Multiobjective water resources investment planning under budgetaryand socio-technical uncertainties

An integration of stochastic dynamic programming (SDP) and integer goal programming (IGP) modeling framework is proposed to handle problems of multiobjective-multicriteria sequential decision making under budgetary and socio-technical uncertainties inherent in water resources investment planning. In the proposed SDP model, probabilities of the funding levels in any time period that are generated using a subjective model are employed to handle budgetary fluctuations. This subjective model consists of historical data as a basic rate, functional relationships among inter-related parameters of the SDP model, scenarios of future budget availability, and subjective inputs elicited from a group of decision makers through a collective opinion technique. Application of the SDP model primarily yields an optimal investment planning policy that recognizes the possibility that actual funding received maybe less than that anticipated, and therefore the projects being implemented under the anticipated budget would be interrupted. In this integrated model, economic return of each level of investment decision together with its associated project portfolio is determined by the IGP model based on goals and criteria preferences with explicit consideration to socio-technical uncertainty     

Directly performance-constrained template-based layout retargeting and optimization for analog integrated circuits

Due to intrinsic intricacy, layout parasitics exhibit a significant impact on the performance of analog integrated circuits. In this paper a directly performance-constrained parasitic-aware automatic layout retargeting and optimization algorithm is presented. Unlike the conventional sensitivity analysis, a general central-difference based scheme using any simulator for sensitivity computation is deployed. We propose a piecewise sensitivity model to enforce more accurate sensitivity computation during parasitic optimization. Moreover, mixed-integer performance constraints due to parasitics are included in the formulated mixed integer nonlinear programming problem rather than through either indirect parasitic-bound constraints or inaccurate worst-case sensitivities. A graph technique and mixed-integer nonlinear programming are effectively combined to solve the formulated parasitic optimization problem. The automatically generated target layouts can satisfy performance constraints to ensure the desired specifications. The experimental results show that the proposed algorithm can achieve effective retargeting of analog circuits with less layout area and significant reduction in execution time.     

Project scheduling conflict identification and resolution using genetic algorithms (GA)

Project management has gained a lot of application in software development activity in the past two decades. It is now considered to be one of the most critical component of software development lifecycle. Project management is traditionally defined as the discipline of planning, organizing, and managing activities and resources for successful execution and completion of project goals and objectives. In this respect, project management holds a key position in satisfactory completion of projects. That is the reason that we have a complete knowledge domain we know as software project management (SPM). The main purpose of SPM is to achieve all the project goals and objectives while working within the constraints posed by project environment and stakeholders. These constraints include (but not limited to) time, scope, resources, resource allocation and optimization etc. Successful project planning involved careful selection and synchronization of resources in order to achieve satisfactory completion of projects. These resources include human resource, rime, infrastructure etc. While planning software projects, it is natural to be confronted with various conflicts in resource allocation. It becomes a very time consuming activity to identify and sort out these conflicts when project size is large and time constraints are severe. A good project management activity is one which can effectively foresee these conflicts and resolve them in an optimal fashion. Computationally intelligent techniques are a good candidate to be used for the purpose of automation of this task. In this paper, a genetic algorithm based technique for conflict identification and resolution for project activities has been proposed. The effectiveness and utility of such a technique has also been discussed in this paper. The technique has been subjected to extensive experimentation and results have been presented.     

A Multi-Objective Software Quality Classification Model Using Genetic Programming

A key factor in the success of a software project is achieving the best-possible software reliability within the allotted time & budget. Classification models which provide a risk-based software quality prediction, such as fault-prone & not fault-prone, are effective in providing a focused software quality assurance endeavor. However, their usefulness largely depends on whether all the predicted fault-prone modules can be inspected or improved by the allocated software quality-improvement resources, and on the project-specific costs of misclassifications. Therefore, a practical goal of calibrating classification models is to lower the expected cost of misclassification while providing a cost-effective use of the available software quality-improvement resources. This paper presents a genetic programming-based decision tree model which facilitates a multi-objective optimization in the context of the software quality classification problem. The first objective is to minimize the "Modified Expected Cost of Misclassification", which is our recently proposed goal-oriented measure for selecting & evaluating classification models. The second objective is to optimize the number of predicted fault-prone modules such that it is equal to the number of modules which can be inspected by the allocated resources. Some commonly used classification techniques, such as logistic regression, decision trees, and analogy-based reasoning, are not suited for directly optimizing multi-objective criteria. In contrast, genetic programming is particularly suited for the multi-objective optimization problem. An empirical case study of a real-world industrial software system demonstrates the promising results, and the usefulness of the proposed model     

