With the accelerated urbanization in China, passenger demand has dramatically increased in large cities, and traffic congestion has become serious in recent years. Developing public urban rail transit systems is an indispensable approach to overcome these problems. However, the high energy consumption of daily operations is an emerging issue due to increased rail transit networks and passenger demands. Thus, reducing the energy consumption and operational cost by using advanced optimization methodologies is an urgent task for operation managers. This work systematically introduces energy-saving approaches for urban rail transit systems in three aspects, namely, train speed profile optimization, utilization of regenerative energy, and integrated optimization of train timetable and speed profile. Future research directions in this field are also proposed to meet increasing passenger demands and network-based urban rail transit systems.
Global ports and maritime shipping networks are important carriers for global supply chain networks, but they are also the main sources of energy consumption and pollution. To limit ship emissions in ports and offshore areas, the International Maritime Organization, as well as some countries, has issued a series of policies. This study highlights the importance and necessity of investigating emergent research problems in the operation management of green ports and maritime shipping networks. Considerable literature related to this topic is reviewed and discussed. Moreover, a comprehensive research framework on green port and shipping operation management is proposed for future research opportunities. The framework mainly comprises four research areas related to emission control and grading policies. This review may provide new ideas to the academia and industry practitioners for improving the performance and efficiency of the operation management of green ports and maritime shipping networks.
Due to the characteristics of hesitant fuzzy sets (HFSs), one hesitant fuzzy element (HFE), which is the basic component of HFSs, can express the evaluation values of multiple decision makers (DMs) on the same alternative under a certain attribute. Thus, the HFS has its unique advantages in group decision making (GDM). Based on which, many scholars have conducted in-depth research on the applications of HFSs in GDM. We have viewed lots of relevant literature and divided the existing studies into three categories: theory, support and methods. In this paper, we elaborate on hesitant fuzzy GDM from these three aspects. The first aspect is mainly about the introduction of HFSs, HFPRs and some hesitant fuzzy aggregation operators. The second aspect describes the consensus process under hesitant fuzzy environment, which is an important support for a complete decision-making process. In the third aspect, we introduce seven hesitant fuzzy GDM approaches, which can be applied in GDM under different decision-making conditions. Finally, we summarize the research status of hesitant fuzzy GDM and put forward some directions of future research.
Seru production is regarded as a new production mode and derived from the production site of Japanese electronics industry. This production mode is proposed to overcome the low flexibility of the assembly line. Seru production has been successfully implemented in Japanese electronics industry, such as Canon and Sony. Benefits from Seru production include rapid response, good flexibility, and high productivity. Seru production has received extensive attention in academic research and production practice. This study reviews the background, characteristics, types, and operation of seru production. The advantages and applicable scenes of seru production are summarized from the perspective of business practice. We compare seru production and famous production modes, i.e., assembly line, cellular manufacturing, and Toyota Production System. The literature on seru production is surveyed and classified. Furthermore, future research directions are provided.
Building information modeling (BIM) is expected to have a large impact on users in the lifeworlds in a construction supply chain. The impact of BIM on users in their lifeworlds is explored using the concepts of Heidegger, Habermas, and Ihde from the perspective of technical mediation. This impact is explored by a case study. BIM mediates and shapes the relationship between users and their lifeworlds and can be characterized as either a hermeneutic or an alterity relationship. BIM conflicts with existing work practices in a ready-to-hand work environment. For users that cannot work with BIM, the work environment remains present-at-hand. The many heterogeneous BIM applications and systems used by the various parties involved result in interoperability problems that are a major barrier to enframing the supply chain by BIM. Although invitation and inhibition of certain actions by BIM may stimulate the rationalization of the lifeworlds, the lack of intrinsic motivation and mutual background knowledge inhibits an alignment of BIM and working practices.
The current physical pre-assembly method of large steel structures is time consuming and costly and requires large sites. Thus, the pre-assembly of large steel structures in a virtual way, starting from building information modeling (BIM), is an interesting alternative to the physical one. In this study, an innovative method for virtual pre-assembly is proposed on the basis of BIM, plane-line-point algorithm, and 3D measurement. This method determines the optimal analytical least squares of the various built components. The technique verifies the feasibility of the steel structure assembly and the fulfillment of the design geometries, starting from the real data obtained by an accurate metric survey of the fabricated steel elements. The method is applied to a real case, and obtained results largely satisfy the prefixed research objectives. Suggestions to improve the proposed method are also discussed.
Understanding of the constitution of client involved decisions is important for future improvements of the processes. Significant decisions in construction projects are reliant on heuristic processes where assumptions are developed from past experience. The paper presents a methodology to collect empirical data in an unstructured manner utilizing participant intuition and experience regarding project level collaboration, a term easily understood by practitioners. Empirical data collected from 6 focus group discussions in Norway and 18 individual interviews in Finland is associated with biases in decision making aimed at bridging the gap of understanding and literature’s insufficient coverage. An analytic framework was developed to suit the diverse emergence of concepts to allow application of psychological principles in a structured manner to empirical data. The paper contributes by identifying types of cognitive and motivational biases in client involved decisions. The biases are found to be alleviated by one another depending on the particular application of the decision. Findings suggest that normative beliefs exist developed from past experience and habitual thinking. A number of emerged biases in this domain are alleviated from normative beliefs which are discussed in this paper.
