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Improving the competitive advantage in the global marketplace is one of the major goals of any organization where the development of the information technology and associated services plays a significant role. Today, many improvements and optimization initiatives are managed through information technology projects, thus relying on the success of the projects to reach the goal. One of the most important aspects of the project management in the information technology is the uncertainty in estimating the duration of activities that usually require the involvement of human resources who are rarely working on a single task. In this situation, the human behavior can have a major negative effect on both the duration and cost of a project if inadequately managed. One area of the human behavior is called the Student Syndrome which is a phenomenon where doing things is delayed until right before the deadline. In an information technology project this means that programmers postpone their project tasks until the deadline, while before it they work at a very relaxed pace with lots of slack and think there is enough time for doing other things. As a result, they simply waste the time required to solve uncertainties that might impact the project work. The study analyzes and quantifies the impact of Student Syndrome on a single project completion time using a simulation-based approach. It provides an insight to be considered by the project manager to ensure the project to succeed. The impact of Student Syndrome is explained by analyzing the project networks. Simulations show that eliminating the Student Syndrome considerably and immediately positively effects the project completion time shortening it by up to 35 percent compared to the situations with the Student Syndrome. [ABSTRACT FROM AUTHOR]

This paper presents a computationally efficient solution for constraint management of multi-input and multi-output (MIMO) systems. The solution, referred to as the Decoupled Reference Governor (DRG), maintains the highly-attractive computational features of Scalar Reference Governors (SRG) while having performance comparable to Vector Reference Governors (VRG). DRG is based on decoupling the input–output dynamics of the system, followed by the deployment of a bank of SRGs for each decoupled channel. We present two formulations of DRG: DRG-tf, which is based on system decoupling using transfer functions, and DRG-ss, which is built on state feedback decoupling. A detailed set-theoretic analysis of DRG, which highlights its main characteristics, is presented. We also show a quantitative comparison between DRG and the VRG to illustrate the computational advantages of DRG. The robustness of this approach to disturbances and uncertainties is also investigated. [ABSTRACT FROM AUTHOR]

Ancillary services are electricity products which include balancing energy, frequency regulation, voltage support, constraint management and reserves. Traditionally they have been procured by system operators from large conventional power plants, as by-products of the production of energy. This paper discusses the use of markets to procure ancillary services in the face of potentially higher demand for them, caused by rising amounts of intermittent renewable generation. We discuss: the nature of markets for ancillary services; what we really mean by ancillary services; how they are impacted by the rise of distributed generation; how they are currently procured; how they relate to the rest of the electricity system; the current state of evidence on ancillary services markets; whether these markets ever be as competitive as conventional wholesale energy markets, and offer some conclusions. [ABSTRACT FROM AUTHOR]

Companies view Information Technology (IT) as a competitive advantage of the future. In recent years, IT departments have proven to have a significant impact on the business process improvement and better customer service, enabling organizations to become more competitive in the global marketplace. To meet these demands, many IT organizations are now transforming their software development and information technology operations to a combined team, called DevOps. However, with only organizational changes and no changes in how the IT DevOps teams manage their tasks, this is not easy to achieve, especially when there is a tension to keep all resources busy and overload them with a task by maintaining a high work-in-progress. As a result, organizations operating in this mode typically experience a significant degradation in their performance. The paper presents a simulation-based performance assessment that significantly improves the performance of task execution by DevOps teams. An approach is presented to determine how to load and allocate resources and an appropriate allocation of additional (reserve) resources to improve performance. The simulations show a possibility of a 135 percent improvement in the Throughput (the number of completed tasks), work-in-progress reduced to the level of one percent of the completed tasks and the time needed to complete tasks is over 17 times shorter. [ABSTRACT FROM AUTHOR]

In this article, an intelligent predictor–corrector entry guidance approach for lifting hypersonic vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning problem is formulated as a univariate root-finding problem based on a compound bank angle corridor, and two constraint management algorithms are presented to enforce the satisfaction of both path and terminal constraints. Second, a DNN is developed to learn the mapping relationship between the flight states and ranges, and experiments are conducted to verify its high approximation accuracy. Based on the DNN-based range predictor, an intelligent, multiconstrained predictor–corrector guidance algorithm is developed to achieve real-time trajectory correction and lateral heading control with a determined number of bank reversals. Simulations are conducted through comparing with the state-of-the-art predictor-corrector algorithms, and the results demonstrate that the proposed DNN-based entry guidance can achieve the trajectory correction with an update frequency of 20 Hz and is capable of providing high-precision, safe, and robust entry guidance for hypersonic vehicles. [ABSTRACT FROM AUTHOR]