Impact of budget uncertainty on project time-cost tradeoff

Any project has to be supported financially. The budget allocated to the project, however, is subject to uncertainty due to various financial, market, and political risks. The present paper incorporates budget uncertainty into project time-cost tradeoff. The proposed model formulates financial feasibility as a stochastic constraint, transforms it into a deterministic equivalent in the case of normal, beta, or triangular distribution, and solves the equivalent accordingly. The direct result is a minimum time-cost curve, which relates the shortest project duration to different levels of budget. The present study shows that a higher degree of budget uncertainty represents a tighter financial constraint and, thus, needs extra contingency duration. Moreover, if the financial constraint has to be met at a higher probability level, extra contingency costs are necessary to ensure an on-time completion. An actual remodeling project is used to demonstrate the application.     

Exploring Genetic Programming and Boosting Techniques to Model Software Reliability

Software reliability models are used to estimate the probability that a software fails at a given time. They are fundamental to plan test activities, and to ensure the quality of the software being developed. Each project has a different reliability growth behavior, and although several different models have been proposed to estimate the reliability growth, none has proven to perform well considering different project characteristics. Because of this, some authors have introduced the use of Machine Learning techniques, such as neural networks, to obtain software reliability models. Neural network-based models, however, are not easily interpreted, and other techniques could be explored. In this paper, we explore an approach based on genetic programming, and also propose the use of boosting techniques to improve performance. We conduct experiments with reliability models based on time, and on test coverage. The obtained results show some advantages of the introduced approach. The models adapt better to the reliability curve, and can be used in projects with different characteristics.     

Fast Heuristics for Designing Integrated E-Waste Reverse Logistics Networks

This paper investigates a mixed-integer linear programming model that solves an integrated facility location and configuration problem for recycling electronic waste (e-waste). Since different recycled e-waste consume different levels of recycling processes and resources, the capability of processing only one or more categories of recycled e-waste for each candidate facility is considered in addition to its location to maximize the total revenue. Computational experiments based on proposed heuristics are conducted using data collected from Taiwan's recycled e-waste market and show our proposed methods give a high-quality near-optimal solution in a promising time shorter than previous solution methods and CPLEX.     

Downlink scheduling optimization in CDMA networks

The problem of allocating radio resources in the downlink of a code-division multiple access (CDMA) network is studied. The modulation and coding schemes, numbers of multicodes, and transmit powers used for all mobile stations (MS) are jointly chosen so as to maximize the total transmission bit rate, subject to certain constraints. Based on the discrete and nonlinear nature of the proposed model, a mixed-integer nonlinear programming optimization problem is formulated. It is shown that the optimal allocation generally involves simultaneous transmissions to several MS. A scheduler which uses knowledge of MS traffic loads is also proposed and shown to yield a significant improvement in throughput.     

Rescuing Troubled Software Projects by Team Transformation: A Case Study With an ERP Project

Many software projects fail, whether failure is measured in terms of budget, schedule, or some other requirement. The causes of such failures are many, but are not always easily recognized. This is not the least due to the human dimension of corporate activities, as spurious or misdiagnosed issues in Enterprise Resource Planning (ERP) projects can take on a life of their own and become a magnet for company politics. This paper reports an industrial case in which the senior management attempted to deal with a troubled ERP implementation (SAP R/3) in an international fast moving consumer goods (FMCG) company during 2001 and 2002. This paper reflects this dimension as it uses original emails and PowerPoint slides to recount a number of representative episodes in a troubled but ultimately successful project. At the heart of this success is the realization that whereas it can be difficult and time-consuming to do root-cause analyses, it is relatively simple to identify problem owners. In this case, the senior management without IT backgrounds turned around a failing project by reorganizing the team structure according to process areas so that issues in each process area had one problem owner. We summarize the management's actions into a troubleshooting framework, and in addition, suggest three actions for rescuing troubled projects: keep the project manager but narrow down the manager's scope of responsibility to one or two process areas; assign the right people to be responsible for other process areas; and have the General Manager chair the ERP meetings.     

