Project Selection Problem Research Articles

Project selection with partially verifiable information

We study a principal–agent project selection problem with asymmetric information. The principal must choose exactly one of N projects, each defined by the utility it provides to the principal and to the agent. The agent knows all the utilities, and the principal can commit to a mechanism (without transfers) that maps the agent’s report about the utilities to a chosen project. Unlike the typical literature, which assumes the agent can lie arbitrarily, we examine the principal’s problem under partial verifiability constraints. We characterize the class of truthful mechanisms under a family of partial verifiability constraints and study the principal’s problem for the specific cases of no-overselling and no-underselling. Our results suggest significant benefits for the principal from identifying or inducing such partial verifiability constraints, while also highlighting the simple mechanisms that perform well.

Network-level pavement maintenance and rehabilitation planning considering uncertainties using chance-constrained programming

ABSTRACT In practice, given limited funds, to consider multiple strategic goals/objectives that different stakeholders concern, pavement network-level maintenance and rehabilitation (M&R) planning becomes a multi-objective optimisation (MOO) based project selection and budget allocation problem. In an attempt to solve this problem, most agencies established MOO models under the deterministic situation without appropriate consideration of uncertainties. However, ignoring performance uncertainties often leads to unreasonable decisions. To provide more convincing and reliable pavement M&R decisions, this paper proposes a Chance-Constrained Programming (CCP) based MOO method to incorporate performance uncertainties in network-level single period pavement M&R planning. First, a general deterministic MOO model with budget and network performance constraints is established. Then, three commonly-used statistical forms of network-level performance measures are introduced. To incorporate uncertainties, the probability distribution of each form of performance measure is derived. Based on the CCP method, the MOO model is transformed to an equivalent deterministic formulation as a mixed non-linear integer programming (MNLIP) problem. To demonstrate the proposed method, a case study using real data is conducted. The results show that the proposed method can effectively help decision-makers to appropriately incorporate performance uncertainties in conducting network-level pavement M&R planning.

Uncertain multi-period project adjustment and selection under the carbon tax and carbon quota policies

To achieve the carbon neutrality goal, enterprises should consider not only the development of new low-carbon emission projects but also the adjustment of the existing high-carbon emission projects. This paper discusses a multi-period project adjustment and selection (MPPAS) problem under the carbon tax and carbon quota policies. First, we propose an uncertain mean-chance MPPAS model for maximizing the profit of the project portfolio under the carbon tax and carbon quota policies. Then, we provide the deterministic equivalent of the proposed model and conduct the theoretical analysis of the impact of carbon tax and carbon quota policies. Next, we propose an improved adaptive genetic algorithm to solve the proposed model. Finally, we give numerical experiments to verify the proposed algorithm’s performance and show the proposed model’s applicability. Research has shown that the government can achieve the carbon neutrality goal by determining reasonable carbon tax and carbon quota policies, and companies can make the optimal investment decisions for the project portfolio by the proposed model. In addition, the proposed algorithm has good performances in robustness, convergence speed, and global convergence.

An Intuitionistic Fuzzy Decision Support for Agile Project Selection Problem

In the early 1990s, organizations started to use information technology and software engineering, which encouraged the companies for employing project management methodologies to survive and then advance in competitive technologic environment. Use of project management methodologies provides efficient planning, budgeting and scheduling processes and high management quality for companies. A lot of different methodologies were developed and used to reach better ways of defining the project requirements, analyzing the problem, and implementing it in a systematic manner. In 2001, agile project management methodology came forward in response to cope with waterfall project methodology’s limitations that arise from unpredictability of technology evolution, customer requirements, and unstable business environments. In this study, intuitionistic fuzzy COPRAS method, which aims to obtain a solution relative to the ideal solution, is used to rank agile project alternatives and identify the best performing one among them. Intuitionistic fuzzy sets are used to deal with loss of information and hesitation in data that may occur in operations with fuzzy numbers. The application of the proposed intuitionistic fuzzy decision-making approach is illustrated by conducting a case study.

