In sustainable supply chain networks, companies are obligated to have a systematic decision support system in place to help it adopt right decisions at right times.Among strategic decisions, supplier selection and evaluation outranks other decisions in terms of importance due to its long-term impacts.Besides, the adoption of such strategic decision entails exploring several factors that contribute to the complexity of decision making in the supply chain.For the purpose of solving non-linear regression problems, a novel neural network technique known as least squaresupport vector machine (LS-SVM) with maximum generalization ability has successfully been implemented.However, the performance quality of the LS-SVM is recognized to notoriously vary depending on the rigorous selection of its parameters.Therefore, in this paper, a continuous general variable neighborhood search (CGVNS) which is an effective meta-heuristic algorithm to solve the real world engineering continuous optimization problems is proposed to be integrated with LS-SVM.The CGVNS is hybridized in our novel integrated LS-SVM and CGVNS model, to tune the parameters of the LS-SVM to better estimate performance rating of supplier selection and evaluation problem.To demonstrate the improved performance of our proposed integrated model, a real data set from a case study of a supplier selection and evaluation problem is presented in a cosmetics industry.Additionally, comparative evaluations between our proposed model and the conventional techniques, namely nonlinear regression, multi-layer perceptron (MLP) neural network and LS-SVM is provided.The experimental results simply manifest the outperformance of our proposed model in terms of estimation accuracy and effective prediction.

Selecting the most suitable robot among their wide range of specifications and capabilities is an important issue to perform the hazardous and repetitive jobs. Companies should take into consideration powerful group decision-making (GDM) methods to evaluate the candidates or potential robots versus the selected attributes (criteria). In this study, a new GDM method is proposed by utilizing the complex proportional assessment method under interval-valued hesitant fuzzy (IVHF)-environment. In the proposed method, a group of experts is established to evaluate the candidates or alternatives among the conflicted attributes. In addition, experts assign their preferences and judgments about the rating of alternatives and the relative importance of each attribute by linguistic terms which are converted to interval-valued hesitant fuzzy elements (IVHFEs). Also, the attributes weights and experts weights are applied in procedure of the proposed interval-valued hesitant fuzzy group decision-making (IVHF-GDM) method. Hence, the experts opinions about the relative importance of each attribute are considered in determination of attributes weights. Thus, we propose a hybrid maximizing deviation method under uncertainty. Finally, an illustrative example is presented to show the feasibility of the proposed IVHF-GDM method and also the obtained ranking results are compared with a recent method from the literature.

The real-world economic conditions have inevitably forced many companies to pursue outsourcing as a suitable long term planning tool to reduce operating costs and improve their competitiveness in different marketplaces. One of the critical activities for outsourcing success is the outsourcing provider selection, which may be regarded as a type of multi-criteria decision making (MCDM) problem. In this study, we propose a multiple-criteria group decision making model under interval-valued intuitionistic fuzzy environment to select the best outsourcing provider. First, an IVIF-weighted geometric averaging (IVIFWGA) operator is employed to aggregate all individual IVIF-decision matrices provided by a group of experts into a collective IVIF-decision matrix. Then, a new version of ELECTRE method in an IVIF environment by novel indexes is proposed for the evaluation process in terms of insufficient and inaccurate information. Finally, to demonstrate its usefulness, an application example for evaluating of outsourcing providers is given from the recent literature.

Selecting the best project in construction industry is a complex decision problem in which numerous conflicting factors should be considered for the assessment. Among well-known models in the soft computing field, artificial intelligence (AI) can be suggested to yield better predictions than traditional methods. For this purpose, this paper introduces an effective All model based on new neural networks and fuzzy logic to improve the decisions for projects owners. A computationally AI model, namely locally linear neuro-fuzzy (LLNF), is proposed to precisely predict the overall performance of projects in construction industry. Proposed model can be properly employed for the long term prediction of performance data in construction industry. Finally, the model is applied to facilitate the assessment process in a real case study. To demonstrate the applicability of the proposed AI model, the computational results in terms of the performance and accuracy are compared to two widely-used regression methods.

Effective project selection necessitates considering numerous conflicting factors for the decision making in construction industry. Multiple factors, such as resource requirements, budget control, technological implications and governmental regulations, influence the decision to select an appropriate project selection in construction industry. Among the recent methods and models, an artificial intelligence can be recommended to achieve higher performance than traditional methods in the field. This paper introduces an effective artificial intelligence (AI) model based on modern neural networks to improve the decision making for the projects owners. A hybrid AI model based on least squares support vector machine and cross validation technique is proposed to predict the overall performance of construction projects. The presented model can be successfully utilized for long-term estimation of the performance data in construction industry. Finally, the proposed model is implemented in a real case study for construction projects. To illustrate the capabilities of the proposed model, two well-known AI models, known as back propagation neural network and radial basis function neural network, are taken into consideration. The comparisons demonstrate the superiority of the presented model in terms of its performance and accuracy through the real-world prediction problem.