Ancillary services are electricity products which include balancing energy, frequency regulation, voltage support, constraint management and reserves. Traditionally they have been procured by system operators from large conventional power plants, as by-products of the production of energy. This paper discusses the use of markets to procure ancillary services in the face of potentially higher demand for them, caused by rising amounts of intermittent renewable generation. We discuss: the nature of markets for ancillary services; what we really mean by ancillary services; how they are impacted by the rise of distributed generation; how they are currently procured; how they relate to the rest of the electricity system; the current state of evidence on ancillary services markets; whether these markets ever be as competitive as conventional wholesale energy markets, and offer some conclusions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel flexibility exchange strategy to facilitate the management of congestion issues and voltage profiles (e.g., avoiding voltage violation and reducing voltage fluctuation) via minimum participation from customers or aggregators. In the proposed approach, the expectation of voltage profiles and power flow is determined by network constraints and customers’ requirement, and it is used to guide the estimation of network state toward the expected state so that the predefined expectation (regarding voltage profile and power flow) is fulfilled. Availability of flexibility exchange from customers is integrated in estimation process. Flexibility factors are proposed to constrain the variation of network variables including voltage, power consumption/generation and power flow. A genetic algorithm based-optimisation procedure is applied to obtain the minimum power variation from customers (i.e., minimum power variation from customers) while the defined expectation and constraints of flexibility availability are met. The approach is tested out on two representative distribution networks and the results have demonstrated the feasibility of the proposed approach in obtaining optimal flexibility exchange strategy that meets the predefined requirement/expectation whilst involving the least power variation from customers. [ABSTRACT FROM AUTHOR]

A numerical multi-constrained predictor-corrector guidance algorithm is proposed in this study, focusing on real-time longitudinal trajectory generation, constraint management, and lateral guidance improvement. First, a new compound bank corridor is designed to help convert the complicated trajectory planning problem into a root-finding problem, which is identified by a recursive least squares estimation algorithm and solved by a newly proposed period-crossing steepest descent method within a fraction of a second in Matlab. Second, three constraint enforcement operators are designed based on state prediction and feedback theory, and the long-standing constraint violation problem of predictor-corrector algorithms is addressed by on-board bank angle compensation. Additionally, a new predictive lateral guidance algorithm is proposed based on a cross-range proportional decrement strategy, and the resulting new bank reversal logic with only one user-defined parameter has better performance on reversal controllability and guidance robustness than the traditional algorithms. Finally, extensive numerical simulations are carried out for different mission scenarios with significant dispersions, and the new methodology is proved to be capable of autonomous and robust guidance flight for reentry vehicles. [ABSTRACT FROM AUTHOR]

Static security constraint management, formulated as security constrained optimal power flow (SCOPF), is one of the critical tasks performed during planning and operation. These constraints at any operating point can be divided into noncritical and critical constraints, where former can be managed with preventive controls, while the latter cannot. The violation of at least one critical constraint indicates that network is overstressed and needs a prompt activation of remedial actions to preserve system integrity. Accordingly, critical constraint violations (CCVs) must be identified by solving related SCOPF problem, as information on their types and locations can help to devise and activate optimal remedial actions. However, when dealing with overstressed systems, conventional SCOPF methods may not be able to identify CCVs due to problem infeasibility and there is no commonly accepted method to find related CCVs. Extending previously developed metaheuristic approach, this paper first identifies the CCVs and then proposes a novel methodology to devise the most effective remedial actions to mitigate overstressed system operating conditions. The practical relevance of the remedial actions is demonstrated using IEEE 30-bus, 39-bus, and 57-bus test networks. [ABSTRACT FROM AUTHOR]

ABSTRACT: Supply chain design and constraint management are widely‐adopted techniques in industry, necessitating that operations and supply chain educators teach these topics in ways that enhance student learning and retention, optimize resource utilization (especially time), and maximize student interest. The Chantey Castings Simulation provides a platform to accomplish all three criteria. In a fun, collaborative environment, students learn important concepts related to these techniques by identifying obstacles to matching customer demand with sourcing, production, and logistics for a variety of products made from Play‐Doh. Assessment data indicate the simulation is a highly‐effective experiential learning activity. [ABSTRACT FROM AUTHOR]

In construction scheduling, constraints among activities are vital as they govern the schedule solution. Understanding their criticality is essential for better schedule management. This paper presents a systematic method to classify and identify the criticality of schedule constraints for the schedule management from the constraint perspective. In terms of criticality, schedule constraints can be grouped into four types: project-critical, activity-critical, sequence-critical and non-critical. Project-critical constraints are those which govern start/finish time of critical activities and the project end time. Activity-critical constraints define the start/finish time of non-critical activities, and sequence-critical constraints are those whose existence affect the start/finish time of some activities or the project end time. Constraints belonging to any of these groups are vital to a schedule as they cannot be removed from the constraint collection. Non-critical constraints, on the other hand, do not govern either start/finish time of any activity or the project end time. Accordingly, non-critical constraints are redundant and can be removed from the constraint collection without causing any change to the schedule solution. The method proposed was applied to a illustrative case example based on the construction of the main entrance of a nursing house for further interpretation. The proposed classification scheme could shed light on a more in-depth understanding of the nature of criticality and the role of constraints in a schedule, and thus better schedule management may be achieved.

This research constitutes a first trial to examine the impact of public park visit motivations, constraints, and constraint management strategy use on park visitation in South Korea and Asia, and was conducted to compliment the lack of research on public parks. A selfadministered questionnaire survey was administered in ten popular public parks in Seoul. Four hundred thirty one questionnaires were finally utilized for data analysis, for which a structural equation modeling technique was used. The results suggest that park visit constraints play a negative role, while park visit motivations and constraint management strategy use play a positive role in encouraging a park visit. The author recommends further investigation on the specific role of motivations, as motivations did not play a positive role in leisure activity participation in some other studies. [ABSTRACT FROM AUTHOR]

Although institutional discourse is subject to a vast ensemble of constraints, its design is not fixed beforehand. On the contrary, optimizing the satisfaction of these constraints requires considerable discourse design skills from institutional agents. In this article, we analyze how Dutch banks’ mortgage advisors navigate their way through the consultations context. We focus on what we call discourse design explications, that is, stretches of talk in which participants refer to conflicting constraints in the discourse context, at the same time proposing particular discourse designs for dealing with these conflicts. We start by discussing three forms of design explication. Then we will examine the various resolutions they propose for constraint conflicts and show how advisors seek customer consent or cooperation for the proposed designs. Thus our analysis reveals how institutional agents, while providing services, work on demonstrating how the design of these services is optimized and tailored to customers. [ABSTRACT FROM AUTHOR]

Although multidimensional adaptive testing (MAT) has been proven to be highly advantageous with regard to measurement efficiency when several highly correlated dimensions are measured, there are few operational assessments that use MAT. This may be due to issues of constraint management, which is more complex in MAT than it is in unidimensional adaptive testing. Very few studies have examined the performance of existing constraint management methods (CMMs) in MAT. The present article focuses on the effectiveness of two promising heuristic CMMs in MAT for varying levels of imposed constraints and for various correlations between the measured dimensions. Through a simulation study, the multidimensional maximum priority index (MMPI) and multidimensional weighted penalty model (MWPM), as an extension of the weighted penalty model, are examined with regard to measurement precision and constraint violations. The results show that both CMMs are capable of addressing complex constraints in MAT. However, measurement precision losses were found to differ between the MMPI and MWPM. While the MMPI appears to be more suitable for use in assessment situations involving few to a moderate number of constraints, the MWPM should be used when numerous constraints are involved. [ABSTRACT FROM AUTHOR]