Investment Project Selection with Multi Criteria Decision Making Techniques in FMCG Industry

In today's our modern world, where rivalry is increasing from day to day and technological developments are advancing very fast, decision-making processes are becoming more difficult. Especially for companies, investment projects are a critical decision-making problem due to their huge budgets and their impact on the company. Therefore, making decisions by utilizing scientific methods is of the essence for companies. Related in the thesis, it is determined to select the most optimum project for the company to invest in a leading company in the FMCG sector based on a project selection problem with 5 main criteria, 8 sub-criteria and 5 alternatives. In problem solving, AHP, which is the most widely utilized method for weighting criteria in the literature, and TOPSIS, VIKOR, which are the most typically used methods in project selection in the literature, and ARAS method, which is rarely used, were used to address the project selection problem. Since ARAS method studies are impreplance in the literature, it is purposed to fill gap in this thesis is supported by Fuzzy AHP and Fuzzy TOPSIS procedures. In the thesis, it was make firm that the ARAS method, which is scarce used in the literature, works in harmony with the widely used AHP, TOPSIS and VIKOR procedures. With this work; It is forecast that companies can use these mathematical methods to enable decision making in cases involving many criteria and alternatives. This application in the FMCG sector, where project selection decisions are frequently made, has brought a academic approach to investment project selection problems. The use of AHP, BAHP in project criteria weighting; TOPSIS, Fuzzy TOPSIS, VIKOR and ARAS methodologies in project selection has make a help to the literature and proved that the methods work in concord with one together. It is estimated that these mathematical modeling can be used in particular decision-making trouble of the company.

Hybrid Approach to Supply Chain Project Manager Selection Problem

Supply chain management covers the management of all processes, from determining the material needs of manufacturing companies to delivering value-added products to customers. Theoretical approaches and practical applications for supply chain management processes are presented in the literature. However, there is no project-based supply chain manager selection for the improvement of supply chain processes yet. In this research, the supply chain project manager selection problem is discussed. In this context, multi criteria decision making techniques used for project manager selection problem applications were used. According to the literature review and interviews with the manufacturing executive jury, seven criteria were determined. The criterion weights were determined by the fuzzy the stepwise weight assessment ratio analysis (F-SWARA) method. The grey operational competitiveness rating method (OCRA-G) method was applied to select the most suitable candidate among the four candidates. According to the research findings, the experience criterion was determined as the most important criterion in the selection of the supply chain project manager. The second candidate was found to be the most suitable candidate. In this research, an alternative hybrid model method for the project selection problem has been presented to the literature by using fuzzy and gray based approaches in a hybrid way. In addition, suggestions have been developed for manufacturing companies, supply chain project manager candidates and researchers.

A multi-objective mean–semivariance model for project selection using reinvestment and synergy under uncertainty

Project selection problems with imprecise parameters is one of the hot spots that have attracted many scholars’ interest. In this paper, we first propose a multi-objective mean–semivariance model to solve the multi-objective project selection problem considering reinvestment and synergy between projects with different investment and operation periods by applying the uncertainty theory. The investment outlay and profit are treated as uncertain variables with an uncertainty distribution function that are determined based on experts’ evaluations. The objectives are to maximize the expected value of uncertain net present value (NPV) and to minimize its risk (semivariance). The difference between the models taking variance and semivariance as the risk of NPV is compared, and the effects of reinvestment and synergy on project selection are analyzed. We next propose new binary versions of Jaya and Rao algorithms and develop multi-objective binary meta-heuristic algorithms binMOJaya, binMORao1 and binMORao2 to solve the proposed model. The performances of the proposed three algorithms are demonstrated through comparison with other well-known binary multi-objective algorithms for the 10 problems including large-scale problems. Finally, the validity of proposed model is illustrated through an example problem using the zigzag uncertainty distribution. Computational experiments have shown that the proposed uncertain model explains reinvestment and synergy well, and that the proposed algorithms, especially the binMORao2 algorithm, are very suitable for solving the proposed model.

A Fuzzy Approach for Generalized Project Selection and Scheduling Problem with Resource Management

In this study, the problem of project selection and scheduling with resource management is considered project setup times, dynamic project arrivals, priorities and relationship between projects. A fuzzy multi-objective mixed integer linear programming (MILP) model is proposed for the solution of the problem. The classic two-phase fuzzy goal programming (FGP) approach is modified to solve the proposed multi-objective MILP model. The addressed problem is defined over the project selection and scheduling problem of a construction company. The effect of resource management on the project selection and scheduling problem is demonstrated over the generated test problems. Modified two-phase FGP and classic two-phase FGP approaches are compared over test problems. With the use of the modified two-phase FGP approach, additional alternative solutions are found for the problem.