This paper presents a novel multiple attribute group decision-making (MAGDM) model based on the compromise ratio method under an interval-valued intuitionistic fuzzy (IVIF) environment. The compromise ratio method under uncertainty is introduced by a group of experts based on the concept that the chosen alternative should be as close as possible to the IVIF-positive-ideal point and as far away from the IVIF-negative-ideal point as possible concurrently. First, an IVIF-weighted geometric averaging (IVIFWGA) operator is employed to aggregate all individual IVIF-decision matrices provided by a group of experts into a collective IVIF-decision matrix. Two new basic IVIF-operations are introduced to handle the evaluation process. Then, an extended collective index in an IVIF environment is proposed to discriminate among alternatives for the evaluation process in terms of subjective and objective information. Finally, to demonstrate the suitability and applicability of the proposed IVIF-MAGDM model, an application example of reservoir flood control operation is given from the recent literature.

The location of multiple cross-docking centers (CDCs) and vehicle routing scheduling are two crucial choices to be made in strategic/tactical and operational decision levels for logistics companies. The choices lead to more realistic problem under uncertainty by covering the decision levels in cross-docking distribution networks. This paper introduces two novel deterministic mixed-integer linear programming (MILP) models that are integrated for the location of CDCs and the scheduling of vehicle routing problem with multiple CDCs. Moreover, this paper proposes a hybrid fuzzy possibilistic–stochastic programming solution approach in attempting to incorporate two kinds of uncertainties into mathematical programming models. The proposed solving approach can explicitly tackle uncertainties and complexities by transforming the mathematical model with uncertain information into a deterministic model. m′ imprecise constraints are converted into 2Rm′ precise inclusive constraints that agree with Rα-cut levels, along with the concept of feasibility degree in the objective functions based on expected interval and expected value of fuzzy numbers. Finally, several test problems are generated to appraise the applicability and suitability of the proposed new two-phase MILP model that is solved by the developed hybrid solution approach involving a variety of uncertainties and complexities.

The purpose of this paper is to design a new extension of the ELECTRE, known as the elimination and choice translating reality method, for multi-criteria group decision-making problems based on intuitionistic fuzzy sets. This method is widely utilized when a set of alternatives should be identified and evaluated with respect to a set of conflicting criteria by reflecting decision makers’ (DMs’) preferences. However, handling the exact data and numerical measure is difficult to be precisely focused because the DMs’ judgments are often vague in real-life decision problems and applications. A more realistic and practical approach can be to use linguistic variables expressed in intuitionistic fuzzy numbers instead of numerical data to model DMs’ judgments and to describe the inputs in the ELECTRE method. The proposed intuitionsitic fuzzy ELECTRE utilizes the truth-membership function and non-truth-membership function to indicate the degrees of satisfiability and non-satisfiability of each alternative with respect to each criterion and the relative importance of each criterion, respectively. Then, a new discordance intuitionistic index is introduced, which is extended from the concept of the fuzzy distance measure. Outranking relations are defined by pairwise comparisons and a decision graph is depicted to determine which alternative is preferable, incomparable or indifferent in the intuitionistic fuzzy environment. Finally, a comprehensive sensitivity analysis is employed to further study regarding the impact of threshold values on the final evaluation, and a comparative analysis is demonstrated with an application example in flexible manufacturing systems between the proposed ELECTRE method and the existing intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS) method.

This paper considers a construction project problem under multiple criteria in a fuzzy environment and proposes a new two-phase group decision making (GDM) approach. This approach integrates a modified analytic network process (ANP) and an improved compromise ranking method, known as VIKOR. To take uncertainty and risk into account, a new decision making approach is presented with multiple fuzzy information by a group of experts, and a risk attitude for each expert is incorporated that can be expressed linguistically. First, a modified fuzzy ANP method is introduced to address the problem of dependence as well as feedback among conflicting criteria and to determine their relative importance. Then, a fuzzy VIKOR method is extended to rank potential projects on the basis of their overall performance. An illustrative example from the literature is provided for the construction project problem to demonstrate the effectiveness and feasibility of the proposed approach. The computational results show that the proposed two-phase GDM approach is suitable to cope with imprecision and subjectivity for the complicated decision making problem. Finally, the associated results of the proposed approach with risk attitudes and without risk attitudes are compared with the results reported by Cheng and Li [1], and the merits are highlighted.

This paper presents a novel compromise solution method for solving fuzzy group decision-making problems by a group of experts, which can determine the best alternative by considering both conflicting quantitative and qualitative evaluation criteria in real-life applications. The compromise solution method is developed based on the concept that the chosen alternative should be as close as possible to the positive ideal solution and as far away from the negative ideal solution as possible concurrently. The performance rating values of alternatives versus conflicting criteria as well as the weights of criteria are described by linguistic variables with multi-judges and are converted to triangular fuzzy numbers. Then, a new collective index is introduced to distinguish among potential alternatives in the assessment process with respect to subjective judgment and objective information. Finally, a real case study and an application example for a contractor selection problem are provided in construction industry to demonstrate the implementation process of the proposed method.