Healthcare costs continue to increase dramatically, while quality remains a significant problem. Reform measures initiated by the government will drive expansion of these costs, further stressing taxpayers and employers, and forcing hospitals to adopt fundamental changes as they try to adjust to increased demands for services and to lessening reimbursements from all payers. This struggle is best seen at the point of entry for many at a hospital: the emergency department (ED). It is at the emergency department that patients’ expectations regarding staff communication with patients, wait times, the triage process, capacity and payment will determine a significant part of a hospital’s revenue. Using Dr. Eliyahu M. Goldratt’s Thinking Process, we will determine what core problem(s) are causing a 362-bed regional West Texas hospital emergency department to lose revenue. Evaluation of the current emergency department will determine the Undesirable Effects (UDE). Using that information will lead to the construction of the Current Reality Tree (CRT), which will bring focus to the core problem(s). To break the constraints, which are the core problem(s), an Evaporative Cloud (EC) is generated. And, the end result will be to construct a Future Reality Tree (FRT), which will validate the idea(s) generated in the EC. It was determined that there are ten major UDE’s that affected this hospital’s emergency department. They were focused around staff communication, wait times, triage process, information management, service provided and bill collections. A conclusion was made that the core problem dealt with triaging patients and utilization of the services provided by the hospital. Since the reimbursement rate is affected by the patient’s satisfaction, the areas to focus on would be: triage, education, communication and retention. Although it may be neither feasible nor desirable to meet all the patient’s expectations, increased focus on those areas may increase the emergency department’s efficiency and the hospital’s bottom line.

This research was carried out to investigate the results of the application of 5-steps on-going improvement process one of the elements of Theory of Constraints which is a management system philosophy advocating the view that each company aims to make money has at least one constraint and the performance of the company is determined by this constraint or constraints. 5-steps focusing process which managing the constraint/constraints was applied in the synthetic thinner production process of a production company which operates in the paint industry and then the profitability of the company was evaluated. By following 5-steps process of on-going improvement, capacity constrained resource in the production system was determined. After the identification the capacity constrained resource in the system, optimal product mix was determined and then alternative recommendations were offered for the elimination of the constraint. Changes occurring in the profitability of the company through these recommendations and the profitability in the case that the constraint existing are compared. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to enforce state, output, and control constraints in closed-loop nonlinear systems. The approach is based on the Reference Governor (RG) scheme and leverages set-theoretic methods in control, as well as data-driven offline optimization. Specifically, the approach decomposes the design of the constraint management strategy into two parts: enforcement at steady-state, and during transient. The former is achieved by using the forward and inverse steady-state characterization of the nonlinear system, which is available, for example, in many applications in the automotive industry. The latter is achieved by implementing an RG-based approach, which employs a novel Robust Output Admissible Set (ROAS), which is obtained using data from the nonlinear system. This scheme is systematically studied, and algorithms for its implementation are provided. The scheme is validated using simulations, as well as real experiments on a turbocharged gasoline engine. [ABSTRACT FROM AUTHOR]

The paper provides a survey of 18 years' progress that my colleagues, students (both former and current) and I made in a prominent research area in Psychometrics-Computerized Adaptive Testing (CAT). We start with a historical review of the establishment of a large sample foundation for CAT. It is worth noting that the asymptotic results were derived under the framework of Martingale Theory, a very theoretical perspective of Probability Theory, which may seem unrelated to educational and psychological testing. In addition, we address a number of issues that emerged from large scale implementation and show that how theoretical works can be helpful to solve the problems. Finally, we propose that CAT technology can be very useful to support individualized instruction on a mass scale. We show that even paper and pencil based tests can be made adaptive to support classroom teaching. [ABSTRACT FROM AUTHOR]

This study aims to gain greater insight into scheduling expertise by comparing the work of experts and novices when designing a university timetable. We assumed that the scheduling activity would take place within two dual spaces: the constraints space (CS) and the objects space (OS). Constraints are defined in the strictest sense as relations between variables that cannot be represented in the solution (timetable), whereas objects are constraint satisfactions that can be thus represented. The study shows that experts were more likely than novices to use external representations as activity support. They satisfied many constraints with partially defined objects. On the contrary, novices devoted a long time to managing constraints in their heads before defining only fully specified objects (concrete objects). The objects space could be a suitable activity support for experts. Novices, on the other hand, could benefit from support in managing constraints and translating constraints into objects. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this article, four item selection methods in computerized adaptive testing are examined in terms of classification accuracy and consistency, including two popular heuristics for constraint management, the maximum priority index (MPI) method and the weighted deviation modeling method, as well as the widely known maximum Fisher information method and randomized item selection as baselines. Results suggest that the MPI method is able to meet constraints and keep test overlap rate low. Among the four methods, it is the only one that manages to produce parallel forms in terms of content coverage and, consequently, the only method to which the idea of classification consistency applies. With tests as short as 12 items, the MPI method does fairly well in classifying examinees accurately and consistently. Its performance improves with longer tests. The effects of number of decision categories and cut score locations are also examined. Recommendations are made in the Discussion section. [ABSTRACT FROM AUTHOR]

