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Due to today's competitive environment and information boom, companies are concerned about production planning in uncertain conditions. This paper integrates decision-making method with production scheduling model by considering limited resources. In this paper, a new mathematical model is extended for production project scheduling with multiple execution modes. The main aim of the formulation is to plan and schedule real production projects in uncertain environments. A new mixed-integer linear formulation is presented by considering trade-off of cost, time, as well as quality. Cost–time-quality trade-off is extended with the interval information. In the presented model, activity quality could be enhanced by reworking. The interval forms of some parameters, including duration, quality of activities, cost, and total available resources, are obtained by determining weights of experts and aggregating them. The presented group decision-making method is based on a bi-directional projection measure to deal with interval information. Since the mathematical model is multi-objective and some data are interval, a new modified solution method is developed for solving the model. The presented method for both decision-making and mathematical models is investigated by a real-world production project and two datasets to ascertain the accuracy of the model. Finally, an appropriate sensitivity analysis is proposed. [ABSTRACT FROM AUTHOR]

Organizations must regard environmental and green concepts in their decision-makings and all of the significant tasks in supplier selection process for the supply chain. For this purpose, the evaluation of conflicting criteria with environmental and conventional approaches can be simultaneously considered in the green supplier selection problem (GSSP). This paper introduces a novel group decision-making process with Monte Carlo simulation under interval-valued fuzzy and stochastic environments for solving the GSSPs. The proposed compromise ratio model includes some main phases: (1) weighting and rating phase, (2) aggregating phase, (3) simulating phase, and (4) ranking phase. New separation measure matrixes are developed with an interval-valued fuzzy possibilistic statistical approach. Then, new distinguish indexes and new final score index are presented to indicate the last inclination arrangement of green supplier candidates. An application example in the GSSP is provided from the recent literature to show the applicability of proposed interval-valued fuzzy-stochastic group decision model. Finally, a comparative analysis is conducted to highlight the advantages of the model in comparison with similar decision methods. [ABSTRACT FROM AUTHOR]

Green supplier selection problem (GSSP) is viewed as multiple attributes group decision-making (MAGDM) issue that includes the green growth and influential factors within subjective and objective natures. Because of the expanding uncertain conditions of social and economic environments, some assessment factors are not sufficiently described by numerical appraisals and classic fuzzy sets. Moreover, supply chain decision makers (DMs) may not provide complete rationality under numerous viable choice circumstances. In this research, a new MAGDM model is proposed by interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) via some matrices of possibilistic mean and standard deviation statistical concepts. A new weighting method of experts within the group decision-making process is developed based on possibilistic statistical information. Also, a new ranking process based on relative-closeness coefficients is presented to rank all green supplier candidates under IT2TrF uncertainty. Finally, this research offers an illustrative example in supply chain networks to appraise green supplier candidates in terms of some factors by the proposed model along with the comparison to a recent decision method. [ABSTRACT FROM AUTHOR]

High-technology projects are known as tools that help achieving productive forces through scientific and technological knowledge. These knowledge-based projects are associated with high levels of risks and returns. The process of hightechnology project and project portfolio selection has technical complexities and uncertainties. This paper presents a novel two-parted method of high-technology project portfolio selection. In the first part, a new decision-making model under Pythagorean fuzzy set (PFS) uncertainty is introduced that is last aggregation and avoids defuzziffcation until the last step of the process. In the last step, a new PFS ranking method is used to make crisp and comparable values. Outcomes from this part form the objective function of a new integer programming (IP) of the project portfolio selection. To display the models application, data from a real case study of high-technology project evaluation and selection is presented, and the steps of the approach are illustrated in addition to presenting the effcacy of the model. [ABSTRACT FROM AUTHOR]

Multiple attributes group decision making (MAGDM) is regarded as the process of determining the best feasible solution by a group of experts or decision makers according to the attributes that represent different effects. In assessing the performance of each alternative with respect to each attribute and the relative importance of the selected attributes, quantitative/qualitative evaluations are often required to handle uncertainty, imprecise and inadequate information, which are well suited to represent with fuzzy values. This paper develops a VIKOR method based on intuitionistic fuzzy sets with multi-judges and multi-attributes in real-life situations. Intuitionistic fuzzy weighted averaging (IFWA) operator is used to aggregate individual judgments of experts to rate the importance of attributes and alternatives. Then, an intuitionistic ranking index is introduced to obtain a compromise solution to solve MAGDM problems. For application and validation, this paper presents two application examples and solves the practical portfolio selection and material handling selection problems to verify the proposed method. Finally, the intuitionistic fuzzy VIKOR method is compared with the existing intuitionistic fuzzy MAGDM method for two application examples, and their computational results are discussed. [ABSTRACT FROM AUTHOR]