This paper presents an order level inventory system with time
dependent Weibull deterioration and ramp type demand rate where
production and demand are time dependent. The proposed model of this
paper considers economic order quantity under two different cases. The
implementation of the proposed model is illustrated using some
numerical examples. Sensitivity analysis is performed to show the
effect of changes in the parameters on the optimum solution.

Assembly line balancing problem is an approach of assigning a set of tasks to an ordered
sequence of workstations. This assignment needs to be made in such a way that the
underlying precedence constraints are not violated and efficiency measures are optimized
subject to the restriction of the cycle time constraint. Research works, reported so far, mainly
deal with the minimization of balancing loss, subject to precedence constraints. Lack of
uniqueness in those optimum solutions and pressing demand to include system loss in the
objective function have led to the present work of minimization of both balancing and system
loss. As there is no standard measure for system loss, the current work proposes a measure
for system loss and offers solution to this bi-objective problem.

In this paper, a single item deterministic inventory model with two separate warehouses called
owned warehouse/show-room (OW) and rented warehouse (RW) is developed. The proposed
model of this paper also considers a realistic assumption regarding the storage capacity of the
rented warehouse. Demand is a function of selling price, advertisement of an item and
displayed inventory level in OW. The stocks of RW are shipped to OW under bulk release
pattern where shortages are not allowed. We discuss different scenarios of the proposed model
to address relative size of stock dependency parameters and the capacity of owned warehouse.
For each scenario, the corresponding problem is formulated as a constrained mixed integer
nonlinear programming problem with three integer and two non-integer variables and a real
coded genetic algorithm (RCGA) is developed to solve the resulted problem. The proposed
model of the paper is also examined using some numerical examples and sensitivity analysis is
performed.

The ordering strategy for a single period inventory model is the key to achieve success in the
competitive business environment. This article considers demand in a form of fuzzy number
and discusses the SPIM in which the retailer has the opportunity to reorder once during the
period. The entire period/season is divided into two slots and the reorder is to be made during
the mid-season after the early-season demand has been observed. The objective is to find the
expected optimal order quantity together with profit maximization. We illustrate the
implementation of the proposed model using a numerical example and explain that the explicit
consideration of this reordering opportunity could lead us to better results in terms of
profitability.

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000–2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.

In this research, we conduct a reverse study to design a compatible model of share issue privatization for Tehran stock exchange (TSE) development. Privatization and stock market development are known as two representatives of economic growth. Governments use these two economic means to achieve their development goals. In this study, the impact of share issue privatization on stock market development is studied and an optimal model of share issue privatization for Tehran stock exchange (TSE) development is introduced. To study the effect of share issue privatization on key market development variables panel data of privatization index(IPIX), transaction volume, turnover velocity ratio and closing price of each TSE listed company are entered in co integration and error correction models (ECM) during March 2008-June 2011. The result of statistical studies shows a significant relation between privatization index (IPIX) growth and the growth of each of Tehran stock exchange development proxies. Then to design a compatible model of share issue privatization for Tehran stock market development, Delphi survey following Schmitt approach during 4 phases is implemented.

Supply chain is a complicated networking of suppliers, manufacturers, warehouses, distributors and end customers. Through this network, we can easily see the flow of raw materials, information and money. In the modern global manufacturing environment, change has become a constant phenomenon. Change occurs due to fluctuation in technology, environmental requirements, regulatory policies, societal needs and the last but economy is the most important thing. Uncertainties in supply and demand, globalization of the market, shorter product life cycle, the increasing use of manufacturing product, fast development of information technology, diversified demand of customer for products distribution and logistics partners are responsible for making more complex supply chain network. Because of all these phenomena emergence of Next–Generation of supply chain management is inevitable. Therefore, in this article we try to incorporate several issues and concerns regarding future supply chain such as natural disasters, problems in outsourcing and import-export issues associated with goods, raw material and several related issues such as taxation, cultural aspects, risk, reliability and last but not the most important one is associated with environmental issues. During the next-generation of supply chain management, the mode of production as well as management is changing from product oriented towards customer’s demand or service oriented. Another salient feature of this paper is to demonstrate how to do the planning and management of the next-generation supply chain so that it would maximize profit in the chain rather than maximizing profit of an organization in isolation.

A simple and environmentally friendly reaction has been developed for the first time for one-pot synthesis of B-amino alcohol derivatives from aromatic phenols, epichlorohydrin and amines by using phase transfer catalysts (PTC) and Aspergillus Oryzae lipase biocatalyst. This method provides access to pharmaceutically relevant products in excellent yields with high regioselectivity. The remarkable catalytic activity and reusability of lipase was possible up to four consecutive cycles.

Today global warming is on the rise and the natural resources are getting consumed at a faster rate. Power consumption has increased many folds to cater the human need. Thus renewable energy resources are the only option available at this juncture. Wind energy is one of the renewable energy. Location selection for wind farm takes an important role on power generation. However, the location selection is a complex multicriteria problem due to the criteria factors which are conflicting in nature as well as uncertain. The process becomes more complex when a group of decision makers are involved in decision making. In the present study, a COPRAS (COmplex PRoportional ASsessment) based multi-criteria decision-making (MCDM) methodology is done under fuzzy environment with the help of multiple decision makers. More specifically, this study is aimed to focus the applicability of COPRAS-F as a strategic decision making tools to handle the group decision-making problems.

The evolving technology has pushed machine learning techniques to replace human smartness. A machine learning model is capable of learning and replicating like our brain. This approach of data-driven model is implemented to predict the cutting force in machining of Ti6Al4V. Titanium alloys are commonly used in high strength applications due to their excellent properties. These same properties make the machining of the titanium alloy complicated. An attempt has been made for finding the cutting force under minimum quantity lubrication (MQL). MQL is a sustainable manufacturing-based lubrication system. Taguchi’s approach was used to attain a full factorial design for combination of different parameters. Accordingly, a neural network (NN) model was developed which was capable of predicting cutting forces based on the trained model. The proposed model could be implemented to find optimal parameters in shortest duration, thereby eliminating the need for experimental computations.