Healthcare costs continue to increase dramatically, while quality remains a significant problem. Reform measures initiated by the government will drive expansion of these costs, further stressing taxpayers and employers, and forcing hospitals to adopt fundamental changes as they try to adjust to increased demands for services and to lessening reimbursements from all payers. This struggle is best seen at the point of entry for many at a hospital: the emergency department (ED). It is at the emergency department that patients' expectations regarding staff communication with patients, wait times, the triage process, capacity and payment will determine a significant part of a hospital's revenue. Using Dr. Eliyahu M. Goldratt's Thinking Process, we will determine what core problem(s) are causing a 362-bed regional West Texas hospital emergency department to lose revenue. Evaluation of the current emergency department will determine the Undesirable Effects (UDE). Using that information will lead to the construction of the Current Reality Tree (CRT), which will bring focus to the core problem(s). To break the constraints, which are the core problem(s), an Evaporative Cloud (EC) is generated. And, the end result will be to construct a Future Reality Tree (FRT), which will validate the idea(s) generated in the EC. It was determined that there are ten major UDE's that affected this hospital's emergency department. They were focused around staff communication, wait times, triage process, information management, service provided and bill collections. A conclusion was made that the core problem dealt with triaging patients and utilization of the services provided by the hospital. Since the reimbursement rate is affected by the patient's satisfaction, the areas to focus on would be: triage, education, communication and retention. Although it may be neither feasible nor desirable to meet all the patient's expectations, increased focus on those areas may increase the emergency department's efficiency and the hospital's bottom line. [ABSTRACT FROM AUTHOR]

With more and more renewable energy generation (REG) connections, busbar voltage violation and line overloading problems may occur for some parts of a distribution network. However, building new circuits to accommodate REG may have high monetary and environmental costs. This paper considers distribution automation as a supplementary scheme to traditional primary asset investments and analyzes the operational benefits from introducing an autonomous regional active network management system (AuRA-NMS) to a practical distribution system with rich renewable sources. The benefits are quantified in terms of optimal power flow control and investment deferral, and the resulting quantification will inform distribution network operators of the trade-offs between investment in the automation system and in the primary assets, thus helping them to make cost-effective investment decisions. Time-series-based simulation for over an entire year is implemented to calculate the benefits of active power loss and curtailment reductions for AuRA-NMS over the current practice. Part I of this paper illustrates the current schemes for voltage control and constraint management, advanced voltage control and constraint management enabled by the distribution automation, and the annual benefit by introducing the AuRA-NMS to the system with different considerable new DG integrations. Part II analyzes the investment deferral benefit by deploying AuRA-NMS. [ABSTRACT FROM PUBLISHER]

Adherents to the Theory of Constraints (TOC) have made clear their antipathy to precise cost accounting. We argue that they have something in common with early writers who were concerned about the allocation of costs. However, given the lack of attention to the long-term in TOC we explore, using an action research approach over a four year period, the relevance of TOC for both long-term and short-run decisions. We argue that TOC does meet short-run decision relevance although it is not entirely adequate for longer term decisions. [ABSTRACT FROM AUTHOR]

The Theory of Constraints is a new approach and different from many traditional approaches both for it's assumptions and objectives. The Theory of Constraints is a management discipline which provides the determination of the factors which hinders a company reaching its objectives and the application of necessary changes to remove these factors. This theory has been successfully applied by a lot of small and big enterprises that have operations in manufacturing, logistics, distribution, project management, reseach and development, sale and marketing for the last two decade. In this study, the theoretical framework of the Theory of Constraints and its fundamental conceptions are examined.. In addition, innovation and benefits which the theory provides for enterprises are displayed without making a comparison with other management approaches. In the direction of this purpose, studies related to theory of constraints in the literature are analyzed and it is determined consequently that this theory offers more effectual solutions than classical approaches for determining and eliminating problems that obstruct achieving goals. [ABSTRACT FROM AUTHOR]

