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With the continuous development of the wind power industry, wind power plant (WPP) has become the focus of resource development within the industry. Site selection, as the initial stage of WPP development, is directly related to the feasibility of construction and the future revenue of WPP. Therefore, the purpose of this paper is to study the siting of WPP and establish a framework for siting decision-making.

Firstly, a site selection evaluation index system is constructed from four aspects of economy, geography, environment and society using the literature review method and the Delphi method, and the weights of each index are comprehensively determined by combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the entropy weight method (EW). Then, prospect theory and the multi-criteria compromise solution ranking method (VIKOR) are introduced to rank the potential options and determine the best site.

China is used as a case study, and the robustness and reliability of the methodology are demonstrated through sensitivity analysis, comparative analysis and ablation experiment analysis. This paper aims to provide a useful reference for WPP siting research.

In this paper, DEMATEL and EW are used to determine the weights of indicators, which overcome the disadvantage of single assignment. Prospect theory and VIKOR are combined to construct a decision model, which also considers the attitude of the decision-maker and the compromise solution of the decision result. For the first time, this framework is applied to WPP siting research.

The widespread family businesses play an important role in the national economy of developed countries in Europe and North America, or of developing countries in East Asia. However, family business succession is a worldwide difficult problem. The innovative family business succession practices of Robert Bosch GmbH, the German family company which has a history of 130 years (1886-2016), basically follow the trend of evolving from family businesses to social enterprises after further socialization. However, it has its own innovation and uniqueness which is worthy of reference by Chinese family businesses.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

We model the conditions under which parents optimally reallocate time to childcare when an outside agent exogenously restricts the number of hours an employer can demand of a working parent. Theoretically, when the restriction binds, a parent's available time increases. We exploit a series of voluntary and mandated labor-market reforms in South Korea that regulated the statutory and maximum work hours of parents. The government implemented the laws in stages by industry and size of firms. This implementation process generates exogenous variation across families where one or both partners worked at jobs that were or were not affected by the reform. We show the reforms affected work hours and use the predicted changes to investigate the total amount they spent on paid childcare and whether or not they changed the relative use of market and parental care. When fathers get more time (work less), parents spend less money on childcare. A change in mother's work time does not affect expenditures. When parents get more time, they are more likely to spend money on paid childcare for school-age children and more likely to use private academies.

This paper investigates the interactional relationships between sustainable human resource management (SHRM) and organizational performance (OP). Sustainable HRM is an approach that links HRM and sustainability. These studies focused on integrating HR with sustainable developments, such as economic and social aspects, in favour of focusing on the environmental aspect. Organizational change is an ongoing process that has to be managed effectively to keep the change in place for a long time.

A framework was offered to estimate the cause-and-effect relation of the SHRM and OP factors. Data is gathered from professionals from various pharmaceutical industries. This study applied two methods, Fuzzy AHP and DEMATEL Type II. These techniques are used to understand the cause-and-effect factors and their interactions.

It was observed from the findings that the factor of SHRM, such as Social Justice (F2), Green Job Design (F5), Green Training (F6) and Implementation of Green Policy (F8), was the most critical for the pharmaceutical sector that effects Financial performance (F13), Customer Satisfaction (F15) and Market performance (F14). Pharmaceutical firms ought to coordinate public health advocacy efforts, engage in healthcare initiatives and provide financial support for environmentally friendly efforts that improve social and economic conditions.

For this sustainability, managers concentrate on creating an environment that is healthy and acceptable, and they work hard to mitigate the impact of natural factors and repair damage done to the environment; it is essential to move towards sustainable development to resolve environmental problems. Improving HR efficiency is among essential HRM responsibilities, as they expand the knowledge base of the workforce, enhance human capital, and eventually create valuable intangible assets and promote and encourage sustainable pharmaceutical products for some years.

This research paper has presented exclusive worth to the SHRM and organizational performance literature as it employs fuzzy FAHP and DEMATEL type 2. There is less research on SHRM in the pharmaceutical sector with these factors. In addition, FAHP and TYPE 2 DEMATEL are used in very few researches on SHRM approaches.

The purpose of this chapter is to cover banking efficiency using the concept of the Meta frontier function and to study group and subgroup differences in the production technology. This study estimates the technical efficiency (TE) and technology gap ratios (TGRs) for banks in Islamic countries. Using the assumption of the convex hull of the Meta frontier production set using the virtual Meta frontier within the nonparametric approach as presented by Battese and Rao (2002), Battese et al. (2004), and O'Donnell et al. (2007, 2008) and after relaxing this assumption, the study investigates if there is a significant difference between these two methods. To overcome the deterministic criterion addressed to nonparametric approach, the bootstrapping technique has been applied. The first part of this chapter covers the analytical framework necessary for the definition of a Meta frontier function and its estimation using nonparametric data envelopment analysis (DEA) in the case where we impose the assumption of the convex production set and follows in the case of relaxation of this assumption. Then we estimated the TE and the TGR in concave and nonconcave Meta frontier cases by applying the Bootstrap-DEA approach. The empirical part will be reserved for highlighting these methods on data bank to study the technical and technological performance level and prove if there is a difference between the two methods. Three groups of banks namely commercial, investment, and Islamic banks in 17 Islamic countries over a period of 16 years between 1996 and 2011 are used.

The workforce management model conceptualised for the effective management of the construction workforce was subjected to expert scrutiny to determine the suitability and applicability of the identified practices and their attributed variables to the construction industry. In achieving this, a Delphi approach was adopted using experts from construction organisations in South Africa. These experts comprised workforce management personnel and construction professionals in senior management positions. The data were analysed using appropriate statistical tools such as interquartile deviation, Kendell’s coefficient of concordance, and chi square to determine consensus among these experts. After a two-round Delphi, the seven constructs proposed in the conceptualised workforce management model were adjudged to be important and worthy of adoption by construction organisations seeking to improve workforce management in the current fourth industrial revolution era.

Using the American and the French time-use surveys, we examine whether people have a preference for a more diversified mix of activities, in the sense that they experience greater well-being when their time schedule contains many different activities rather than is concentrated on a very small number. This could be due to decreasing marginal utility, as is assumed for goods consumption, if each episode of time is conceived as yielding a certain level of utility per se. With returns to specialization, people would then face a trade-off between efficiency and diversity in choosing how to allocate time. We examine these issues and investigate potential gender differences, considering both instantaneous feelings and life satisfaction.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.