SELECTED CURRENT AND RECENT PROJECTS

Centre National d’Art et de Culture Georges Pompidou, Paris, 1971-77 Insurance Market and Headquarters Building, Lloyds of London, 1978-86 Inmos Microprocessor Factory, Newport, South Wales,

Agricultural Investment Project Decisions Based on an Interactive Preference Disaggregation Model Considering Inconsistency

Agricultural investment project selection is a complex multi-criteria decision-making problem,as agricultural projects are easily influenced by various risk factors,and the evaluation information provided by decisionmakers usually involves uncertainty and inconsistency.Existing literature primarily employed direct preference elicitation methods to address such issues,necessitating a great cognitive effort on the part of decision-makers during evaluation,specifically,determining the weights of criteria.In this study,we propose an indirect preference elicitation method,known as a preference disaggregation method,to learn decision-maker preference models fromdecision examples.To enhance evaluation ease,decision-makers merely need to compare pairs of alternatives with which they are familiar,also known as reference alternatives.Probabilistic linguistic preference relations are employed to account for the presence of incomplete and uncertain information in such pairwise comparisons.To address the inconsistency among a group of decision-makers,we develop a pair of 0-1mixed integer programming models that consider both the semantics of linguistic terms and the belief degrees of decision-makers.Finally,we conduct a case study and comparative analysis.Results reveal the effectiveness of the proposed model in solving agricultural investment project selection problems with uncertain and inconsistent decision information.

Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding

Due to its outstanding ability in processing large quantity and high-dimensional data,machine learning models have been used in many cases,such as pattern recognition,classification,spam filtering,data mining and forecasting.As an outstanding machine learning algorithm,K-Nearest Neighbor(KNN)has been widely used in different situations,yet in selecting qualified applicants for winning a funding is almost new.The major problem lies in how to accurately determine the importance of attributes.In this paper,we propose a Feature-weighted Gradient Decent K-Nearest Neighbor(FGDKNN)method to classify funding applicants in to two types:approved ones or not approved ones.The FGDKNN is based on a gradient decent learning algorithm to update weight.It updates the weight of labels by minimizing error ratio iteratively,so that the importance of attributes can be described better.We investigate the performance of FGDKNN with Beijing Innofund.The results show that FGDKNN performs about 23%,20%,18%,15%better than KNN,SVM,DT and ANN,respectively.Moreover,the FGDKNN has fast convergence time under different training scales,and has good performance under different settings.

ZTE Selected by BelTelecom for GPON National Broadband Network Project

ZTE Corporation announced on May 30, 2011 that it had won BelTelecom＇s national broadband network project in Belarus.

Multi-objective optimization in highway pavement maintenance and rehabilitation project selection and scheduling:A state-of-the-art review

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.

Scenario-based approach for project portfolio selection in army engineering and manufacturing development

The decisions concerning portfolio selection for army engineering and manufacturing development projects determine the benefit of those projects to the country concerned.Projects are typically selected based on ex ante estimates of future return values,which are usually difficult to specify or only generated after project launch.A scenario-based approach is presented here to address the problem of selecting a project portfolio under incomplete scenario information and interdependency constraints.In the first stage,the relevant dominance concepts of scenario analysis are studied to handle the incomplete information.Then,a scenario-based programming approach is proposed to handle the interdependencies to obtain the projects,whose return values are multi-criteria with interval data.Finally,an illustrative example of army engineering and manufacturing development shows the feasibility and advantages of the scenario-based multi-objective programming approach.

Application Study of Climatic Feasibility Demonstration of Airport Project Site Selection

According to the Technical Guide for Climatic Feasibility Demonstration of Airport Project Site Selection,via statistical analysis on historical climate data of reference weather station,climatic background characteristics and meteorological disaster situation of preselected site,and characteristics of seasonal distribution,interannual variation and long-term trend of main meteorological elements and disasters were summarized.The difference of meteorological elements between preselected site and reference weather station and its cause were analyzed,and fine analysis of fog,low visibility event,wind,cloud and other meteorological elements or weather with high influence in preselected site was conducted.Climatic feasibility,advantages and disadvantages of meteorological conditions and possible meteorological risks in preselected site were evaluated.The research could provide decision-making basis for site selection of Lingling Airport and airport engineering design.Moreover,key technology route and research results were extracted,and technical analysis process of demonstration report was integrated,which could provide reference for climate feasibility demonstration on site selection of similar airports in China.

ANTEL Selects ZTE for Uruguay National GPON Project

On September 19,2011, ZTE Corporation, a publicly-listed global provider of telecommunications equipment and network solutions, announced it has been selected by the Administracion Nacional de Telecomunicaciones （ANTEL） to assist in providing 300,000 subscribers in the country with a gigabit passive optical network （GPON）. The tender was one of the largest ever put out in South America.