Abstract: We present the Constructive Query Containment (CQC) method to check query containment and query containment under constraints for queries over databases with safe negation in both IDB and EDB subgoals and with or without built-in predicates. The aim of the CQC method is to construct a counterexample that proves that the query containment relationship being checked does not hold. The method uses different Variable Instantiation Patterns (VIPs) to generate only relevant counterexamples according to the syntactic properties of the queries and the databases considered in each test. The main contribution of the CQC method is threefold: it handles broader cases of queries and database schemas than most previous methods, it checks “true” containment instead of uniform containment (which is a sufficient but not necessary condition for containment) and it is not less efficient than other methods for the cases that they handle. Moreover, we prove also soundness and completeness of our method both for success and for failure. [Copyright &y& Elsevier]

The need to improve quality and productivity in health care is great. The usefulness of production and inventory management techniques is largely unexplored. In this case study from a university hospital in Finland, it is learnt how a change in queuing system for open heart surgery from a single first-in-first-out queue to a double-queue scheduling system can radically increase available capacity. The new double-queue system is based on finding out in advance the estimated duration of surgery ­ i.e. long or short procedures which are based on the cardiological examination and the management of queues separately. [ABSTRACT FROM AUTHOR]

The paper presents concepts and ideas underlying an approach for consistency management in object-oriented (OO) databases. In this approach constraints are considered as first class citizens and stored in a meta-database called constraints catalog. When an object is created constraints of this object are retrieved from the constraints catalog and relationships between these constraints and the object are established. The structure of constraints has several features that enhance consistency management in OO database management systems which do not exist in conventional approaches in a satisfactory way. This includes: monitoring object consistency at different levels of update granularity, integrity independence, and efficiency of constraints maintenance; controlling inconsistent objects; enabling and disabling constraints, globally to all objects or locally to individual objects; and declaring constraints on individual objects. All these features are provided by means of basic notations of OO data models. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract. Scheduling problems in industry are more and more specific, and a generic software can hardly provide the user with an acceptable solution in a fully automated way. As a consequence, cooperation between man and software becomes a major issue for industrial schedulers. We analyse in this paper the specific requirements induced by a cooperative approach and we suggest some solutions oriented towards visualization tools, constraint management and interactive planning. The concept of generalized constraints is suggested in order to increase the modelling capacities of the software. The implementation of these facilities in an industrial software called IO is described. [ABSTRACT FROM AUTHOR]

Architecture, engineering and construction (AEC) design processes involve the participation of many designers who may work independently in geographically distinct locations. Independent designs evolve owing to changes made by the designers and discipline-specific CAD applications, and these changes need to be evaluated and configured with respect to a set of project constraints for overall design consistency. In addition, each designer needs to be informed about the relevant changes made by other designers. Most rework orders result from inadequate sharing of the evolving design information between various participants in the design process. The awareness of important changes through automated constraint management can avoid expensive reworks at the later stages of the projects. In this paper, we present an incremental approach to constraint checking in AEC design configurations. A configuration represents a design state stored in multiple databases: each configuration has a set of discipline-specific databases and an associated set of inter-disciplinary constraints. The constraints are evaluated on the databases to give the third component of configurations: a set of violations. Including violations in the configuration definition allows us to support partially consistent configurations. Partially consistent configurations are important from a practical standpoint since all the design information may not be available, or may not be consistent, at all stages of the AEC design process. We provide a formal description of configurations and the semantics of changes on configurations. This framework facilitates incremental constraint checking to support the notions of persistent and what-if configurations. We also provide a classification of constraints that is helpful in selective evaluation of constraints. [ABSTRACT FROM AUTHOR]

A large engineering project involves participation of many engineers from multiple disciplines The participating engineers work concurrently on their respective designs, and the large amount of data associated with their discipline-specific designs requires efficient data and configuration management support throughout the design process. One of the essential aspects of configuration management is the detection, notification, and resolution of design inconsistencies in a configuration of project databases This paper proposes automated configuration management based on design databases which represent discipline-specific designs in a project, and specified constraints which represent the requirements on these designs A DCV approach consisting of databases (D), constraints (C), and violations (V) is presented In engineering projects, both the data and constraints should scale as the project progresses and, therefore, this approach makes the constraints persistent in the databases to allow efficient storage and management of both the data and constraints The violations identify inconsistencies and store the results of the constraint checking process on configuration databases Early identification and notification of design inconsistencies will result in consistent project configurations with fewer design changes and less engineering reworks [ABSTRACT FROM PUBLISHER]