A Fuzzy Multicriteria Group Decision Making Approach for Evaluating and Selecting Fintech Projects

The use of financial technologies (Fintech) has increased recently due to their support to financial institutions in managing their financial operations and achieving competitive advantages. Even though there are several benefits with Fintech development and implementation, selecting the most suitable Fintech project can be complex. This is due to the involvement of numerous decision makers, the conflicting nature of multiple evaluation criteria, and fuzzy data derived from imprecise judgments of qualitative performance ratings. Interval-valued based intuitionistic fuzzy numbers are used to deal with the inherent vagueness and imprecision of the evaluation process. An algorithm based on an interval-valued intuitionistic fuzzy weighted geometric (IIFWG) and the concept of ideal solutions is developed. As a result, the most suitable Fintech project alternative can be selected across all evaluation criteria. To demonstrate the effectiveness of the approach, a Fintech project selection problem is presented.

RESEARCH AND DEVELOPMENT PROJECT SELECTION: A COMPREHENSIVE ANALYSIS OF THE TRENDS AND METHODS

The purpose of this paper is to present a comprehensive literature review of the research and development (R&D) project selection problem since 1977, using the PRISMA method. This study aims (i) to support researchers and practitioners to understand selection methodologies, decision-maker types, and perspectives; (ii) to guide them in choosing the most suitable project selection method; and (iii) to clarify a common confusion by proposing a more precise definition of ‘ R&D project selection ’ and ‘ R&D portfolio selection ’ . The study also highlights several areas for improvement and the future of project selection techniques for further studies.

The Road to Prosperity: A Study on the Efficiency of Resource Allocation in China’s Expressway Construction

This paper presents a new multi-objective mixed integer programming model to prioritize seven highway infrastructure projects. The proposed model explicitly incorporates socio-economics, uncertainty, resource constraints, and interdependencies to deal with the optimal transportation infrastructure project selection problem. Next, project prioritization is determined based on the ANP results, which reflect the interdependency between criteria and a set of feasible candidate projects. Finally, an empirical study of eastwest highway improvement program is conducted to illustrate how the modeling process proceeds. The results show that the proposed model can effectively support a transportation investment policy that best suits the goal of urban land redevelopment for highly urbanized settings. As such, the model can be generalized for other human settlement studies and illustrate the spatially complex characteristics of transportation networks as well as some of the difficulties raised with respect to priority of national capital improvement projects. The research will provide a useful reference for the study of the relationship between highways and regional economic development.

An integrated multicriteria decision‐making approach for distribution system expansion planning

The project selection problem in an electric distribution utility, which is part of the network expansion planning task, requires a formal process of prioritizing some projects since the budget is limited. A complete solution to this problem, involving optimization and a decision-making model, is still a few explored in the literature. We proposed an integrated multicriteria decision-making methodology, including optimization and solutions ranking. The optimization module finds a set of Pareto-optimal portfolios, using the multi-objective genetic algorithm nondominated sorting genetic algorithm-II to select and schedule the reinforcement projects in a multistage planning horizon. The proposed decision support module ranks the project portfolios based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Simple Multi-Attribute Rating Technique (SMART), considering the decision-maker profile embedded into the model by the Rank-Order Centroid (ROC) and Analytic Hierarchy Process (AHP) criteria weights. This paper tackles this methodology's theoretical foundations, taking into account some essential attributes, such as power quality and operational performance indexes, the number of consumers, and projects' financial impacts. We presented realistic case studies to show how portfolios are composed, not only when all nine attributes of projects are included in the optimization module, but also when the criteria weights are changed in the decision support module. This paper's main contribution lies in proposing a unique model to find optimal expansion plans and rank these project portfolios, considering the decision-maker profile adherent to its goals. About the decision-making methods applied in this problem, we infer that the TOPSIS performance overcomes SMART in all analyzed problems and the ROC and AHP weights indicate the same alternative in most of the analyzed cases.