A Decision Support Model and Procedure for Selecting Interrelated R&D Projects

A decision support model with stochastic multiobjective functions and constraints and its solution procedure are presented for selecting R&D projects, in which the uncertainty of project evaluation and selection, the interactions of technique, resource and benefit among projects, and the experience, knowledge and preference of R&D managers are considered. The statistical results of stochastic factors representing the benefit contributions and the three kinds of interactions corresponding to each objective among projects are obtained by the Delphi method and QS/NI decision process for developing the model. In the decision analysis solution procedure, experiences and preferences of R&D managers to make decisions are considered by means of aspiration goal levels(AGL), probabilities to reach AGL called satisfied degrees, and probabilities to satisfy the corresponding constraints called feasible degrees, under which a project selection plan can be proposed by the decision analyst or computer. The utility of this model and solution procedure are demonstrated by a real world case example in R&D program of strength and vibration of aircraft turbine engines. which involves 23 project candidates. The evalutions of benefit contributions and interactions of projects are made by 41 experts in the field.

A comparative analysis of text representation, classification and clustering methods over real project proposals

Purpose-When a large number of project proposals are evaluated to alocate available funds,grouping them based on their simiarites is benefciaL.Current approaches to group proposals are primarily based on manual matching of similar topics,discipline areas and keywordls declared by project applicants.When the number of proposals increases,this task becomes complex and requires excessive time.This paper aims to demonstrate how to ffctively use the rich information in the titles and abstracts of Turkish project propsals to group them atmaially.Design/methodology/approach-This study proposes a model that effectively groups Turkish project proposals by combining word embedding,clustering and classification technigues.The proposed model uses FastText,BERT and term frequency/inverse document frequency(TF/IDF)word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish.The extracted terms were grouped using both the clustering and classification techniques.Natural groups contained within the corpus were discovered using k-means,k-means++,k-medoids and agglomerative clustering algorithms,Additionally,this study employs classification approaches to predict the target class for each document in the corpus.To classify project proposals,var ious classifiers,including k nearest neighbors(KNN),support vector machines(SVM),artificial neural networks(ANN),cassftcation and regression trees(CART)and random forest(RF),are used.Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.Findings-The results show that the generated word embeddings an fftvely represent proposal texts as vectors,and can be used as inputs for dustering or casificatiomn algorithms.Using clustering algorithms,the document corpus is divided into five groups.In adition,the results demonstrate that the proposals can easily be categoried into predefmned categories using cassifiation algorithms.SVM-Linear achieved the highest prediction accuracy(89.2%)with the FastText word embedding.method.A comparison of mamual grouping with automatic casification and clutering results revealed that both classification and custering techniques have a high sucess rate.Research limitations/implications-The propsed mdelatomatically benefits fromthe rich information in project proposals and significantly reduces numerous time consuming tasks that managers must perform manually.Thus,it eliminates the drawbacks of the curent manual methods and yields significantly more acurate results.In the future,additional experiments should be conducted to validate the proposed method using data from other funding organizations.Originality/value-This study presents the application of word embedding methods to eftively use the rich information in the titles and abstracts of Turkish project proposals.Existing research studies focus on the automatice grouping of proposals;traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals.Unlike previous research,this study employs two outperforming neural network-based textual feature extraction techniques to obtain termns representing the proposals:BERT as a contextual word embedding method and F astText as a static word embedding method.Moreover,to the best of our knowledge,there has been no research conducted on the grouping of project proposals in Turkish.

AN EXTENDED MODEL FOR PROJECT PORTFOLIO SELECTION WITH PROJECT DIVISIBILITY AND INTERDEPENDENCY

In this paper, we develop an extended model for the project portfolio selection problem over a planning horizon with multiple time periods. The model incorporates the factors of project divisibility and interdependency at the same time for real-life applications. The project divisibility is considered as a strategy, not an unfortunate event as in the literature, in choosing the best execution schedule for the projects, and the classical concept of＂project interdependencies＂ among fully executed projects is then extended to the portions of executed projects. Additional constraints of reinvestment consideration, setup cost, cardinality restriction, precedence relationship and scheduling are also included in the model. For efficient computations, an equivalent mixed integer linear programming representation of the proposed model is derived. Numerical examples under four scenarios are presented to highlight the characteristics of the proposed model. In particular, the positive effects of project divisibility are shown for the first time.

The Research and Application of the Strategic Analysisand Selection of R&D Project

On the basis of important role of science and technology in economic development, thisarticle systematically studies and discusses strategic evaluation techniques and methods of R&D projectfrom management sector’s angles. Firstly, it summarizes the existing R&D project evaluation methods.Then the ’three step’ method of R&D project evaluation is presented, that is (1) the benefit and successprobability of R&D project are evaluated based on the fuzzy influence diagram, (2) the portfolio analysisin the structure of R&D project is studied. (3) ’0-1’ model is used for project selection. The methodcan combine qualitative analysis with quantitative calculation, and describe the relationship betweenfactors that influence project’s result, thus improve the decision level of R&D project evaluation andselection. Finally, by means of practical application, the effectiveness and feasibility of the method areverified.

Allocation Planning for R&D with Uncertainty:Model and Procedure

A decision support model with the stochastic objective function measuring the benefit contributions of projects and stochastic resource constraint and its decision making analysis procedure with the case study are presented for selecting R&D projects, in which the uncertainty in project evaluation and selection, the technical or outcome, cost or resource and benefit or payoff interrelationships among projects, and the experience and knowledge of the R&D manager can be considered.