Time estimation in new product development (NPD) projects is often a complex problem due to its nonlinearity and the small quantity of data patterns. Support vector regression (SVR) based on statistical learning theory is introduced as a new neural network technique with maximum generalization ability. The SVR has been utilized to solve nonlinear regression problems successfully. However, the applicability of the SVR is highly affected due to the difficulty of selecting the SVR parameters appropriately. The imperialist competitive algorithm (ICA) as a socio-politically inspired optimization strategy is employed to solve the real world engineering problems. This optimization algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. This paper presents a new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the SVR. A real data set from a case study of an NPD project in a manufacturing industry is presented to demonstrate the performance of the proposed model. In addition, the comparison is provided between the proposed model and conventional techniques, namely nonlinear regression, back-propagation neural networks (BPNN), pure SVR and general regression neural networks (GRNN). The experimental results indicate that the presented model achieves high estimation accuracy and leads to effective prediction.

Long lasting complicated processes and organizational features generate abundant risks in Engineering, Procurement and Construction (EPC) projects.Iran witnesses an unprecedented boom in engineering, procurement and construction activities at all levels with the government's goal of diversifying its income away from oil dependence to commercial and industrial activities based on the fourth economical development plan.The number, size and complexity of new EPC projects have created an extra burden on the participants and resulted in lots of risks.It is important to identify and prioritize the important risks in Iran to help local and international companies to consider these important risks.Hence, risk identification and prioritization are influential factors in risk monitoring decisions (Ebrahimnejad et al., 2009).The risk management process aims to identify and assess project risks in order to enable them to be understood clearly and managed effectively.In fact, project risk management is a systematic way of looking at areas of risk and consciously determining how each area should be treated.It is a management tool that aims at identifying sources of risk and uncertainty, determining their impact, and developing appropriate management responses (Thomas, 2003.).There are many commonly used techniques for risk identification and prioritization separately.These techniques generate a list of risks that often does not directly assist the project manager in knowing where to focus risk management attention.Qualitative assessment can help to prioritize identified risks by estimating their probability and impact, exposing the most significant risks; this approach deals with risks one at a time and does not consider their possible correlations, and so also does not provide an overall understanding of the risk faced by the project as a whole (Hillson, 2002).Project risk prioritization is usually affected by numerous factors including the human error, data analysis and available information.The great uncertainty in projects often causes difficulty in assessing risk factors.However, many risk assessment techniques currently used in EPC projects are comparatively mature, such as fault tree analysis, event tree analysis, monte carlo analysis, scenario planning, sensitivity analysis, failure mode and effects analysis, program evaluation and review technique (Carr & Tah, 2001).In this paper, an applicable approach in an uncertain environment that can identify and prioritize project risks simultaneously is introduced.A decision approach is proposed that www.intechopen.com

The issue of risk assessment has been always the matter of debate in large engineering projects (LEPs). The assessment is an indispensable means for the projects to accomplish their objectives. It is firmly accepted that LEPs are particularly subject to more potential risks than other business activities because of their complexity, uncertainty and ambiguity. These characteristics are often conducive to small sample sizes of the gathered risk data in practice. Consequently, traditional statistical techniques cannot contribute significantly to analyze the risk data. The non-parametric resampling technique, namely bootstrap, has been used subsequently to solve numerous complicated problems and evaluate the accuracy of a parameter estimator in situations where commonly used techniques are not valid. It is also more natural, applicable and simple to estimate the risk data in an interval form under decision-making process by considering the concept of safety by professional experts in LEPs. Hence, in this paper, a new approach based on bootstrap technique with the interval analysis is presented in the context of the project risk assessment. The proposed approach not only plays an important role in reducing risk data and saving time but also is more economical. A real case study is conducted to illustrate the applicability of the approach. Finally, the comparison results indicate that the proposed approach outperforms the traditional technique in terms of the accuracy and efficiency. (C) 2011 Elsevier Ltd. All rights reserved.

Risk assessment in highway projects has been investigated extensively; however, it is still comparatively neglected for this process with a non-parametric jackknife technique. Highway projects' data and experts' remarks in developing countries are small and limited; moreover, statistical distributions of parameters which play significant role in the projects are usually unknown. Therefore, common approaches cannot assist such kind of problems remarkably. To mitigate the foregoing issues in highway projects, the non-parametric jackknife resampling technique is applied in this paper. Risks are first ranked with a common technique, and then those risks will be ranked with the jackknife technique. The final rankings are conducive to some rewarding results, such as reduction of standard deviation and normality of data. Furthermore, the common risk ranking and jackknife risk ranking are compared in detail and illustrated with the risk data from a highway project, and also compared with the normal probability plot.

Bridge scheme selection (BSS) which includes superstructure types is a complex project engineering that is built up in the present paper. By presenting some novel criteria and considering a fuzzy decision making process, we propose a new hybrid quality function deployment (QFD) for TOPSIS approach and applied to BSS project. At last the best type of scheme is obtained.