A production/remanufacture model with returns’ subassemblies managed differently

This paper considers a production-remanufacturing inventory model for a single product, where constant demand is satisfied from the inventory of newly produced and remanufactured items. Although the available models in the literature imply that collected used units (or returns) are disassembled for recovery purposes, these models really do not treat them as such. Contrary, the returns are assumed to be recovered as whole units, perhaps, for simplicity. This assumption may not capture the benefits reaped from product recovery programs. This paper addresses this limitation in the literature and assumes that each unit of a used product is collected and disassembled into components, where these components are sorted into subassemblies, which are fed back into the production-remanufacturing process. The returned subassemblies are remanufactured and reassembled to represent a second source of as-good-as-new units of the end-product. For this multi-component inventory problem, the question that needs to be answered is whether, or not, extreme strategies of either pure remanufacturing or pure production are more economical than a mixed strategy (one that combines both strategies). A mathematical model is developed that accounts for the inventories of subassemblies. The results suggested that not accounting for the disassembled components of a product leads to inappropriate inventory decisions that are not environmentally sound.     

A review of the extensions of a modified EOQ model for imperfect quality items

Imperfect items in the raw material and production stages of a supply chain directly impact the coordination of the product flow within a supply chain. In response to this concern, production and inventory lot sizing models, which incorporate imperfect items into their formulation have become an important and growing area of research. The contribution of Salameh and Jaber (2000) is one of the fundamental models on lot sizing when procured items are of imperfect quality. Over the past decade, there has been a noticeable amount of interest in the EOQ model for imperfect items that was set forth in Salameh and Jaber (2000). Several researchers have published adaptations and extensions of this original model that address supply chain coordination, quality improvement and yield management, and the impact of human error on production and inventory systems. In this paper, we summarize the current body of research that has extended the Salameh and Jaber (2000) EOQ model for imperfect items. Some possible future research directions are identified at the end of the paper.     

A measure of regional influence with the analytic network process

The metropolitan region is commonly defined by a socio-spatial network of urban nodes that are linked in territory and function. Such a network is differentiated by size and dominance of the linked nodes, characterized by physical or virtual flows of a wide-ranging variety. The analytic network process (ANP) is a multi-criteria analytic method that measures the influence or dominance of the nodes in a network with feedback. We illustrate how ANP determines county rank as a measure of influence in a metropolitan statistical area (MSA) defined by its interrelated socio-economic and spatial elements qualitatively and quantitatively. We compare the ANP results to similar measures of regional influence in the literature.     

Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company’s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.     

Investment portfolio balancing: application of a generic self-organizing fuzzy neural network (GenSoFNN)

In contrast to short-term stock trading, portfolio managers are interested in the medium- to long-term peaks and troughs of the stock price cycles as signals to balance their stock portfolios – the predicted trough is the signal to buy the stock and the predicted peak is the signal to sell the stock. As statistical models are generally inadequate or incapable of providing such portfolio balancing signals, we propose using the generic self-organizing fuzzy neural network (GenSoFNN)—a fuzzy neural system – as a tool for portfolio balancing. The network adopts the supervised learning approach to detect inflection points in the stock price cycles, and a modified locally weighted regression algorithm is employed to smooth the stock cycles. The GenSoFNN-based portfolio balancing system was evaluated with experiments conducted using 23 stocks from the New York Stock Exchange and NASDAQ, and the results showed an average profit return of 65.66%. The contributions of the proposed GenSoFNN intelligent portfolio balancing system are twofold: it can be used as an efficient trading solution and it can provide decision support in trading via its generated rules. Copyright © 2009 John Wiley & Sons, Ltd.     

Modelling reverse mortgages

This paper suggests a framework for analyzing reverse mortgages in a fixed interest rate environment as well as a variable interest rate environment. To analyze the risk and potential profit of a reverse mortgage, we propose to calculate the expected present value of profit and the probability of loss as indicators of the viability of the loan. Using Singapore data, we provide some estimates of these quantities for various levels of annuity payment under different circumstances.The author is from the Department of Economics and Statistics, National University of Singapore. He wishes to thank Chan Wai Sum, Thomas Herzog, Lim Kian Guan (the previous chief editor), Tan Kin Lian and Albert Tsui for their invaluable comments on the earlier drafts of the paper. All errors and shortcomings, however, remain his responsibility.     

Stock Market Mean Reversion and the Optimal Equity Allocation of a Long-Lived Investor

This paper solves numerically the intertemporalconsumption and portfolio choiceproblem of an infinitely-lived investor whofaces a time-varying equity premium.The solutions we obtain are very similarto the approximate analytical solutionsof Campbell and Viceira (1999), except atthe upper extreme of the state spacewhere both the numerical consumption andportfolio rules flatten out.We also consider a constrained version ofthe problem in which the investor facesborrowing and short-sales restrictions.These constraints bind when the equitypremium moves away from its mean in eitherdirection, and are particularly severe forrisk-tolerant investors. The constraints havesubstantial effects on optimalconsumption, but much more modest effects onoptimal portfolio choice in theregion of the state space where they are notbinding.     

In Search of Distress Risk

This paper explores the determinants of corporate failure and the pricing of financially distressed stocks whose failure probability, estimated from a dynamic logit model using accounting and market variables, is high. Since 1981, financially distressed stocks have delivered anomalously low returns. They have lower returns but much higher standard deviations, market betas, and loadings on value and small‐cap risk factors than stocks with low failure risk. These patterns are more pronounced for stocks with possible informational or arbitrage‐related frictions. They are inconsistent with the conjecture that the value and size effects are compensation for the risk of financial distress.     

Effects of pricing and promotion on consumer perceptions: it depends on how you frame it

This research examined the extent to which different promotional frames increased perceptions of deal value. Price discounts dominate the sales promotions employed by marketers. The framing literature suggests discounts have robust positive effects on consumer perceptions of deal value. However, the current research showed that negative quality inferences moderated discount framing effects and undermined deal value, particularly when no assurance of product quality was provided. Every-day-low-price offers were also vulnerable to negative quality inferences, while free gift frames maintained quality perceptions and increased deal value. Product trial acted to further magnify promotional framing effects, according to a confirmation bias. These findings were consistent with an attribute framing mechanism.