Calculation of contingency in construction projects

This paper presents a probabilistic model that considers the random nature of change orders and their impact on the cost and schedule of a construction project. The model incorporates uncertainties in project cost and schedule and calculates the contingency based on the level of confidence specified by the owner. It assumes a Poisson arrival pattern for change orders in a construction project. The effect of schedule delays in further increasing the project cost is considered explicitly and the effect of correlation between change orders is discussed and analytically considered. The proposed model may be used for budgeting purposes at the early stages of project development. Typical applications of the proposed model in the context of construction industry are discussed. A numerical example is presented that demonstrates the use of the model in establishing a budget contingency. The results of the example are compared with actual data from previous construction projects.     

A scheduling approach for design activities in concurrentengineering

This paper presents an optimization scheduling approach for concurrent design projects, in which activities may be executed in more than one operating mode and renewable as well as nonrenewable resources exist. Research on the development of a scheduling approach for concurrent scheduling is expected to shorten development lead time, minimize cost, and eliminate unnecessary redesign periods. In this paper, an integrated criterion function is proposed to ensure optimal concurrent scheduling and effective utilization of resources along with fluent delivery of information. In the criterion function, some key factors such as time order, resources, lead time and overlapping time of activities, which can make concurrent activities execute successfully, are taken into account adequately. Besides, two cruxes in concurrent engineering-role allocation, prerelease, and feedback revision process are discussed in detail. The example is part of a certain product development process, and the scheduling results demonstrate that the proposed algorithm is feasible     

Optimal design for software reliability and development cost

A process for reliability-related quality programming is developed to fill existing gaps in software design and development so that a quality programming plan can be achieved. The tradeoffs among system reliability improvement, resource consumption, and other relevant constraints through the management phase are investigated. A software reliability-to-cost relation is developed both from a software reliability-related cost model and from software redundancy models with common-cause failures. A generic N-component redundancy model is also developed. The software reliability optimization problems can be formulated into a mixed-integer programming problem     

Optimal use of budget reserves to minimize technical and management failure risks during complex project development

Project managers are recognizing that adequate resource reserves are a critical success factor in a project development environment that is complex and uncertain. Yet, justifying the need for project reserves is still a challenge, as is the optimal allocation of any available resources to minimize development uncertainties. This paper presents a multiperiod decision model designed to support the management of reserves considering the risks of failures including technical, managerial, i.e., exceeding budget and schedule, or strategic, i.e., meeting budget, schedule, and technical specifications but not achieving the full strategic value of the project. In this paper, we examine the tradeoffs among these risks and their implications for resource allocation during a project's development phase. This decision support model is referred to as Dynamic Advanced Probabilistic Risk Analysis Model. It provides decision makers with a quantitative tool to allocate reserves (beyond the bare-bone minimum project costs) among project reserves, technical reinforcements of the engineered system, and product enhancements, with the advantage of flexibility over time. The model yields first, coarse estimates of the value of deferring some commitments about the product's design until critical uncertainties are resolved and second, an estimate of the optimal amount to be invested in testing and reviews. We show that the greater the uncertainties at the onset of the development phase, the greater the value of this information.     

多模态生物识别中生物特征组合的最优化设计

如何在预算一定的情况下挑选出最佳的生物特征组合,通常是多模态生物特征识别技术中首先需要解决的问题。该文运用最优化技术中的“多目标规划”与“0-1线性整数规划”方法建立出一套简洁、实用的数学模型,并给出运用MATLAB的具体求解方法。经试验数据对比证明,该文提出的方法能在一定约束条件下快速、有效的找到满足用户要求的最优的多模态生物特征组合方案。     

R&D/marketing communication during the fuzzy front-end

The planning stage of an innovation project has a great effect on the commercial performance of the project. During the “fuzzy front-end”, the organization formulates a concept of the product to be developed, and determines whether or not the organization will invest resources in the concrete development of the idea. The integration of R&D and marketing activities is a necessary condition for success in innovation projects. The research question of this study is: from an information processing perspective, what role does information transfer play in integrating R&D and marketing functions during the planning stage, and what effects do project formalization and project centralization of R&D and marketing planning activities have on the efficiency of marketing and technological uncertainty reduction? The authors use an ex post facto research design to test the propositional model. The findings show that successful project teams are characterized by a maximum uncertainty reduction during planning, i.e., by a maximum decrease of R&D and marketing task variability, and a maximum increase of R&D and marketing task analyzability. Information flows between these functions help them to achieve this efficient uncertainty reduction. While project centralization has a negative effect on efficient uncertainty reduction, project formalization is curvilinearly related to the amount of uncertainty reduced during planning     

Orchestration mechanism for VNF hardware acceleration resources in SDN/NFV architecture

