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Cycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.

A stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.

First, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.

A novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

The overall goal of this chapter is twofold. First, the authors aim to identify indigenous phenomena that influence employee turnover and retention in the Chinese context. Second, the authors link these phenomena to the contextualization of job embeddedness theory. To achieve the goal, the authors begin by introducing three macro-level forces (i.e., political, economic, and cultural forces) in China that help scholars analyze contextual issues in turnover studies. The authors then provide findings in the literature research on employee retention studies published in Chinese academic journals. Next, the authors discuss six indigenous phenomena (i.e., hukou, community in China, migrant workers, state-owned companies, family benefit prioritization, and guanxi) under the three macro-level forces and offer exploratory propositions illustrating how these phenomena contribute to understanding employee retention in China. Finally, the authors offer suggestions on how contextualized turnover studies shall be conducted in China.

The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error.

The paper proposes a wear model of a metropolitan train wheel based on a discrete state space model; the model considers the wheel’s stochastic degradation and measurement error simultaneously. The paper estimates the RUL on the basis of the estimated degradation state. Finally, it presents a case study to verify the proposed approach. The results indicate that the proposed method is superior to methods that do not consider measurement error and can improve the accuracy of the estimated RUL.

RUL estimation is a key issue in condition-based maintenance and prognostics and health management. With the rapid development of advanced sensor technologies and data acquisition facilities for the maintenance of metropolitan train wheels, condition monitoring (CM) is becoming more accurate and more affordable, creating the possibility of estimating the RUL of wheels using CM data. However, the measurements of the wheels, especially the wayside measurements, are not yet precise enough. On the other hand, few existing studies of the RUL estimation of train wheels consider measurement error.

The approach described in this paper will make the RUL estimation of metropolitan train wheels easier and more precise.

Hundreds of million yuan are wasted every year due to over re-profiling of rail wheels in China. The ability to precisely estimate RUL will reduce the number of re-profiling activities and achieve significant economic benefits. More generally, the paper could enrich the body of knowledge of RUL estimation for a slowly degrading system considering measurement error.

This study develops a model and algorithm to solve the decentralized resource-constrained multi-project scheduling problem (DRCMPSP) and provides a suitable priority rule (PR) for coordinating global resource conflicts among multiple projects.

This study addresses the DRCMPSP, which respects the information privacy requirements of project agents; that is, there is no single manager centrally in charge of generating multi-project scheduling. Accordingly, a three-stage model was proposed for the decentralized management of multiple projects. To solve this model, a three-stage solution approach with a repeated negotiation mechanism was proposed.

The experimental results obtained using the Multi-Project Scheduling Problem LIBrary confirm that our approach outperforms existing methods, regardless of the average utilization factor (AUF). Comparative analysis revealed that delaying activities in the lower project makespan produces a lower average project delay. Furthermore, the new PR LMS performed better in problem subsets with AUF < 1 and large-scale subsets with AUF > 1.

A solution approach with a repeated-negotiation mechanism suitable for the DRCMPSP and a new PR for coordinating global resource allocation are proposed.

This paper explores the association between directors’ and officers’ liability insurance (D&O insurance) and management tone manipulation.

This study uses data from A-share listed non-financial companies from 2009 to 2021 as its sample for empirical tests. In addition, the study relies on text analysis and the construction of models to investigate the relationship between D&O insurance and management tone manipulation.

The authors find that the purchase of D&O insurance will lead to management tone manipulation in the “management discussion and analysis” part of companies’ annual reports, and operating risk and agent cost are the two paths for the effect. Further analysis shows that having a male CEO and employing high-quality auditors can weaken the positive impact of D&O insurance on tone manipulation.

This paper provides a new approach for studying the literature related to D&O insurance and management behavior, and the findings enrich our understanding of the influencing factors and the mechanism of management tone manipulation, thus revealing policy implications for further standardization of the terms and system of D&O insurance in China.

This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.

A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.

Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.

The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

Effective performance management (PM) in public–private partnership (PPP) projects is critical to realizing value for money (VFM). This study aims to provide an in-depth understanding of problems existing in PPP PM and possible avenues for improvement, presenting an experimental system to verify that building information modeling (BIM) and other information communication technologies can improve PPP PM.

The mixed research method adopted in this study combined empirical research with experimental research. Semistructured interviews were used to ascertain the current situation of PPP PM with the help of Nvivo software. A BIM-based performance management system (BPMS), which combines BIM with Web and Cloud technology, was then constructed to achieve performance monitoring, performance measurement, and performance-based payment. Finally, a case study was introduced to explain the function application of the proposed system.

The case demonstration verified is found to verify that the developed BIM-based execution framework for PPP PM can effectively guide stakeholders toward achieving mixed PM, promote effective PM, and improve work efficiency with the support of BIM and other information and communication technologies.

Through the development of a BPMS for PPP projects, the effectiveness and efficiency of PM are improved. Practical PM applications are also provided to different stakeholders, through which the key performance indicators and the behaviors of the government and private-sector partners can be monitored to form a more comprehensive and reasonable PM mechanism and promote the realization of VFM in PPP projects.

The paper aims to identify and analyze passengers’ riding paths for providing better operational support for digital transformation in megacity metro systems.

The authors develop a method to leverage certain passengers’ deterministic riding paths to corroborate other passengers’ uncertain paths. Using Automatic Fare Collection data and train schedules, a witness model is built to recover the actual riding paths for passengers whose paths are unknown otherwise. The identification and analysis of passenger riding paths between three different types of origin–destination) pairs reveal the complexity of passenger path choice.

The results show that passenger path choice modeling is usually characterized by complexity, experience and partial blindness. Some passengers choose paths that are not optimal due to their experience and limited access to overall metro system information. These passengers could be the subject of improved path guidance in light of riding efficiency improved through digital transformation.

This research contributes to the improvement of metro management and operations by leveraging ongoing digital transformation in megacity metro systems. Based on the riding paths and trip chains of a large number of individual passengers identified by the proposed method, metro operation management could prevent risks in areas with concentrated passenger flow in advance, optimally adjust train schedules on a daily basis and deliver real-time riding guidance station by station, which would greatly improve megacity metro systems’ service safety, quality and operational efficacy over time.

The purpose of this paper is to investigate the literature on the treatment of primary pupils and inspecting the role of environmental psychology, e-learning, learning style and school design on the behavior of students in elementary schools.

A questionnaire was designed to evaluate the components of the model. Experts with significant experiences in the field of students’ behavior revised the surveys. Data were collected from 400 teachers of the elementary schools in Iran. The SMART-PLS 3.2 and SPSS 22 software package were used in the field of questionnaires’ statistical analysis.

Findings confirmed the suggested model’s validity for elementary students’ behavior assessment. The consequences of this research illustrated the effect of environmental psychology on the behavior of elementary students. In addition, the authors were concluded that intention to e-learning has a significant role in developing the action and behavior of the elementary students. Moreover, the learning style has an affirmative and considerable impact on the behavior of elementary students. Finally, school design has an affirmative and significant effect on the manner of the elementary students.

The consequences of this research have provided some traces about the basic perspectives, which have to be in the center of attention of administrators. For instance, school design and learning style sound to be a decisive mechanism for improving action and learning behavior. In addition, educational leaders may use the findings to evaluate their school facilities and define where developments will have the most significant impact or planners may use the results to assist architects in the design and construction of new educational services.

This study builds a valuable contribution by focusing on pupil environmental psychology, e-learning, learning style and school design in elementary schools by enlightening the connection between them and students’ manner.