Probabilistic reliable linguistic term sets applied to investment project selection with the gained and lost dominance score method

Probabilistic linguistic term sets (PLTSs) are an effective tool in keeping with the habits of decision makers (DMs). However, in multi-criteria group decision making (MCGDM) problems, it is necessary to deal with the information reliability problem because of the difference of the DMs’ knowledge backgrounds and knowledge structures. Therefore, this paper proposes a novel concept called probabilistic reliable linguistic term sets. Based on which, some basic operations, comparison laws, distance measures, similarity measures and aggregation operators are defined. After that, we propose the probabilistic reliable linguistic gained and lost dominance score method to cope with MCGDM problems, and we further apply the proposed method to solve an investment project selection problem about lucky bag machine. Finally, we make some comparative analyses to verify the effectiveness and highlight the strength of the proposed method compared with four methods, i.e., the aggregation-based method, the TOPSIS method under probabilistic reliable linguistic environment, the gained and lost dominance score (GLDS) method with probabilistic linguistic information and the GLDS method with hesitant linguistic information.

Integrating an Extended Outranking‐TOPSIS Method with Probabilistic Linguistic Term Sets for Multiattribute Group Decision‐Making

Group decision‐making is a common activity in organizational management and economic conditions. In practice, the opinions of experts may be fuzzy. This paper proposes integrating an extended outranking‐TOPSIS method with probabilistic linguistic term sets for multiattribute group decision‐making, which is used to solve the real‐world public‐private partnership (PPP) project selection problem. First, an extended outranking method based on probabilistic linguistic term sets is proposed, and each expert’s ranking of alternatives is obtained according to this method. After the individual ranking is completed, the large‐scale expert group is clustered by the K‐means clustering method, and then the improved consensus mechanism is used to study the degree of consensus of the expert group. If the consensus of the group is not up to the standard, then, for clusters with a lower degree of consensus with the group, the feedback mechanism is used to adjust the weight between different clusters so that the group consensus can be improved. After achieving the target group consensus, an improved technique for order preference by similarity to an ideal solution (TOPSIS) method is used to synthesize expert opinions, and the ranking results are obtained. Finally, there are cases used to demonstrate the feasibility and rationality of the method.

APPLICABLE CHARACTERISTICS OF PASSENGER TRANSPORTATION OF TOURISTS INCLUSIVE TYPE

Goal. Research of ways to increase the adaptive capabilities of the organization by improving the mechanism of adaptive management of project management decisions on the adoption, rejection or revision of the project based on determining the degree of consistency / differences of strategic and project goals of the organization. Method. The following methods were used for the study: methods of project management, formalization, modeling, analysis, expert method. Results. An approach to determining the relevance of organizational goals and projects to ensure adaptation has been developed. Scientific novelty. The calculation of determining the degree of relevance between the goals and strategy of the organization and the project to ensure adaptation is proposed and substantiated. Practical significance. The proposed approach can be used to solve project selection problems for businesses and organizations and allows you to identify the relationship between project objectives and business goals and identify relevant goals.

Methodology for analyzing the trade‐offs associated with multi‐objective optimization in transportation asset management under uncertainty

AbstractTrade‐off analysis, one of the key tenets of multi‐objective optimization for the project selection problem in Transportation Asset Management (TAM), can help decision‐makers quantify and comprehend the consequences of different resource allocations in terms of the multiple measures of system performance. In analyzing TAM trade‐offs, it is vital to account duly for the uncertainties associated with these system‐wide performance measures. In this paper, we present a methodology that integrates chance‐constraint programming, the Lindeberg Central Limit Theorem, and a hybrid NSGA II method, to address the performance uncertainties associated with the TAM multi‐objective optimization problem. Through analyzing the trade‐offs between expenditure and performance, and between different performance measures, we generate Pareto frontiers at different confidence levels using a hybrid NSGA II method. We demonstrate the proposed methodology using a case study involving real‐life assets and the expected cost and performance benefits of projects associated with these assets. Regarding the trade‐off between cost and performance, we determine the extent to which the strengths of these relationships vary across different confidence levels. We find that, generally, for a given network performance level, a higher expenditure is needed to achieve a high confidence level compared to the expenditure needed to achieve a low confidence level, and more importantly, measures these sensitivities. This is the “Price of Confidence” concept. Regarding the trade‐off between different pairs of performance measures under budgetary constraints, we show how these relationships vary with the confidence level specified for the analysis, and we measure the extent to which higher confidence level requirements translate into lower levels of overall system‐wide performance.