Governments at all levels are increasingly motivating the private sector to participate in infrastructure development using alternative project delivery methods to relieve financial burden. When designing contracts, governments usually offer incentives while requiring cost or time guarantee to balance project attractiveness to the private sector and fair protection of public interest. However, a practical and critical problem is how to properly design these provisions. Although previous studies have investigated the value of these provisions, a knowledge gap still exists with respect to methods of fairly and effectively designing such provisions. This study fills this gap by developing a methodology that analyzes the appropriateness of guarantee or warranty provisions for contracts. In this study, a contract reliability index is constructed, and a process of evaluating contract reliability is proposed. The New Mexico Highway 44 project, in which three warranty provision arrangements are investigated, is used as a case study to illustrate the analysis process. Results show that although a ceiling clause can effectively motivate the private sector to participate in the project, it sacrifices a significant amount of public benefits. By contrast, although a warranty option can protect public benefits, it cannot effectively incentivize the private sector. A combination of the ceiling clause and the warranty option will therefore result in improved contract provision design. The proposed methodology in this study is especially useful for governments in properly determining contract clauses in infrastructure development.
Job hopping affects the development of industries in terms of efficiency and quality of work. It is a problem for the Chinese construction industry, where excessive job hopping is detrimental to meeting the current daunting challenges involved in the industry’s transformation and efficiency improvement. To provide an exhaustive analysis of this effect, game theory is combined with social relationship networks to create an agent-based simulation model. Simulation results indicate that the frequent job moves of Chinese construction workers have a negative effect on their skill development, employment, and worker relationships, as well as results in sharp increase in employer labor costs. The findings point to the need to act for the benefit of workers and employers and maintain the development of the industry.
The construction industry produces a large amount of data on a daily basis. However, existing data sets have not been fully exploited in analyzing the safety factors of construction projects. Thus, this work describes how temporal analysis techniques can be applied to improve the safety management of construction data. Various time series (TS) methods were adopted for identifying the leading indicators or predictors of construction accidents. The data set used herein was obtained from a large construction company that is based in Singapore and contains safety inspection scores, accident cases, and project-related data collected from 2008 to 2015. Five projects with complete and sufficient data for temporal analysis were selected from the data set. The filtered data set contained 23 potential leading indicators (predictors or input variables) of accidents (output or dependent variable). TS analyses were used to identify suitable accident predictors for each of the five projects. Subsequently, the selected input variables were used to develop three different TS models for predicting accident occurrences, and the vector error correction model was found to be the best model. It had the lowest root mean squared error value for three of the five projects analyzed. This study provides insights into how construction companies can utilize TS data analysis to identify projects with high risk of accidents.
This paper examines the uncertainty events encountered in the process of constructing highways, and evaluates their impact on construction time, on highway projects in South Africa. The rationale for this examination stems from the view held by scholars that the construction of highways is a complex process, taking place in changing environments and often beset by uncertainties; and that there is a lack of appropriate evaluation of these uncertainty events occurring during the construction process. The research made use of a review of extant literature in the area of uncertainty management, and modeling in infrastructure projects, to guide the direction of the study. The inquiry process consisted of brainstorming by highway experts and interviewing them to identify the uncertainty factors that impact construction time.
An uncertainty matrix for South African highway projects was developed, using a quantitative model and descriptive statistics. It emerged from the study that the uncertainty events affecting the construction time of highway projects are distributed across economic, environmental, financial, legal, political, social and technical factors. Also, it was found that each factor might account for several uncertainty events which impact on construction time differently, through a combination of the uncertainty events of the individual construction activities.
Based on the obtained data, an Adaptive Neuro Fuzzy Inference System (ANFIS) has been developed, as a simple, reliable and accurate advanced machine learning technique to assess the impact of uncertainty events on the completion time of highway construction projects. To validate the ANFIS model, the Stepwise Regression (SR) models have been designed and their results are compared with the results of the ANFIS. Based on the predicted impact size of uncertainty events on the time of highway projects, it can be concluded that construction time on South African highway projects is significantly related to the social and technical uncertainties factors.
Contractor: Shanghai Baoye Group Engineering Co., Ltd.
Constructor: Shenzhen Longgang Construction and Works Bureau
Architectural Designer: Shenzhen Institute of Building Research Co., Ltd.
Supervisor: Shenzhen Yinjian’an Engineering Project Management Co., Ltd.
Operator: Shenzhen Jiuyi Green Operation Management Co. Ltd.