit financing, and the prediction of the demand and quality of products, among others. Drawing upon relevant research in inventory theory, in light of trade credit financing for uncertain demand of the customers, this study empirically develops a framework of an economic order quantity model for conforming and nonconforming quality products, while the nonconforming items, a random proportionate of the total products, are sold at a reduced price after a 100% screening process. In general, a trade credit policy is offered to the retailer by the supplier to buy more items. In this point of view, different delay periods with different purchasing costs are settled for the retailer. The associated expected average profit for both conforming and nonconforming products by trading off selling prices, inventory costs, interest earned from selling items, interest charged for delayed payments of purchasing cost, and shortage costs is maximized analytically. Numerical examples are illustrated to justify the model. Sensitivity analysis of the key parameters is also carried out for various issues.

An Economic Order Quantity Model for Nonconforming Quality Products

The main concern of construction projects is to select the right contractor in uncertain environment. According to the complex nature of today’s projects, classical approaches are not successful since they have considered only the proposed price and completion time of the project. The selection of a successful contractor depends on multi-criteria such as managerial and financial ability, technical capability, and organizational performance criteria in the presence of a decision-making group. Due to the uncertainties, ambiguities, and lack of information in this multi-criteria decision-making problem, this paper proposes an outranking method based on intuitionistic fuzzy theory. This approach involves reciprocal preference relation (RPR) and credibility function. The RPR is used to complete information, and credibility function is employed to select the best contractor. Choosing appropriate contractor to work in power plant projects is essentially owing to the fact that high-sensitive power plant construction projects are handed over to the contractor through tenders. Finally, a case study for selecting contractor in a power plant project is presented to demonstrate the effectiveness of the proposed method.

Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method

This study considers a two-echelon competitive supply chain consisting of two rivaling retailers and one common supplier with trade credit policy. The retailers hope that they can enhance their market demand by offering a credit period to the customers and the supplier also offers a credit period to the retailers. We assume that the market demand of the products of one retailer depends not only on their own market price and offering a credit period to the customers, but also on the market price and offering a credit period of the other retailer. The supplier supplies the product with a common wholesale price and offers the same credit period to the retailers. We study the model under a centralised integrated case and a decentralised Vertical Nash case and compare them numerically. Finally, we investigate the model by the collected numerical data.

Shib Sankar Sana

Papers

Manufacturing and selling tactics for a green supply chain under a green cost sharing and a refund agreement

The ( p , q , r , l ) model for stochastic demand under Intuitionistic fuzzy aggregation with Bonferroni mean

An Economic Order Quantity Model for Nonconforming Quality Products

Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method

Two-echelon competitive integrated supply chain model with price and credit period dependent demand