A New Algorithm for the Discrete Shortest Path Problem in a Network Based on Ideal Fuzzy Sets

A shortest path problem is a practical issue in networks for real-world situations. This paper addresses the fuzzy shortest path (FSP) problem to obtain the best fuzzy path among fuzzy paths sets. For this purpose, a new efficient algorithm is introduced based on a new definition of ideal fuzzy sets (IFSs) in order to determine the fuzzy shortest path. Moreover, this algorithm is developed for a fuzzy network problem including three criteria, namely time, cost and quality risk. Several numerical examples are provided and experimental results are then compared against the fuzzy minimum algorithm with reference to the multi-labeling algorithm based on the similarity degree in order to demonstrate the suitability of the proposed algorithm. The computational results and statistical analyses indicate that the proposed algorithm performs well compared to the fuzzy minimum algorithm

A Fuzzy Decision-Making Methodology for Risk Response Planning in Large-Scale Projects

Risk response planning is one of the main phases in the project risk management and has major impacts on the success of a large-scale project. Since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. The conventional approaches tend to be less effective in dealing with the impreciseness of risk response planning. This paper presents a new decision-making methodology in a fuzzy environment to evaluate and select the appropriate responses for project risks. To this end, two fuzzy well-known decision-making techniques, namely, decision tree and TOPSIS (technique for order preference by similarity to ideal solution), are extended based on multiple selected criteria, simplifying parameterized metric distance and fuzzy similarity measure. Finally, a case study in an oil and gas project in Iran is provided to show the suitability of the proposed fuzzy methodology in large-scale practical situations

A mathematical modeling approach for high and new technology-project portfolio selection under uncertain environments

High and new technology-project as a tool to achieve productive forces through scientific and technological knowledge is characterized as knowledge based with high risk and returns. Often conflicting objectives of these projects have complicated their assessment and selection process. This paper offers a novel approach of high technology-project portfolio selection in two main parts. In the first part, a new risk reduction compromise decision-making model is proposed that applies a new approach in determining the weights of experts and in avoiding information loss. The objective function of a new interval type-2 fuzzy sets (IT2Fs) based mathematical model of project portfolio selection is formed by the outcome. To depict model’s applicability, data from case study of high technology-project selection in the literature is used to present the efficacy of the model

Determining project characteristics and critical path by a new approach based on modified NWRT method and risk assessment under an interval type-2 fuzzy environment

In this paper with respect to the importance of risks in real-world projects and ability of interval type-2 fuzzy sets (IT2FSs) to tackle the uncertainty, a new approach is introduced to consider risks and the correlation among risk factors by subjective judgments of experts on the probability and impact under IT2FSs. Furthermore, a new impact function for considering the correlation among the risk factors are extended under an IT2F environment. Moreover, a new subtraction operator is introduced for the critical path analysis. The node-weighted rooted tree (NWRT) method is modified based on the proposed new operator to avoid producing negative number for characteristics of each activity. Also, in order to cope with the uncertainty of the projects, NWRT method is developed under the IT2FSs. Eventually, to illustrate the validity and capability of the proposed method, two examples from the literature are solved and compared

Time Prediction Using a Neuro-Fuzzy Model for Projects in the Construction Industry

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is intended for use by members of the project team in performing the time control of projects in the construction industry. The present paper addresses the effects of different factors on the project time and schedule by using both fuzzy sets theory (FST) and artificial neural networks (ANNs) in a construction project in Iran. The construction project is investigated to demonstrate the use and capabilities of the proposed model to see how it allows users and experts to actively interact and, consequently, make use of their own experience and knowledge in the estimation process. The proposed model is also compared to the well-known intelligent model (i.e., BPNN) to illustrate its performance in the construction industry

Project portfolio selection problems: a review of models, uncertainty approaches, solution techniques, and case studies

Project portfolio selection has been the focus of many scholars in the last two decades. The number of studies on the strategic process has significantly increased over the past decade. Despite this increasing trend, previous studies have not been yet critically evaluated. This paper, therefore, aims to presents a comprehensive review of project portfolio selection and optimization studies focusing on the evaluation criteria, selection approach, solution approach, uncertainty modeling, and applications. This study reviews more than 140 papers on project portfolio selection research topic to identify the gaps and to present future trends. The findings show that not only the financial criteria but also social and environmental aspects of project portfolios have been focused by researchers in project portfolio selection in recent years. In addition, meta-heuristics and heuristics approach to finding the solution of mathematical models have been the critical research by scholars. Expert systems, artificial intelligence, and big data science have not been considered in project portfolio selection in the previous studies. In future, researchers can investigate the role of sustainability, resiliency, foreign investment, and exchange rates in project portfolio selection studies, and they can focus on artificial intelligence environments using big data and fuzzy stochastic optimization techniques

