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Shib Sankar Sana

Bio: Shib Sankar Sana is an academic researcher from Bhangar Mahavidyalaya. The author has contributed to research in topics: Supply chain & Economic order quantity. The author has an hindex of 44, co-authored 176 publications receiving 5462 citations. Previous affiliations of Shib Sankar Sana include University of Calcutta & Swami Vivekanand Subharti University.


Papers
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Journal Article•DOI•
01 Jan 2011
TL;DR: An integrated production-inventory model is presented for supplier, manufacturer and retailer supply chain, considering perfect and imperfect quality items, and an analytical method is employed to optimize the production rate and raw material order size for maximum expected average profit.
Abstract: In this paper an integrated production-inventory model is presented for supplier, manufacturer and retailer supply chain, considering perfect and imperfect quality items. This model considers the impact of business strategies such as optimal order size of raw materials, production rate and unit production cost, and idle times in different sectors on collaborating marketing system. The model can be used in industries like textile and footwear, chemical, food, etc. An analytical method is employed to optimize the production rate and raw material order size for maximum expected average profit. An example is illustrated to study the behavior and application of the model.

247 citations

Journal Article•DOI•
TL;DR: In the proposed model, all increasing deterministic demands are discussed analytically, numerically and graphically in the environment of permissible delay in payment and discount offer to the retailer.

207 citations

Journal Article•DOI•
TL;DR: An EPL (Economic Production Lotsize) model in an imperfect production system in which the production facility may shift from an 'in- control' state to an 'out-of-control' state at any random time is investigated.

200 citations

Journal Article•DOI•
TL;DR: A model to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process is developed and the Euler-Lagrange method is used to obtain optimal solutions for product reliability parameter and dynamic production rate.

182 citations

Journal Article•DOI•
TL;DR: The author develops the criterion for the optimal solution for the replenishment schedule, and proves the optimal ordering policy is unique, and suggests to new functions regarding price-dependent demand and time varying deterioration rate.

145 citations


Cited by
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Journal Article•DOI•
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: 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.).

13,246 citations

01 Jan 2002

9,314 citations

Journal Article•DOI•
TL;DR: The aim of this paper is to review recently published papers in reverse logistic and closed-loop supply chain in scientific journals and identify gaps in the literature to clarify and to suggest future research opportunities.

1,364 citations

01 Jan 2010
TL;DR: 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 by introducing graphs representing the domain of integration of the integrals in each term.
Abstract: 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.

550 citations