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The purposes of this paper are to: investigate schedule performance of new and retrofitting green building projects; identify the critical factors that influence the schedule performance of new and retrofitting green building projects; and provide solutions to improve schedule performance of new and retrofitting green building projects.

A questionnaire survey were conducted and responses were received from 34 firms experienced in green building projects in Singapore. After the data from the survey had been analyzed, face-to-face interviews were conducted with two senior project managers to solicit comments on the survey results.

This study identified the degree of project delay in 98 new green building projects and 51 retrofitting green building projects in Singapore. The result indicated that 22 percent of the Singaporean green building projects were plagued with delay and retrofitting projects had a significantly higher likelihood of delay and significantly longer extension than new projects. In addition, “consultant cooperation to solve problems” was the most influential to schedule performance of both new and retrofitting green building projects, and the two project groups agreed on the overall ranking of the factors affecting schedule performance.

There may be geographical limitation on the conclusions drawn from the findings. Also, the sample size was still small, despite a relatively high response rate. In addition, the majority of the respondents were contractors as other project players were reluctant to respond to the survey.

This study provides a clear understanding of the schedule performance of green building projects as well as the critical factors that should be highlighted when constructing green building projects. Also, strategies to overcome the negative impact of these factors allow practitioners to better deal with the potential causes of delay and to attain the schedule performance.

Although construction delays have been widely investigated in previous studies relating to construction management, few have attempted to analyze the schedule performance of new and retrofitting green buildings. Thus, this study adds significantly to the existing research on both green building and construction delay.

The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.

When construction delays occur, it is necessary to ascertain the liabilities of the contracting parties and to direct the appropriate amount of resources to recover the schedule. Unfortunately, delay analysis and schedule compression are normally treated as separate or independent aspects. This paper examines the feasibility of integrating the delay analysis and schedule compression functions into a broad‐scoped two‐stage process. The main issue is shown to be the kind of delay analysis required for each stage of the process and seven existing techniques are illustrated for use in conjunction with schedule compression. Since the current form and assumptions of delay analysis techniques are unlikely to provide the necessary level of feedback reliability for recovering delays, it is necessary to modify these techniques by incorporating some means of delay type scrutiny, excusable delays updating, and treatment of concurrent delays. The modified delay analysis techniques can serve as a basis for negotiation between the client and contractor and hence improve the interdisciplinary relations.

Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.

By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.

This model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.

Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.

Risk factors related delay hinder the schedule performance of most construction projects in the world. It is a critical challenge to realize the advantages of prefabricated construction projects (PCPs) under the negative effect of schedule delay. This paper aims to propose an exhaustive list of risk factors impeding the progress of PCPs and evaluate the collected risk factors based on the cause–effect relations. The ultimate goal is to improve the understanding of the complex relations among various risk factors related delay in PCPs, and also offer managers a reference on aspect of schedule risk management.

This paper proposes a hybrid method of GT–DEMATEL–ISM, that is combing grounded theory, DEMATEL (decision-making trial and evaluation laboratory) and ISM (Interpretative Structural Modeling), to collect, evaluate and structure risk factors related delay for PCPs. The research procedure of this methodology is divided into three stages systematically involving qualitative and quantitative analysis. In the first stage, GT is utilized to implement qualitative analysis to collect the risk factors leading to schedule delay in PCPs. While, the quantitative analysis is to analyze and evaluate the collected risk factors based on the cause–effect relations in the next two stages evaluation by the DEMATEL focuses on quantifying the priority and intensity of the relations between factors. Additionally, ISM is employed to construct the hierarchical structure and graphically represent the pairwise relations between factors.

The outcome of qualitative investigation by grounded theory proposes a theoretical framework of risk factors related delay for PCPs. The framework contains three levels of category, namely, core category, main category and initial category and provides a list of risk factors related delay. Following this finding, evaluation results by the DEMATEL classify factors into cause and effect groups and determine 11 critical delay risk factors. Meanwhile, the findings show that risks referring to organizational management issue foremost impact the progress of PCPs. Furthermore, a systemic multilevel hierarchical structure model is visually constructed by ISM to present the pairwise linkages of critical factors. The model provides the risk transmission chains to map the spread path of delay impact in the system.

The contribution of the study involves twofold issues. Methodologically, this research proposes a hybrid method GT–DEMATEL–ISM used to identify and analyze factors for a complex system. It is also applicable to other fields facing similar problems that require collecting, evaluating and structuring certain elements as a whole in a comprehensive perspective. The theoretical contribution is to fill the relevant research gap of the existing body of knowledge. To the best knowledge of the authors, this paper is the first attempt to integrate qualitative and quantitative research for risk analysis related delay and take the insight into the whole process of PCPs covering off-site manufacture and on-site construction. Furthermore, the analysis of findings provided both a micro view focusing on individual risk factor and a managerial view from a systematic level. The findings also contribute the effective information to improve the risk management related schedule delay in PCPs.

This paper aims to present a graphical comparison method for construction schedules, which illustrates the differences for each individual activity. The method overlays the observed differences on a bar chart creating a representation of whether each activity is ahead, on or behind schedule at a given date.

The method is implemented using a Microsoft Project add-in (plug-in). The paper demonstrates the method and its potential uses with three illustration cases: a time impact analysis, an alternative analysis for the selection of subcontractors and a multi-baseline analysis of an as-built schedule.

