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

Optimal pricing strategy for livestock of fishery and poultry

The mutation is one of the most important stages in the genetic algorithm (GA) because of its influence on exploring solution space and overcoming premature convergence. Since there are many types of mutation operators, the problem lies in selecting the appropriate type, and so, researchers usually need more trial and error. This paper investigates a new mutation operator based on teaching–learning-based optimization (TLBO) to enhance the performance of genetic algorithms. This new mutation operator treats intelligently instead of the random type, enhances the quality of solution, and speeds up the convergence of GA simultaneously. Several experiments are conducted on six standard test functions to evaluate the effect of the proposed mutation operator. First, proper comparisons are made between the performance of the proposed mutation to the classic mutation of GA and their combinatorial format. The result indicates the effect of the proposed mutation operator on the significant enhancement of the genetic algorithms’ performance particularly. Due to computational analysis with Intel(R) Core(TM) i5-2430 M CPU @ 2.40 GHz processor, this method causes 32–53.3% reduction in essential iteration to present zero amount as the final value for four test functions (i.e., Beale, Himmelblau, Booth, and Rastrigin). For the two other functions that provides a non-zero value (i.e., Ackley and Sphere), the proposed method improves nearly 100% in average of objective. According to the result, the final solutions of the proposed method are equal or better than the classic GA in all six problems. Then, the performance of the proposed algorithm in comparison to five well-known algorithms ensures its superiority. In all comparisons, the proposed method performs equal or better than five other algorithms in CPU time and quality solutions.

Teaching–learning-based genetic algorithm (TLBGA): an improved solution method for continuous optimization problems

An Economic Production Lot Scheduling model is generalized analytically in which time-dependent demand in the market is adjusted by finite rate of production. The rate of production depends upon modern technology, capital investment and number of labors. Also, the generalized EPLS model is simplified especially for constant demand, linear trend in demand, quadratic demand and exponentially demand pattern. The relevant cost function of this model is minimized analytically.

/pdf/on-a-volume-flexible-inventory-model-3wifzjk4wa.pdf

On a volume flexible inventory model

The paper proposes a two-stage supply chain model for price sensitive demand in imperfect production system while manufacturer and supplier are the members of the chain. The supplier screens the raw materials first and supplies good materials to the manufacturer at a constant rate. The production rate varies randomly within a finite interval. The inventory cycle of the manufacturer starts with shortages and production and it finishes with shortages again, in which shortages are partially backlogged. We consider a mixture of LIFO (last in, first out) and FIFO (first in, first out) dispatching policies to fill the backlogged demand. Thus, the objective of the proposed paper is to determine the optimal ordering lot-size and selling price of the manufacturer such that the per unit average integrated expected profit of the supply chain model is maximized. A numerical example is provided to analyze and illustrate the behavior and application of the model. Finally, sensitivity analysis of the key parameters are presented to test feasibility of the model.

/pdf/optimal-pricing-and-ordering-policy-for-two-echelon-varying-egx901wr51.pdf

Optimal Pricing and Ordering Policy for Two Echelon Varying Production Inventory System

This article deals with a two-stage supply chain comprising of the retailer and the supplier in which demand of the end customers follows news-vendor type demand. The setup costs, purchasing and procurement costs are dependent on lot sizes. The mathematical models for continuous and discrete variables of the chain are analyzed mathematically to maximize the expected average profits of individual and the collaborating systems. As our study suggests that collaborating system is always better than the decentralized system for known distribution cases, we also optimize the joint profit of the retailer and the supplier for a distribution free case which is implemented for unknown distribution. Finally, numerical examples of the demand patterns are illustrated to justify the proposed model. The punctual research directions from the proposed model are also provided in the conclusion section.

/pdf/integrated-supply-chain-of-supplier-and-retailer-for-36gqdn65cd.pdf

Shib Sankar Sana

Papers

Optimal pricing strategy for livestock of fishery and poultry

Teaching–learning-based genetic algorithm (TLBGA): an improved solution method for continuous optimization problems

On a volume flexible inventory model

Optimal Pricing and Ordering Policy for Two Echelon Varying Production Inventory System

Integrated supply chain of supplier and retailer for stochastic demand