In today’s era of higher competition in the business, there are many conditions such as offered concession in bulk purchasing, seasonality, higher ordering cost, etc., which force a retailer to purchase more quantities than needed or exceed the storage capacity. So in this situation the retailer has to purchase an extra warehouse named as rented warehouse to stock the extra quantity. In this paper an inventory model for deteriorating products with selling price dependent rate is developed. The occurring shortages are assumed to be partially backlogged and cycle time is also variable. The purpose of the development of this model is to compute the amount and time of order which can optimize the total average cost of the system. A solution procedure and numerical example are presented to illustrate the implementation of the proposed study. Sensitivity analysis concerning with distinct system parameters is also presented to demonstrate the model.
DFT calculations indicate that the decomposition reaction of nitroethyl benzoates in the presence of 1,3-dimethylimidazolium cation takes place much faster than in the case of the non-catalyzed process. Additionally, our calculations suggest one-step polar mechanism of title reactions.
In this investigation, neodymium orthoferrite (NdFeO3) nanoparticles has been synthesized through ultrasonic method in the presence of octanoic acid as surfactant. This method comparing to the other methods is very fast and it does not need high temperatures during the reaction. The spherical NdFeO3 nanoparticles with an average particles size of about 40 nm can be obtained at a relatively high calcination temperature of 800 °C for 4 h. Also, product obtained by this method are uniform in both morphology and particles size. The phase composition, morphology, lattice parameters and size of particles in these product are characterized by Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD) scanning electron microscopy (SEM) and energy dispersive X-ray spectrometer (EDX). The XRD analysis reveals only the pattern corresponding to perovskite type NdFeO3 which crystallizes in the orthorhombic structure. Energy dispersive X-ray analysis confirms the elemental compositions of the synthesized material.
Knowledge management strategies are considered as the foundation of learning organizations. One of the problems of Iranian organizations is the assessment of knowledge management processes. The purpose of the present study is to present an applied organized model for the assessment of knowledge management performance in six dimensions, i.e., the financial dimension, stakeholders, local processes, growth and learning, employee satisfaction, and environment and community; identifying and investigating the correlation among the criteria; mapping network relations; weighing the indices using DEMATEL Technique; ranking assessment dimensions of knowledge management using ORESTE Technique; drawing strategic map; and designing Balanced Scorecard for improved performance of knowledge management. The population and sample of the study included 25 petrochemical Tehran managers and senior experts in information technology section. The results of this study provides a comprehensive view for the decision makers of Iran Petrochemical Industries for an improved performance in knowledge management.
Benzyl -L-rhamnopyranoside, prepared by both conventional and microwave assisted glycosidation techniques, was converted into benzyl 2,3-O-isopropylidene-α-L-rhamnopyranoside which after lauroylation followed by removal of isopropylidene group gave the benzyl 4-O-lauroyl-α-L-rhamnopyranoside in good yield. Several derivatives of benzyl 4-O-lauroyl-α-L-rhamnopyranoside were prepared and assessed in vitro for their antimicrobial activity against ten human pathogenic bacteria and seven fungi. The structure activity relationship (SAR) study revealed that incorporation of 4-O-lauroyl group in rhamnopyranoside frame work along with 2,3-di-O-acyl group increased the antifungal potentiality of the rhamnopyranosides.
Maintenance Qualitn Function Deployment (MQFD) is a methodology for improving the quality and effectiveness of maintenance services in a manufacturing organization. One major part of it is House of Quality (HoQ). HoQ translates the experts’ voice into technical requirements for the improvement of maintenance quality. These data are generally vague in nature. Fuzzy numbers are generally used to represent vague data in HoQ. Since some parameters are predefined in fuzzy approach, the experts’ opinion may not be truly reflected in the HoQ analysis. In this work, a rough set - fuzzy approach, is proposed for MQFD to overcome this drawback.The objective of this model is to prioritize the technical requirements effectively with the proper reflection of customers/experts’ perceptions in the output. An illustrative example is presented to explain this approach.
Multiple attribute decision making (MADM) methods are very useful in choosing the best alternative among the available finite but conflicting alternatives. TOPSIS is one of the MADM methods, which is simple in its methodology and logic. In TOPSIS, Euclidean distances of each alternative from the positive and negative ideal solutions are utilized to find the best alternative. In literature, apart from Euclidean distances, the city block distances have also been tried to find the separations measures. In general, the attribute data are distributed with unequal ranges and also possess moderate to high correlations. Hence, in the present paper, use of statistical distances is proposed in place of Euclidean distances. Procedures to find the best alternatives are developed using statistical and weighted statistical distances respectively. The proposed methods are illustrated with some industrial problems taken from literature. Results show that the proposed methods can be used as new alternatives in MADM for choosing the best solutions.
Vendor managed inventory (VMI) is one of the most effective methods for reducing bullwhip effect. This paper presents a mathematical VMI model where there are three levels of central storage, multi distribution centers and various retailors. The problem is formulated as a mixed integer programming by considering uncertainty on different input parameters. To cope with uncertainty, the study uses rectangular fuzzy numbers. We also propose two metaheuristics; namely, genetic algorithm and particle swarm optimization to solve the resulted problems for some large instances. The preliminary results have indicated that genetic algorithm could solve the proposed model faster than particle swarm optimization in terms of CPU time reaching to slightly better objective functions.
Nowadays enterprises should consider seeking to reduce the supply chain risks as a crucial part of their activities in order to improve their competitiveness in the international context. Choosing the suitable strategy in connection with assigning some parts of the production process to outside the organization is a complex multi-criteria decision making problem and it gets more complicated when supply chain risk factors as the factors to select the strategy as well as dependence and the close ties between these criteria also be considered. In this paper, after the identification of risks in the supply chain of a medical equipment manufacturer company, dependence and ties between criteria in line with choosing the best strategy among existing alternatives has been examined in the form of a combined ANP-ELECTRE method. This combined model is of high performance to give a solution to the problem considered in this paper. But given the complex and time consuming nature of the AHP and ELECTRE, in this study a meta-heuristic algorithm is developed called SIMANP that despite the simplicity of computing and high-speed, is good in the terms of precision and efficiency. The results of comparing SIMANP algorithm and the proposed ANP - ELECTRE method are presented at the end.
Weather forecasting is essential and demanding scientific task of meteorological services across the world. It is a complex procedure that includes many specific technological field of study. The prediction is intricate process in meteorology because all decisions are made within a facet of uncertainty associated with weather systems. This research finding introduces a novel rough fuzzy computing approach for a short term rainfall forecasts. The model consists of rough set based optimal weather parameter selection module and fuzzy rule based classification module. The proposed fuzzy decision support model is compared with benchmarked classification approaches. The fuzzy classification model used in fuzzy decision support system is trained and tested using the reduct sets generated using proposed maximum frequency weighted feature reduction technique. The optimal reduct set constituting the weather parameters; minimum temperature, relative humidity and solar radiation achieved better prediction accuracy than complete feature set and the reducts. Most of the classification models have shown better accuracy when trained using the selected subsets of the target input. Thorough evaluation of the proposed model has revealed that coupling fuzzy decision support system and rough based pre-processing techniques was a better approach than traditional techniques. The experimental results revealed the proposed rough fuzzy model as a better rainfall prediction approach for modeling short range rainfall forecast.