The cases included in the paper show that the proposed method uses a simplified and familiar attribute comparison for each activity in a schedule. The method affords flexibility in presenting differences between schedules such as the start/finish dates or duration. As the method does not rely on a specific software application or analysis method, it can be implement to different software applications as well as performance or delay analysis techniques. The method also makes it possible to present multiple and selective baseline comparisons overlaid on an updated or as-built schedule.

The method graphically presents a comparison of start dates, durations and finish dates for each activity that can be integrated with any schedule. The method can be used for forensic analysis as well as project control measures during construction. As the method does not rely on any specific performance or delay calculation method, it can be applied to any forensic analysis technique and delay analysis.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Under the background that engineering, procurement and construction (EPC) contracting model is introduced to adapt to the highly fragmented characteristics of prefabricated construction, the schedule management of general contractor is faced with the challenge of dynamic transmission and interaction of construction scheduling-related risk. The purpose of this paper is to develop the hierarchy of prefabricated construction scheduling-related risks from the perspective of the general contractor, and to analyze the transmission mechanism between risks. The paper also aims to further distinguish the difference of the impact degree of scheduling-related risks, and provide reference for formulating the strategy to alleviate the construction delay.

Based on a review of the literature on prefabricated buildings, this paper identifies 22 scheduling-related risks in construction from the perspective of the general contractor. Semi-structured interviews were then conducted to obtain experts' views on the interrelationships among these risks. Following this, their overall structure was determined by using a hierarchical structure established by using interpretive structural modeling (ISM), and Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) technique was applied to classify them into four groups according to their driving and dependence powers.

The results indicate that the 22 scheduling-related risks in construction followed the inherent path of step-by-step transmission, and all of them could cause different degrees of delays in prefabricated construction. Among them, general experience in contracting projects, the use of emerging technologies and the completeness of the relevant standards and specifications were strong drivers of scheduling delays in construction, and should be prioritized by the general contractor in schedule management. The transitive link between scheduling risks can guide them in developing prevention strategies.

Data quality and reliability risks are the major drawbacks of semi-structured interviews. These were minimized by engaging experts with rich theoretical and hands-on experience in prefabricated construction projects. The hierarchical model only reflects static influence relationships, and so dynamic interactions among scheduling-related risks should be studied in future.

The primary value of this study is in its development of a hierarchical model by using the integrated ISM–MICMAC approach that reflects the interaction between scheduling risks in the construction of prefabricated buildings. The hierarchy of these risks and the results of a “driving-dependence power” analysis can guide the general contractor in taking targeted preventive measures to avoid scheduling delays in the construction of prefabricated buildings.

This paper aims to develop a scheduler for multiple picking agents in a warehouse that takes into account distance and loading queue delay minimization within the context of minimizing makespan (i.e. picking time).

The paper uses tabu search to solve the scheduling problem in a more global sense. Each search iteration is enhanced by a custom local search (LS) procedure that hastens convergence by driving a given schedule configuration quickly to a local minimum. In particular, basic operators transfer demand among agents to balance load and minimize makespan. The new load distribution is further improved by considering a vehicle‐routing problem on the picking assignments of the agents with relocated demands. Loading queue delays that may arise from the reassignments are systematically minimized using a fast scheduling heuristic.

The proposed tabu scheduler greatly improves over a widely practiced scheduling procedure for the given problem. Variants of the tabu scheduler produce solutions that are roughly of the same quality but exhibit considerable differences in computational time.

The proposed methodology is applicable only to the static scheduling problem where all inputs are known beforehand. Furthermore, of the possible delays during picking, only loading queues are explicitly addressed (although this is justifiable, given that these delays are dominant in the problem).

The proposed approach can significantly increase through‐put and productivity in picking systems that utilize multiple intelligent agents (human pickers included), e.g. in warehouses/distribution centers.

The paper addresses a practical scheduling problem with a high degree of complexity, i.e. scheduler explicitly deals with delays while trying to minimize makespan (generally, delays are ignored in the literature to simplify things). In the tabu implementation, an LS procedure is introduced in the metaheuristic loop that enhances the search process by minimizing non‐productive time of picking agents (travel time and delays).

To reduce the time of flight rescheduling, reduce the total delay cost of all flights to a minimum and put forward more references for passengers to take flights, this paper aims to mainly study the recovery of flights affected by snow disaster within the minimum delay time.

The temporal and spatial network flight recovery model is used to optimize all flights of various types of aircraft, and the adjusted flight schedule based on minute delay time is obtained. In addition, for passenger travel flights, the impact of passenger delay cost on the total delay time is minimized as an objective function to calculate the passenger delay cost.

In this paper, the actual departure time of aircraft is sorted in ascending order. Up to five planes can take off from the runway every 5 min, and the 10-min decision interval is successively delayed. The actual arrival time is sorted by the same method and the sequential delay is calculated to obtain the adjusted flight schedule. As a result, it takes less time to reschedule flights.

In this paper, heuristic algorithm is used to adjust the schedule of delayed flights flexibly, which is convenient for manual modification. This decision method has good robustness and can partially adjust the interrupted flights without affecting other scheduled flights while maintaining the stable operation of the whole plan, greatly improving the efficiency of civil aviation operations and reducing the impact of flight delays.