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The construction industry has extensively applied the fast-tracking approach to the demanding need for the fast delivery of infrastructure projects. However, the fast-track strategy might be threatened by distinctive risks or changes in risk characteristics that emerge when activities are overlapped (overlapping risks). This article proposes a risk assessment simulation model to quantify the economic impact of overlapping risks on fast-track infrastructure projects. The model uses Monte Carlo simulation and a proprietary engine solution for the optimization procedure. It quantifies the overlapping risk impacts in the project duration and cost that could originate in three different overlapping degrees and evaluates the optimal overlapping degree to reduce the impact of the overlapping risks. The model demonstration used a commercial renovation project. The results suggest that overlapping risks have a high potential impact on the total cost, although with a high probability of attaining the target duration. Eight top risks affected the total duration, cost, or both. The optimum overlapping to reduce the economic impact and achieve the target project duration combines different overlapping degrees. This study contribution is a model for fast-track projects considering overlapping risks, their impact characteristic as a distribution, and the potential relationship between these risks. [ABSTRACT FROM AUTHOR]

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Tensor completion methods based on the tensor train (TT) have the issues of inaccurate weight assignment and ineffective tensor augmentation pre-processing. In this work, we propose a novel tensor completion approach via the element-wise weighted technique. Accordingly, a novel formulation for tensor completion and an effective optimization algorithm, called tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed. In addition, we specifically consider the recovery quality of edge elements from adjacent blocks. Different from traditional reshaping and ket augmentation, we utilize a new tensor augmentation technique called overlapping ket augmentation, which can further avoid blocking artifacts. We then conduct extensive performance evaluations on synthetic data and several real image data sets. Our experimental results demonstrate that the proposed algorithm TWMac-TT outperforms several other competing tensor completion methods. The code is available at https://github.com/yzcv/ TWMac-TT-OKA [ABSTRACT FROM AUTHOR]

The construction industry has extensively applied the fast-tracking approach to the demanding need for the fast delivery of infrastructure projects. However, the fast-track strategy might be threatened by distinctive risks or changes in risk characteristics that emerge when activities are overlapped (overlapping risks). This article proposes a risk assessment simulation model to quantify the economic impact of overlapping risks on fast-track infrastructure projects. The model uses Monte Carlo simulation and a proprietary engine solution for the optimization procedure. It quantifies the overlapping risk impacts in the project duration and cost that could originate in three different overlapping degrees and evaluates the optimal overlapping degree to reduce the impact of the overlapping risks. The model demonstration used a commercial renovation project. The results suggest that overlapping risks have a high potential impact on the total cost, although with a high probability of attaining the target duration. Eight top risks affected the total duration, cost, or both. The optimum overlapping to reduce the economic impact and achieve the target project duration combines different overlapping degrees. This study contribution is a model for fast-track projects considering overlapping risks, their impact characteristic as a distribution, and the potential relationship between these risks.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then we further provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently, in which the established machine learning-based model is exploited to explore the relative relationships of channel gains of users and obtain several fixed grouping patterns via Merge operation. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed GuG scheme achieves significant performance gains in terms of system sum rate. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. In most existing NOMA user grouping approaches, users are grouped into disjoint groups, which may lead to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then, we address this problem by exploiting the overlapping coalition formation (OCF) game framework, and we further propose an OCF-based algorithm in which each user can be self-organized into a desirable overlapping coalition structure. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed OCF-based GuG NOMA scheme achieves significant performance gains in terms of system sum rate. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • High power diode laser surface hardening AISI 4130 is investigated by DOE approach. • Laser power, focal plane position, scanning speed are varied as input variables. • Geometry of the hardened area, micro hardness and microstructure are investigated. • Hardness in diode laser hardening process is 3 times of base metal hardness. • Effect of overlapping percentage is studied in the optimum setting. Abstract Laser surface hardening of AISI 4130 carbon steel was conducted with a high power diode laser using Response Surface Methodology. Scanning speed, laser power and focal plane position were considered as the input process variables and cross sectional geometry of the hardened area, average micro-hardness and the ferrite phase percentage were considered as process responses. The effect of parameters on the responses variations were investigated using analysis of variance. Microstructure evaluation of the laser hardened zone was performed using optical and field emission scanning electron microscopy. Results indicated that by increasing the laser power and decreasing the scanning speed and focal plane position, higher surface hardness with more penetration in depth, higher average micro-hardness and minimum ferrite percentage will be achieved. Finally, the process was optimized by desire ability approach based on the applied statistical analyses. Minimum value of percentage of the ferrite and maximum value of the other responses are considered as optimization criteria. The recommended optimized results were validated using the experimental tests. The results show that the hardness of the diode laser hardening process is 3 times of the hardness of the base metal. Laser-overlapping scanning is performed in the optimum setting and effect of overlapping percentage is investigated. [ABSTRACT FROM AUTHOR]

