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.

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.

Uncertainty Theory Based Novel Multi-Objective Optimization Technique Using Embedding Theorem with Application to R &D Project Portfolio Selection

This paper introduces a novice solution methodology for multi-objective optimization problems having the coefficients in the form of uncertain variables. The embedding theorem, which establishes that the set of uncertain variables can be embedded into the Banach space C[0, 1] × C[0, 1] isometrically and isomorphically, is developed. Based on this embedding theorem, each objective with uncertain coefficients can be transformed into two objectives with crisp coefficients. The solution of the original m-objectives optimization problem with uncertain coefficients will be obtained by solving the corresponding 2 m-objectives crisp optimization problem. The R & D project portfolio decision deals with future events and opportunities, much of the information required to make portfolio decisions is uncertain. Here parameters like outcome, risk, and cost are considered as uncertain variables and an uncertain bi-objective optimization problem with some useful constraints is developed. The corresponding crisp tetra-objective optimization model is then developed by embedding theorem. The feasibility and effectiveness of the proposed method is verified by a real case study with the consideration that the uncertain variables are triangular in nature.

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.

Tabu Search Solution for Resource Confidence Considered Partner Selection Problem in Cross-Enterprise Project

Cross-enterprise project is the main implementation form in multi enterprises collaborative production environment. Minimizing the risk of failure and tardiness caused by the uncertainty of partner’s resources in partner selection is the key problem to ensure success in Cross-enterprise project. In this paper, considering the factors and constraints of sub-project processing times, precedence of sub-project and project due date, especially the resource confidence, a 0-1 integer programming model was presented with the objective to minimize the risk of failure and the tardiness of the project. A project scheduling algorithm was designed to search and evaluate selection solutions, and the project scheduling algorithm was embedded into a Tabu search algorithm to solve the model. Simulation experiments and comparisons with other algorithms showed that the proposed approach was possible to find the optimal solution with a faster speed and higher probability.

Dominance-Based Rough Set Approach in Selection of Portfolio of Sustainable Development Projects

In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.

Expanded models of the project portfolio selection problem with learning effect

This research develops two new models for project portfolio selection, in which the candidate projects are composed of multiple repetitive units. To reflect some real situations, the learning effect is considered in the project portfolio selection problem for the first time. The mathematical representations of the relationship between learning experience and investment cost are provided. One numerical example under different scenarios is demonstrated and the impact of considering learning effect is then discussed.

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.

Fuzzy Assessment Method and Its Application to Selecting Project Managers

Open competition is a new form of the assessment of candidates and selection of project managers. This has many merits compared to the traditional administrative method of appointment. This article introduces a method of fuzzy assessment of project manager candidates. Fuzzy assessment unifies objective qualitative and quantitative appraisal and can be used for improving decision-making in the selection process.

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.

A Genetic Fuzzy Analytical Hierarchy Process Based Projection Pursuit Method for Selecting Schemes of Water Transportation Projects

The optimal selection of schemes of water transportation projects is a process of choosing a relatively optimal scheme from a number of schemes of water transportation programming and management projects, which is of importance in both theory and practice in water resource systems engineering. In order to achieve consistency and eliminate the dimensions of fuzzy qualitative and fuzzy quantitative evaluation indexes, to determine the weights of the indexes objectively, and to increase the differences among the comprehensive evaluation index values of water transportation project schemes, a projection pursuit method, named FPRM-PP for short, was developed in this work for selecting the optimal water transportation project scheme based on the fuzzy preference relation matrix. The research results show that FPRM-PP is intuitive and practical, the correction range of the fuzzy preference relation matrix [WTHX]A[WT] it produces is relatively small, and the result obtained is both stable and accurate; therefore FPRM-PP can be widely used in the optimal selection of different multi-factor decision-making schemes.

CLUSTERING VIA DIMENSIONAL REDUCTION METHOD FOR THE PROJECTION PURSUIT BASED ON THE ICSA

The performance of the classical clustering algorithm is not always satisfied with the high-dimensional datasets, which make clustering method limited in many application. To solve this problem, clustering method with Projection Pursuit dimension reduction based on Immune Clonal Selection Algorithm (ICSA-PP) is proposed in this paper. Projection pursuit strategy can maintain consistent Euclidean distances between points in the low-dimensional embeddings where the ICSA is used to search optimizing projection direction. The proposed algorithm can converge quickly with less iteration to reduce dimension of some high-dimensional datasets, and in which space, K-mean clustering algorithm is used to partition the reduced data. The experiment results on UCI data show that the presented method can search quicker to optimize projection direction than Genetic Algorithm (GA) and it has better clustering results compared with traditional linear dimension reduction method for Principle Component Analysis (PCA).

Selecting the Six Sigma Project: A Multi Data Envelopment Analysis Unified Scoring Framework

The importance of the project selection phase in any six sigma initiative cannot be emphasized enough. The successfulness of the six sigma initiative is affected by successful project selection. Recently, Data Envelopment Analysis (DEA) has been proposed as a six sigma project selection tool. However, there exist a number of different DEA formulations which may affect the selection process and the wining project being selected. This work initially applies nine different DEA formulations to several case studies and concludes that different DEA formulations select different wining projects. Also in this work, a Multi-DEA Unified Scoring Framework is proposed to overcome this problem. This framework is applied to several case studies and proved to successfully select the six sigma project with the best performance. The framework is also successful in filtering out some of the projects that have “selective” excellent performance, i.e. projects with excellent performance in some of the DEA formulations and worse performance in others. It is also successful in selecting stable projects;these are projects that perform well in the majority of the DEA formulations, even if it has not been selected as a wining project by any of the DEA formulations.

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.

LIRR "R&D and Industrialization of High PerformanceFunctional Refractories for Metallurgy New Technology" Project Gained Fund Support

Checked by Henan Province Development and Relbrm Commission and Henan Province MunicipalFinance （）ffice, the “R ＆ I） and Industrialization of High Performance Functional Refractories for Metallurgy New Technology” project declared by 1,1RR gained a special fund support on self-renovation and products structure adjustment. The project will realize the technology integration and industriali-zation of key refractories including purging component, metering nozzle and toil and belt continuous casting nozzle, etc. through building high performance functional refractories production lines for metallurgy new technology.

Discussion on construction and type selection of China high dams

At the beginning of 21st century, with the rapid and steady development of China economy, a lot of large scale hydropower projects with large dams from 200 m to 300 m high are being or to be built. China dam constructions are reaching the level of 300 m high arch dam, 250 high CFRD (concrete face rockfill dam) and 200 m high RCC (roller compacted concrete) gravity dam. Due to the safety and the economy, the type selection for high dams has become the key issue during the argumentation for the hydropower projects, and further efforts are still needed in this aspect for high dams. After reviewing the high dam constructions in China and abroad, authors proposed some advices for the selection of dam types, and hope that it can provide some helpful information for the researches and the design of high dams.

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,

A BRIEF INTRODUCTION TO R&D PROJECTS WINNING SPECIAL-GRADE AND FIRST-GRADE PRIZES AT THE 1994 CAS AWARDS FOR S&T PROGRESS

Following a thorough examination and appraisal by the Awards jury and after ratification by the CAS President’s Working Conference, a total of 15 R&D projects were granted special-grade prizes and 13 first-grade prizesrespectively at the 1994 CAS Awards for S&T Progress.A brief introduction of them is given as follows: