Energy development concerns not only the development of renewable energies but also the shift from centralised to clean, decentralised power generation. The development of decentralised energy (DE) is a core part of the energy and economic strategies being adopted around the world that drives the progress toward a highly sustainable future. This paper reviews the concepts, development status, trends, benefits and challenges of DE systems and analyses the existing models and methods for assessing the performance of these systems. A hierarchical decision model for evaluating the performance of DE systems is also constructed based on the framework of multiple criteria decision analysis, which considers the identification, definition and assessment grade of decision criteria. The evidential reasoning approach is applied to aggregate assessment information in a case study of the implementation of an intelligent decision system. Sensitivity and trade-off analyses are also conducted to show how the proposed model can be used to support decision making in DE systems.
The low carbon energy transition has attracted worldwide attention to mitigate climate change. Renewable energy (RE) is the key to this transition, with significant developments to date, especially in China. This study systematically reviews the literature on RE development to identify a general context from many studies. The goal is to clarify key questions related to RE development from the current academic community. We first identify the forces driving RE development. Thereafter, we analyze methods for modeling RE developments considering the systematic and multiple complexity characteristics of RE. The study concludes with insights into the target selection and RE development roadmap in China.
Due to uncertainties in water supply, there is growing demand for water resource management in enterprises. In this study, we evaluated the effects of companies’ water-saving reconstruction projects. We used Hina Advanced Materials Company as a case to construct an investment decision model to (1) calculate the internal and external costs of water resources based on circular economic value analysis theory, and (2) locate the level of water resources circulation. We adopted gray situation decision analysis to identify the typical problems that occur in water resource utilization. Moreover, we demonstrated optimization plans for different potential improvements, thereby providing guidance and references for water resource cost management and the comprehensive optimization of environmental benefits. We concluded that the circulation economic value analysis model can effectively display the flow and amount of value derived from water resource flows, thereby providing guidance and suggestions for optimizing water resource flows.
The mechanism of risk allocation is designed to protect all stakeholders, and it is vital to project success. Qualitative and quantitative ways of optimizing risk allocation have been well documented in extant literature (e.g., allocation principles, models, and solutions), and the foci of existing research are usually the maximization of rational utility. Few research has focused on partners’ social preferences affecting the output of risk allocation. This study presents a quantitative approach based on modeling alliance member (AM)’s inequity aversion (IA) to analyze risk-sharing arrangements in an alliance project. Fehr and Schmidt’s inequity-aversion model is integrated into modeling partner’s utility. This paper derives results for an alliance leader (AL)’s optimal risk-sharing ratio and AM’s optimal risk-management effort simultaneously. The derivation is based on solving a restrained optimization problem using the conception and methods from Stackelberg game theory. Results show that an AM’s IA significantly affects risk allocation between AL and AM. Specifically, envious preference is positively related to AL’s optimal risk-sharing ratio, whereas guilty preference negatively affects AL’s optimal risk-sharing ratio. These findings will be of interest to academics and practitioners involved in designing alliance negotiations.
This paper examines the current state of project cultures in the German turnkey construction industry and the ideal project cultures in terms of partnering from the perspective of various key stakeholders (i.e., Investors, General Contractors, (Sub-)Contractors and Designers). To investigate the current and ideal cultures, data were gathered among the key stakeholders by means of a survey study with 72 respondents divided over 12 companies. The respondents rated the current and desired cultures by using the Organizational Culture Assessment Instrument, which belongs to the Competing Values Framework. The investigations show many similarities and differences between the stakeholder perspectives of the current and the idealized partnering project cultures. Mainly, the General Contractors desire more cooperative behaviors than the (Sub-)Contractors, and the Investors desire more pronounced flexibility than the General Contractors. All stakeholders desire a cultural change from highly competitive behaviors toward more cooperation. Changes in terms of clear procedures or more flexibility are only desired by the Designers. Defining both the current and an ideal partnering project culture enables academics and project managers to compare their actual project cultures to an ideal situation. With such an approach, academics and project managers could measure whether new tools or changes in resources affect their project cultures toward a partnering project culture.
Carbon emission reduction is the only way to alleviate environmental problems, such as global warming. Effective evaluation of carbon performance can help enterprises to carry out energy saving and emission reduction activities to a certain extent and promote sustainable development. This paper constructs a carbon performance evaluation index system that includes the four dimensions of carbon resource (energy) input, cycle, output, and carbon management by incorporating the principles of circular economy and the theory of resource value circulation from the perspective of the flow trajectory of carbon-containing resources in the circulation of enterprises combined with the production characteristics of thermoelectric enterprises. Subsequently, combined with the case study, this paper discusses the scientific and practical nature of the system and provides another way of thinking for carbon performance evaluation of micro-enterprises in other industries. This paper expands the application boundary of matter–element model and supplements the literature of carbon performance, which has certain theoretical and practical significance.
The field of engineering management usually involves evaluation issues, such as program selection, team performance evaluation, technology selection, and supplier evaluation. The traditional self-evaluation data envelopment analysis (DEA) method usually exaggerates the effects of several inputs or outputs of the evaluated decision-making unit (DMU), resulting in unrealistic results. To address this problem, scholars have proposed the cross-efficiency evaluation (CREE) method. Compared with the DEA method, CREE can rank DMUs more completely by using reasonable weights. With the extensive application of this technique, several problems, such as non-unique weights and non-Pareto optimal results, have arisen in CREE methods. Therefore, the improvement of CREE has attracted the attention of many scholars. This paper reviews the theory and applications of CREE, including the non-uniqueness problem, the aggregation of cross-efficiency data, and applications in engineering management. It also discusses the directions for future research on CREE.
Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries. Motivated by the major development strategies and needs of industrial intellectualization in China, this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization, as well as their application to smart industrial engineering. First, this study describes a general methodology for the fusion of data analytics and optimization. Then, it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing. Finally, it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization. The framework uses data analytics to perceive and analyze industrial production and logistics processes. It also demonstrates the intelligent capability of planning, scheduling, operation optimization, and optimal control. Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing, resources and materials, energy, and logistics systems, such as high energy consumption, high costs, low energy efficiency, low resource utilization, and serious environmental pollution. The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency. Therefore, industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.