Solving construction project selection problem by a new uncertain weighting and ranking based on compromise solution with linear assignment approach

Selecting a suitable construction project is a significant issue for contractors to decrease their costs. In real cases, the imprecise and uncertain information lead to decisions made based on vagueness.  Fuzzy sets theory could help decision makers (DMs) to address incomplete information. However, this article develops a new integrated multi-criteria group decision-making model based on compromise solution and linear assignment approaches with interval-valued intuitionistic fuzzy sets (IVIFSs). IVIFSs by presenting a membership and non-membership degree for each candidate based on appraisement criteria could decrease the vagueness of selection decisions. The proposed algorithm involves a new decision process under uncertain conditions to determine the importance of criteria and DMs, separately. In this regard, no subjective or additional information is needed for this process; only the input information required is an alternative assessment matric. In this approach, weights of criteria and DMs are specified based on novel indexes to increase the reliability of obtained results. In this respect, the criteria’ weights are computed regarding entropy concepts. The basis for calculating the weight of each DM is the distance between each DM and an average of the DMs’ community. Furthermore, the linear assignment model is extended to rank the candidates. A case study about the construction project selection problem (CPSP) is illustrated to indicate the application of proposed model

Sustainable infrastructure project selection by a new group decision-making framework introducing MORAS method in an interval type 2 fuzzy environment

Project management is a process that is involved with making important decisions under uncertainty. In project management often the existing data is limited and vague. Sustainable project selection has a multi-criteria evaluation nature which calls for attending to various often conflicting factors under vagueness. To deal with sustainable project selection several important factors should be properly considered. In this paper, in order to provide a new multi-criteria project selection method, a novel last aggregation method is presented. This method has several main novelties. First, to address uncertainty interval type 2 fuzzy sets (IT2FSs) are used. Second, the importance of criteria is investigated by using IT2F entropy. Third, a novel index for decision making is presented that has the merits of ratio system in MOORA and COPRAS, named MORAS. Fourth, the weights of decision makers are computed according to the obtained judgments and the weights are employed to aggregate the results. Fifth, the defuzzification is carried out in the last step of the process by means of a new IT2F ranking method. To present the applicability of the method, it is used in an existing case study in the literature and the outcomes are presented

A Neural Network Model Based on Support Vector Machine for Conceptual Cost Estimation in Construction Projects

Estimation of the conceptual costs in construction projects can be regarded as an important issue in feasibility studies. This estimation has a major impact on the success of construction projects. Indeed, this estimation supports the required information that can be employed in cost management and budgeting of these projects. The purpose of this paper is to introduce an intelligent model to improve the conceptual costaccuracy during the early phases of the life cycle of projects in construction industry. A computationally efficient model, namely support vector machine model, is developed to estimate the conceptual costs of construction projects. The proposed neural network model is trained by a cross validation technique in order to produce the reliable estimations. To demonstrate the performance of the proposed model, twopowerful intelligent techniques, namely nonlinear regression and back-propagation neural networks (BPNNs), are provided. Their results are compared on the basis of the available dataset from the related literature in construction industry. The computational results illustrate that the presented intelligent model performs better than the other two powerful techniques

Integrated product-process design: Material and manufacturing process selection for additive manufacturing using multi-criteria decision making

Market dynamics of today are constantly evolving in the presence of emerging technologies such as Additive Manufacturing (AM). Drivers such as mass customization strategies, high part-complexity needs, shorter prod- uct development cycles, a large pool of materials to choose from, abundant manufacturing processes, diverse streams of applications (e.g. aerospace, motor vehicles, and health care) and high cost incurred due to manufac- turability of the part have made it essential to choose the right compromise of materials, manufacturing processes and associated machines in early stages of design considering the Design for Additive Manufacturing guidelines. There exists a complex relationship between AM products and their process data. However, the literature to-date shows very less studies targeting this integration. As several criteria, material attributes and process function- ality requirements are involved for decision making in the industries, this paper introduces a generic decision methodology, based on multi-criteria decision-making tools, that will not only provide a set of compromised AM materials, processes and machines but will also act as a guideline for designers to achieve a strong foothold in the AM industry by providing practical solutions containing design oriented and feasible material-machine com- binations from a current database of 38 renowned AM vendors in the world. An industrial case study, related to aerospace, has also been tested in detail via the proposed methodology