The hardware acceleration mechanism for VNF (virtual network function) is recently a hot research topic in SDN/NFV architecture because of the low processing performance of VNF.Once hardware acceleration resources have been plugged into the network,how to optimally mange and orchestrate these resources under service requirements remains a question to be solved.Firstly,a unified management architecture based on separated control for hardware acceleration resources was proposed.Then,traditional network topology together with hardware acceleration resources were modeled into a unified network model and then the hardware acceleration resource orchestration problem was transferred into a multi-objective linear programming problem.Finally,a hardware-accelerator-card-prior’ heuristic algorithm was designed.Experimental results show that compared with existing methods,the proposed orchestration mechanism can efficiently manage hardware acceleration resources and reduce the processing latency by 30%.     

GitHub中基于CNN-LSTM的开发者项目推荐模型

作为一个开源项目托管平台,GitHub以多开发者协同参与进行开源项目的开发,开发者作为GitHub的核心元素,保证了整个系统的活跃性,然而,很多新项目在短时间内无法找到合适的协同开发者而被拖延开发周期.针对这个问题,本文提出了一种基于Word2Vec的CNN-LSTM开发者项目推荐模型,该模型以Word2Vec训练开发者访问项目的序列,并将项目进行向量化表示,结合CNN-LSTM模型计算项目相似度并为开发者推荐合适的项目序列.通过提取GitHub中62,031个开发者在2015全年的项目访问数据进行项目预测和相似项目发现实验,实验结果表明,该模型推荐效果较佳,并且可以帮助开发者发现感兴趣的相似项目.     

Joint R&D Projects as Complex Systems: A Framework of Analysis

This paper proposes a model to analyze joint R&D projects from a systemic approach. We argue that joint R&D projects are an economic and organizational reality that can be studied through their two principal aspects: from the perspective of the process developed to fulfill the objectives of the project and from the point of view of the network of partners constituted to undertake the project. We define the joint R&D project as a sociotechnical system in interaction with its environment and explain it through three subsystems: technological, structural, and governance. Using a sample of 202 European institutions involved in a wide range of joint R&D projects, this study provides empirical evidence to explain joint R&D projects through the separate study of each subsystem and their characteristic variables. Our results show interrelatedness of subsystems and the factors that determine the structure and governance of joint projects, including practical implications that may be useful for their running and management.      

Self-Organizing Migrating Strategies Applied to Reliability-Redundancy Optimization of Systems

The reliability-redundancy allocation problem is a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, mixed-integer non-linear programming, heuristics, and meta-heuristics. Meanwhile, the development of meta-heuristics has been an active research area in optimizing system reliability wherein the redundancy, the component reliability, or both are to be determined. In recent years, a broad class of stochastic algorithms, such as simulated annealing, evolutionary computation, and swarm intelligence algorithms, has been developed for reliability-redundancy optimization of systems. Recently, a new class of stochastic optimization algorithm called SOMA (Self-Organizing Migrating Algorithm) has emerged. SOMA works on a population of potential solutions called specimen, and is based on the self-organizing behavior of groups of individuals in a "social environment". This paper introduces a modified SOMA approach based on a Gaussian operator to solve reliability-redundancy optimization problems. In this context, three examples of mixed integer programming in reliability-redundancy design problems are evaluated. In this application domain, SOMA was found to outperform the previously best-known solutions available.     

Estimating factors affecting project task size in product development-an empirical study

This paper addresses the methodology used to determine the amount of human resources needed to develop products. It is based on an empirical study of five firms in different industries. The methods rely on technical experts operating within budget constraints. The specific methods vary from relatively ad-hoc approaches to database-driven, centralized, and validated approaches. This variation is largely correlated with project and overall product development organization scale in a logical fashion in that larger projects and organizations move away from individual decision-makers toward centralized, objective, and validated methods. The project characteristics used by the experts and implicitly utilized in the database models to assess project complexity are also detailed in this paper. These characteristics are categorized based on a project complexity framework. We found that the scale of a project and the amount of "stretch" are the two most widely used characteristics for estimating project complexity. We found no utilization of the level of either component or task interactions in estimating project complexity by the firms studied. We also found no empirical evidence for interactions being a determinant of project difficulty despite a fair amount of theoretical concern with their importance in project complexity. In addition, we analyzed the accuracy of methods and conclude that since the accuracy at a higher level masks resource allocation problems at a lower level, the accuracy should be followed at multiple levels.