Management of constraints (e.g. materials and labour) is a major challenge in construction projects. Advanced working packaging (AWP) is an effective constraint-management method. However, one prerequisite for AWP, i.e. constraint modelling, is generally performed manually. Information extraction methods in the industry cannot meet the demands for AWP, because they focus on entity extraction but ignore extraction of semantically rich relations. To address this problem, this study proposes a hybrid deep learning model. A bidirectional long short-term memory and conditional random field (Bi-LSTM-CRF) model and knowledge representation learning (KRL) model are developed to extract entities and relations among entities from text documents, respectively. To better apply the KRL model, the study maps domain classes of entities and then stacks class information in the model structure, while employing synonym mapping to handle entities unseen during training. The overall accuracies for extracting entities and relations can reach 0.936 and 0.884, respectively, and adding class information increases relation extraction performance metrics by 6.63%. In a scenario implementation, it is shown that the model can automate constraint modelling for ongoing projects. Therefore, the model is useful for AWP and can reduce delays and reworks by saving a significant amount of time for constraint monitoring and removal. • A hybrid deep-learning model is built to extract constraint information from project documents. • The model can extract constraint entities and set up relations among them. • Domain class information is stacked in the model structure to improve model performance. • The F1-scores of entity and relation extraction can reach 0.936 and 0.884, respectively. • The model can partially automate constraint modelling and save much time for project teams. [ABSTRACT FROM AUTHOR]

Construction projects face various constraints (e.g., materials and equipment). Constraint management approaches such as advanced working packaging (AWP) can remove constraints and ensure smooth work. However, due to inefficient information extraction, the prerequisite of AWP, i.e., identifying and modelling constraints, are performed manually. Efforts that integrate constraint information into project knowledge bases are also limited. This paper proposes a hybrid approach to automatically extract and integrate constraint information from texts. The approach combines a deep learning model with pre-defined rules. The model extracts constraint entities whereas rules created based on domain knowledge are used to establish relations between these entities. Extracted information is encoded into the original ontologies. The approach can extract both entities and relations with over 90% accuracy. The original ontologies can be successfully enriched and support semantic queries. The approach improves AWP by partially automating constraint identification and modelling as well as ontology development for information integration. • A deep-learning model is trained to extract constraints related entities from project documents • Domain knowledge is used to create rules to setup relations among extracted entities • Extracted entities and relations are encoded into domain ontologies to enrich its contents • The approach partially automates identification/modelling for advanced working packaging • The approach also partially automates ontology development [ABSTRACT FROM AUTHOR]

Concrete bridges are critical infrastructures, which require effective rehabilitation to maintain a good condition. Bridge rehabilitation projects have complex constraints and multiple participants. Constraint management is critical for such projects. Integrating and searching for relevant information is a key step for constraint management to timely remove constraints. However, accessing project information still relies on manual searching, which can delay information flow in constraint management and hinder constraint removal. Thus, this study introduces the concrete bridge rehabilitation project management ontology (CBRPMO) to improve information integration and constraint management. The CBRPMO was created by comprehensively collecting domain knowledge of bridge rehabilitation and following standard procedures. Reasoning rules were combined with an application programming interface (i.e. OWL API) to enable functions not supported in traditional ontologies (e.g. temporal computation and dynamic updating). As such, the CBRPMO can effectively handle dynamic information in ongoing projects. The CBRPMO was validated in a case study. The results show that the CBRPMO can: 1) integrate project information of constraints, tasks and procedures, participants, and relations between these project entities; 2) support various management functions based on dynamic project information, including evaluating project progress, constraint removal, and participants' performance. The CBRPMO contributes to the industry by: 1) extending the application of ontologies in the bridge sector to cover the rehabilitation stage; 2) enhancing functions of conventional ontologies; and 3) reducing information searching time compared to manual searching, which improves constraint management approaches by automating the information searching step. • An ontology is built to automate information searching in the bridge rehabilitation stage. • The ontology focuses on information of project constraints, tasks, and participants. • SWRL, SWQRL, and OWL API are combined to address syntax limitations in traditional ontologies. • The ontology can effectively integrate both static and dynamic project information. • The ontology also supports essential project management functions. [ABSTRACT FROM AUTHOR]