With the increasing advancement of technology and science in the fields of engineering, different industries have different benefits that can help the global community in areas related to social, environmental, and economic requirements. The mining industry is one of the suitable sources of wealth production in different societies, which leads different societies to appropriate profitability. In this regard, one of the challenges facing managers and decision-makers is not related to financial challenges and risks. This research uses the dynamic intuitionistic fuzzy entropy method to compute the criteria weights, and the dynamic intuitionistic fuzzy maximizing similarity (DIF-SM) approach to compute the DMs' weights to establish consensus dynamic intuitionistic fuzzy group decision making (DIFGDM) and dynamic intuitionistic fuzzy weighting based ideal solution (DIF-WIS) method under dynamic and fuzzy conditions to calculate the ranking of the options. In order to assess the effectiveness of the introduced approach, a real case study was used in Iran. Then, a comparative analysis regarding the relevant studies was used to represent the effectiveness of the method. The results show the reliability of the introduced model, and the final ranking outcomes are the same for both methods.

Investing in renewable energy has been a vital aspect of different governments’ attempts to handle unwanted negative social and environmental impacts of fossil-based energy production. Biomass energy production is considered one of the proper renewable alternatives to replace fossil fuels. Therefore, the optimal design of biofuel energy production networks is an essential part of strategic decision-making to enhance social, environmental, and economic aspects of attempts to produce clean energy. This paper investigates various aspects impacting biorefinery supply chain network design. This includes the possible disruptions in feedstock production as well as, market competition and interactions that can affect feedstock prices. As a result, a mathematical model is developed that minimizes the costs of network design under uncertainty in feedstock demands. The model computes the required demand of feedstock as well as other key parameters that are important in this agent-based simulation process. This is done by developing an agent-based learning approach based on the Roth-Erev method, which considers agents involved in feedstock pricing and flows between the main elements of its supply chain. Then, the final price of feedstock and the tradeoffs among the agents are applied as inputs in the mathematical model to relocate each facility in its supply chain. The data is computed and updated in the mathematical model by using the agent-based approach until the locations of the facilities in two consecutive runs stay intact. The applicability and validity of the model are investigated through a case study presentation. Moreover, the existing literature is utilized to validate each part of the proposed approach

Setting the optimal vehicle routes and the optimal level of inventory to avoid shortage, in addition to reducing costs of transportation, are two of the main objectives in developing the distribution systems of perishable goods. Despite its importance, an area that still requires more investigation is the simultaneous consideration of hub location selection and routing and warehousing operations. In other words, addressing decisions related to the locations of the warehouses, vehicle routing, and warehouse management at the same time could improve the outcome. Therefore, this paper presents a new non-linear mixed-integer model for warehouse location selection, in addition to an integrated multi-objective mixed-integer model for routing and warehousing operations. Moreover, features of real operations are considered, such as dynamic conditions, order picking, and delivery, customer prioritization, time windows for customers, and different perishable goods. Minimizing the operations' costs and time under uncertain conditions is achieved by proposing a possibilistic-robust optimization algorithm. The optimization approach regards the weight of each objective using the opinions of experts by using a novel hesitant fuzzy approach. Finally, a case study is presented, and the results are compared with the actual data along with validating the model by using several numerical examples. The results have shown that the method can perfectly form the supply chain of perishable goods. Furthermore, the optimization method can provide the experts with more flexibility in finding a compromise solution.

Potential competing interests: No potential competing

The recognition of membership function by knowledge acquisition from experts is an important factor for many fuzzy mathematical programming models. Meanwhile, hesitant fuzzy set theory as a known and popular modern fuzzy set by assigning some discrete membership degrees under a set could appropriately deal with imprecise information in decision-making problems. Thus, the hesitant fuzzy membership function (HFMF) estimation could help users of the mathematical programming approaches to provide a powerful solution in continuous space problems. Therefore, this study proposes a possibilistic programming approach based on Bezier curve mechanism for estimating the HFMF. In the process of possibilistic programming approach, an optimization model is presented to tune the primary parameters of Bezier curve by the goal of minimizing the sum of the squared errors (SSE) between the empirical data and fitted HFMF. After that, the efficiency and applicability of the proposed approach is checked by proposing a novel mathematical model for biomass supply chain network design problem. Finally, a computational experiment and validation procedure about the biomass supply chain network design is provided to peruse the verification and validation of the proposed approaches.

Brick making contributes significantly to the of supply materials for the building industry. The majority of brick production sectors, especially in developing countries, employ polluting and energy-inefficient technologies. Due to the increasing pressures on manufacturing firms to improve economic performance and growing environmental protection issues, sustainable and clean production is the main concern for brick makers. This paper considers the technological, economic, environmental, social, and energy-oriented criteria to select the optimal brick production technologies. Therefore, technology selection is viewed as a multi-criteria group decision-making (MCGDM) problem. This research proposes a novel hybrid fuzzy MCGDM (HFMCGDM) model to tackle the problem. In this respect, first of all, the modified triangular fuzzy pair-wise comparison (MTFPC) method is proposed to compute the local weights of criteria and sub-criteria. Then, a fuzzy DEMATEL (FDEMATEL) method is presented to calculate the interdependencies between and within the criteria. Moreover, the integration of MTFPC and FDEMATEL methods is applied to calculate the global criteria weights. Afterward, a novel method is proposed to determine the experts’ weight. Considering the last aggregation approach to diminish data loss, a new version of a fuzzy TOPSIS method is proposed to find the local and global priorities of the candidates. Then, a case study is given to demonstrate the applicability and superiority of the proposed methodology. To get a deeper view about considering kilns, energy and environmental performance of which has been investigated. Moreover, a comparative analysis is presented to illuminate the merits of the proposed methodology. Eventually, a sensitivity analysis is conducted to peruse the influence of criteria weights on ranking order.