Planning and scheduling are challenging processes, particularly for projects with continuously evolving requirements and constraints such as design-build and turnkey projects. In the literature, schedule optimization models can only handle predefined activities and fixed constraints. To better support dynamic projects, this paper proposes a flexible constraint programming (CP) framework that optimizes schedules both at the early planning stage and immediately before construction. At the early planning stage, the model selects among alternative network paths and construction methods to determine the most suitable work packages for the project. Later as more constraints become refined, the optimization model helps to meet the persistent milestones, deadlines, and resource limits, using a variety of activity-crashing strategies without changing the committed construction methods. Experimenting with a case study proved the flexibility of the model, its unique support for planning, and its ability to consider evolving project constraints. The proposed model contributes to developing automated decision support systems for cost effectively meeting the evolving schedule constraints. [ABSTRACT FROM AUTHOR]

In construction schedules, a logical relationship between two activities is specified by the relationship type and a fixed lag time. This rigid representation, however, does not consider the situation when two activities have a degree of flexibility in their relationship. Such flexibility, or soft relation, can be very beneficial as it provides a range of overlapping options that can be utilized in situations that require the schedule to be optimally accelerated. This paper thus proposes a formalization of a generic logical relationship (hard or soft) of any type (finish-to-start, etc.) between any two activities. Using the generic representation, modified activity start and finish time computations are presented to accommodate the overlaps associated with soft relations, and are used in a schedule-crashing model. The model determines the optimum combination of activity crashing and overlapping decisions that minimizes project cost without violating the resource constraints. The new activity relationship and crashing model are beneficial to both researchers and practitioners as they facilitate schedule optimization decisions for projects that exercise a good level of overlapping, such as fast-track projects. [ABSTRACT FROM AUTHOR]

Time-cost trade-offs arise when organizations seek the fastest product development (PD) process subject to a predefined budget, or the lowest-cost PD process within a given project deadline. Most of the engineering and project management literature has addressed this trade-off problem solely in terms of crashing-options to trade cost for time at the individual activity level-and using acyclical networks. Previously (Meier et al. in IEEE Trans Eng Manag 62(2):237-255, 2015), we presented a rich model of the iterative (cyclical) PD process that accounts for crashing, overlapping, and stochastic activity durations and iterations. In this paper, we (1) propose an optimization strategy for the model based on a multi-objective evolutionary algorithm, called ε-MOEA, which identifies the Pareto set of best time-cost trade-off solutions, and (2) demonstrate the approach using an automotive case study. We find that, in addition to crashing, activity overlapping, process architecture, and work policy provide further managerial levers for addressing the time-cost trade-off problem. In particular, managerial work policies guide process cost and duration into particular subsets of the Pareto-optimal solutions. No work policy appeared to be superior to the others in both the cost and duration dimensions; instead, a time-cost trade-off arises due to the choice of work policy. We conclude that it is essential for managers to consider all of the key factors in combination when planning and executing PD projects. [ABSTRACT FROM AUTHOR]

This paper presents a design of dual-band microstrip patch antennas with low volume using the genetic algorithm. This type of antennas provides a practical interest for the ever growing wireless communication. The shape of a typical rectangular patch is modified in order to produce dual resonance frequencies keeping the physical volume of the antenna constant. Indeed, the initial patch is divided into 10x10 small uniform rectangles (Pixel), and the genetic algorithm searches, the optimal configuration for the desired goal. To validate the procedure, an antenna prototype has been fabricated and tested with the FR4 substrate. The measurements results were in good agreement with simulation ones. [ABSTRACT FROM AUTHOR]

