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.

Teaching–Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. This paper presents an improved variant of TLBO algorithm, called Improved Teaching–Learning-Based Optimization (ITLBO). A performance comparison of the proposed method is provided against the original TLBO and some other algorithms. The improved TLBO algorithm shows a marked improvement in performance over the traditional TLBO on several benchmark optimization problems.

The simultaneous scheduling decisions between production systems and material handling systems are highly significant for a substantial reduction in makespan and improvement in throughput of flexible manufacturing system resources. In the absence of appropriate scheduling of production resources, the optimum utilization of FMS resources is not harnessed which turns into wastage of resources. In the present study, investigations are carried out for the sustainable multi-objective scheduling of automatic guided vehicle and flexible manufacturing system by the application of a grey wolf optimization algorithm (GWO). Initially the Giffler and Thompson (GT) algorithm [Giffler, B., & Thompson, G. L. (1960). Algorithms for solving production scheduling problems. Operations research, 8(4), 487-503.] along with four different priority hybrid dispatching rules (PHDRs) are applied for the development of the production center schedule thereafter the grey wolf optimization algorithm is applied for the yield of the sustainable multi-objective schedul-ing of automatic guided vehicles (AGVs) and the FMS together with an objective to minimize the total distance travel and number of backtracking of cruising automatic guided vehicle in the U type flexible manufacturing system facility. The applied methodology is evaluated by conducting computational experiments on a benchmark flexible manufacturing system configuration considered from the literature. The results obtained from the computational experiments clearly show that the proposed application of grey wolf optimization algorithm outperforms the other applied procedures in the literature.

Existing literature on lean principles reveals the impact of lean principles on the organizational performance. During the past few years, the Indian manufacturing context has been competing with the global competitors directly to sustain their presence. One of the big motivations behind this is the steps taken in favor of replacing the policies and regulations for the manufacturing context by the government of India. In present, the manufacturing context is still far away to get the sustainable market because of customer perception variation i.e. cost, delivery and quality re-lated issues. To overcome the uncertainties based on the attributes i.e. quality, productivity, de-livery etc., almost all the manufacturing units use the basic of lean principle i.e. apply the 5’S. Whereas the heavy industries used the VSM approach especially the automotive product manu-facturers. The work presented in this paper gives an insight on the application of lean principles in the manufacturing context and analyzes the impacts using ISM approach.

Accountability is instrumental for ensuring that a trusting relationship exists between shareholders and management of corporations in order that there will be enhanced investor confidence. Towards this end, corporate governance measures are instituted to make the executives or management of business organizations accountable for their stewardships of the organizational resources or shareholders’ investments. It is against that backdrop that the Securities and Exchange Commission in Ghana has also developed a code on best practices on corporate governance. However, the extent to which the provisions in the code are consistent with the theoretical and empirical literature is unknown. This paper, therefore, sought to explore whether or not gaps exist between the corporate governance policy and practices in Ghana and extant literature. This paper achieves this by examining characteristics of the board as they exist in Ghana in relations to the literature. The characteristics examined in this paper include responsibilities, optimal size, independence, board composition, and audit and compensation committees of boards. Recommendations are made based on the literature to address gaps that exist.

This study investigates the association between firm characteristics, corporate governance attributes and the level of corporate disclosure of listed firms in India. The research paper has been based on a sample of 60 firms listed in the Bombay Stock Exchange (BSE) / National Stock Exchange (NSE) during the study period from 2000-01 to 2009-10. The study has used the Standard & Poor (2008) model for measuring the level of corporate disclosure. To examine the association between explanatory variables and the level of corporate disclosure, multiple regression model has been used. The results suggest a positive relationship between board size, ratio of audit committee members to total board members, family control, CEO duality, firm size, profitability, liquidity and the extent of corporate disclosure. However, the degree of corporate disclosure is negatively related to board composition, leverage and age of the firm.