Condorcet Voting Theory based on Analytical Hierarchy Process (AHP) for Project Selection Problems

Condorcet Voting Theory based on Analytical Hierarchy Process (AHP) for Project Selection Problems

Evaluation and selection of construction projects based on risk analysis

The project selection problem has attracted significant attention among construction project participants. Typically, a contractor can execute one or more projects from available projects. This study introduces a new strategy to help contractors in evaluating and selecting suitable construction projects. The new strategy is based on identifying multiple criteria that affect construction projects as well as developing and using a model that supports decision based on the analytic hierarchy process. Furthermore, a fuzzy risk analysis model is proposed to analyze and quantify some of the identified criteria to help contractors select minimally risky projects. Market conditions, financial resources, project conditions, time delays, and cost overruns are five identified criteria that control the selection. Through identifying 70 subsidiary factors, the effects and importance of the first three criteria were determined through a field survey, while time delay and cost overrun criteria were analyzed and quantified through the risk analysis model. The new strategy was applied on a real case study comprising two HVAC system projects in the construction industry (project 1 in Egypt and project 2 in Saudi Arabia). Criteria and causes that yield different effects on the new strategy to support contractor decision and selection control are discussed in detail. Risk factors were ranked according to their importance, and their effects on both time and cost were clarified. The results obtained from the investigated case study showed 55.5% support for project 2, and only 44.5% support for project 1. This strategy is applicable to other case studies in different countries with different projects, criteria, and execution activities.

A comprehensive mathematical model for resource-constrained multi-objective project portfolio selection and scheduling considering sustainability and projects splitting

In the portfolio project selection problem with budgetary and resource constraints, it is frequently assumed that projects cannot be interrupted and can be merely scheduled and processed in consecutive time periods in which resources have been sufficiently allocated. Due to improvement in consuming resources and budgets, this study addresses a linear multi-objective for sustainable project portfolio selection problem that allows projects to be split at any time and postponed to be re-executed at another time. Furthermore, due to the importance of sustainability in projects, the aim of this model is selecting and scheduling projects according to criteria of the sustainability balanced scorecard, time-dependent criteria, resource, and budget constraints. For that reason, a hybrid multi-criteria decision-making approach is considered for ranking, scoring and calculating the sustainable utility of projects. Then, the proposed model is interpreted as a multi-objective optimization where the total benefits and the value of utility for projects are maximized, and the summation of the absolute amount of interruption in executing the selected projects is minimized. In addition to the constraints of budget and resources, and considering the conflicting projects, a novel mathematical formulation is presented to employ the dependency relationship between some projects in both processes of selecting and scheduling the projects. In order to validate this model, several numerical case studies are provided and solved by the ε-constraint method under various circumstances, including un-splitting projects with unlimited resources, constrained resources, splitting projects and interpretation of multi-objective problem. Finally, their results are analyzed and investigated. The results demonstrate that more benefits will be obtained when projects have permission to be split. However, increasing the sustainability of projects and reducing the risk of interruption should not be overlooked, which is why they have been explored as the objective functions in other numerical case studies.

A hybrid DEMATEL-FRACTAL method of handling dependent evidences

Dempster combination rule in evidence theory is widely used in data fusion system. One assumption of Dempster combination rule is the independence among different evidences. However, it is difficult to satisfy this requirement due to many influencing factors. The main contribution of this paper is to propose a systematic model to deal with dependence evidences in evidence theory. The core of the model can be divided into two parts: handling inner dependence and handling outer dependence. For the inner dependence, we use DEMATEL model to establish the relationships between different components in the system and get their relative weights considering their influences. For the outer dependence, we use BPAs inherent fractals features to deal with the uncertainty in the outer environment as well as the dependence among each collected evidence. After that we combine these two aspects of dependence and make decisions. By discounting evidences we avoid redundant calculation thus obtain useful information as much as possible. A case study of transportation project selection problem is used to illustrate our proposed method.