We consider the spectrum sharing problem between a set of device-to-device (D2D) links and multiple co-located cellular networks. Each cellular network is controlled by an operator which can provide service to a number of subscribers. Each D2D link can either access a sub-band occupied by a cellular subscriber or obtain an empty sub-band for its exclusive use. We introduce a new spectrum sharing mode for D2D communications in cellular networks by allowing two or more D2D links with exclusive use of sub-bands to share their sub-bands with each other without consulting the operators. We establish a new game theoretic model called Bayesian non-transferable utility overlapping coalition formation (BOCF) game. We show that our proposed game can be used to model and analyze the above spectrum sharing problem. However, we observe that the core of the BOCF game can be empty, and we derive a sufficient condition for which the core is non-empty. We propose a hierarchical matching algorithm which can detect whether the sufficient condition is satisfied and, if it is satisfied, achieve a stable and unique matching structure which coincides with the overlapping coalition agreement profile in the core of the BOCF game. [ABSTRACT FROM PUBLISHER]

Compressing project schedule using activity accelerating and overlapping requires that an intensive time-cost trade-off analysis be carried out, to determine costs and benefits for each day of compression. However, the cost elements and implications of compression techniques differ significantly, since activity accelerating imposes extra direct cost whereas activity overlapping adds a risk of changes and rework. Such a trade-off becomes even more complicated in capital projects comprised of a large number of schedule activities and relationships. The variety of combinations of accelerating and overlapping of different activities in these complex networks can offer numerous possibilities for compression with various costs and potential risks. The lack of a reliable analytical tool for performing a precise cost-benefit analysis causes this critical task to be performed in a subjective manner during the planning stage of projects. The purpose of this paper is to present an advanced method using a multi-objective evolutionary optimization tool seeking the optimum degree of accelerating and overlapping during the schedule compression process. This optimization technique would be beneficial in maximizing project benefits while meeting the intended target dates. [ABSTRACT FROM AUTHOR]

We suggest new directions for research on the effectiveness of concurrent product development. These models relate overlapping or functional integration to performance using analytical, simulation, or statistical methods. By collecting quantitative data from 82 research articles, we ascertain the percentage of a methodology's models that have incorporated each of certain significant theoretical features. We then use this information to recommend important underutilized features and a comprehensive theory that integrates the features. Analytical and simulation modelers could make more use of functional integration, while statistical modelers could attend more to overlapping, rework, and the constituent phases of the development process. Then, in finding a subset of interrelated features appearing simultaneously in a majority of a methodology's models, we identify a core theory upon which models in the mainstream build. Next, our study offers specific mutual learning suggestions through which modeling efforts in any one methodology might facilitate modeling efforts in another methodology. Analytical and simulation modelers could study the reluctance to transmit and use preliminary information, while statistical researchers could develop dynamic models. Finally, the study provides a template for researchers willing to apply our evaluation methods to other areas. [ABSTRACT FROM PUBLISHER]

Overlapping between development activities stemming from concurrent engineering may not only reduce duration of a project but also create rework risk. This article presents a process modeling and analysis technique for managing complex product development projects using optimization and simulation techniques. In order to analyze the forced overlapped process with lead–risk, we propose evolution and sensitivity design structure matrix for reflecting overlapping and their impact on the degree of activity sensitivity and evolution in the process model. Using the design structure matrix, we present the schedule and cost model associated with iteration, overlapping, and rework by incorporating many more general characteristics of complex design processes. The model can be used for better project planning and control by identifying overlapping and risk for process improvements, and for evaluating alternative planning and execution strategies. An industrial example, a tactics aerocraft preliminary development design process at a company, is provided to illustrate the utility of the model using optimization and discrete-event simulation. The optimization model maximizes the compressed time associated with overlapping and yields several managerial insights, including optimization analysis of project activity sequence, how overlapping and rework risk impact on project schedule and cost, trading off schedule and cost risk. [ABSTRACT FROM PUBLISHER]