In recent years, sustainable development and environmental protection are getting more attention in construction projects. Hence, green road construction (GRC) supplier selection problem is the main key for organizations to grow their environmental and economical performances. Accordingly, a new hierarchical group decision fuzzy ranking framework is presented based on dynamic interval-valued hesitant fuzzy numbers (DIVHFN) and last aggregation approach to select the most appropriate GRC supplier. Thereby, DIVHFN theory and last aggregation concept could decrease the judgmental errors and data loss, respectively. Moreover, the weight of each criterion is obtained by proposing a new dynamic intervalvalued hesitant fuzzy maximize deviation from ideal decision (DIVHF-MDfID) method. Furthermore, the experts' weight is determined by presenting a dynamic interval-valued hesitant fuzzy preference assessment (DIVHF-PA) method. Besides, to reach precise weights the opinions of experts are included in criteria/sub-criteria weights computations. Meanwhile, an actual case regarding GRC supplier evaluation and selection problem for a construction project is provided to detect the implementation process of the proposed approach. Finally, some comparative and sensitivity analysis are performed to confirm the validation and verification of the presented DIVHF-hierarchical group decision (DIVHF-HGD) approach.

Different maintenance policies, including preventive maintenance and predictive maintenance, are introduced to enhance the execution of systems. Maintenance professional experts have faced numerous challenges with distinguishing the proper maintenance policy, among which causes of failure, accessibility, and the capability of maintenance should be regarded seriously. Moreover, most organizations do not have a deliberate and compelling model for evaluating maintenance policies under uncertainty to deal with real-world conditions. The aim of this paper is to introduce a new interval-valued fuzzy (IVF) decision model for the selection of maintenance policy based on order inclination with comparability to ideal solutions by Monte Carlo simulation. This paper introduces novel separation measures and a new IVF-distinguish index via possibilistic statistical concepts (PSCs) which can assist maintenance decision makers to rank maintenance policy candidates. Also, resilience engineering (RE) factors are considered along with conventional evaluation criteria. Finally, the steps of the proposed IVF model-based PSCs are applied to survey a real case in manufacturing industry. Results of the presented model are compared with the recent literature and could help maintenance personnel in identifying the best policy systematically.

Monitoring and estimating the time performance of projects are two crucial factors which lead the companies to be prosperous. To address the issue, the most recent time-based method is earned duration management (EDM) which is used as an efficient technique. This research presents a new triangular intuitionistic fuzzy-EDM model to improve the applicability of time performance indices and to forecast under uncertain conditions. Since traditional fuzzy sets using membership degrees cannot deal with imprecise quantities in real cases, triangular intuitionistic fuzzy numbers (TIFNs) solve this issue by non-membership and hesitation degrees. In this sake, a TIFN as a special type of intuitionistic fuzzy sets (IFSs), defined on the set of real numbers, shows high capability of modeling ill-known and imprecise information in continuous domains. Besides, the importance of risks in time estimation of EDM is less considered in the literature. In this research, a new time-based risk performance indicator (TBRPI) is developed based on novel risk performance metrics (RPMs) to improve the project time performance estimating of EDM. In this respect, the significance of each RPM could increase the accuracy of the proposed approach, in which a new triangular intuitionistic fuzzy group decision method is presented for obtaining the RPMs and constituent factors (CFs)' weights. Finally, a real case study about seawater intake basin construction project is provided to represent the applicability and feasibility of the proposed triangular intuitionistic fuzzy risk-based EDM model.

Nowadays, preferred compromise response of renewable energies' demands regarding the candidate sustainable feedstocks is a crucial issue for market change management. Thus, selecting the most suitable sustainable feedstock is a key factor for optimum renewable products allocation problem. To address the issue, this study proposes a hybrid adaptive framework based on consensus evaluation approach, weighting and ranking procedure, and preferred demand assignment under dynamic hesitant fuzzy sets. In this respect, the consensus evaluation approach is tailored regarding the direct and indirect feedback mechanisms to enhance the quality evaluation of candidate sustainable feedstocks under assessment criteria. Thereby, the weight of each criterion is determined based on the developed dynamic hesitant fuzzy entropy method and the candidate sustainable feedstocks are ranked with respect to developed dynamic hesitant fuzzy positive and negative ideal solutions. Then, a revised multi-choice goal programming model is extended regarding the dynamic hesitant fuzzy closeness indexes to attend to preferred compromise response of demand centers by optimum renewable products allocation. Meanwhile, the presented hybrid adaptive framework is implemented to a real case study to represent the applicability and efficiency of the proposed approach. Furthermore, a comparative analysis is provided by defining eight comparison indexes to compare the obtained results with two recent studies in relevant literature for representing the validation and verification of the proposed approach. The comparative analysis shows that the proposed approach versus the two other approaches has merits such as modeling of uncertainty, experts' weights, adaptive structure, unanimous agreement-based approach, and last aggregation framework. Finally, a sensitivity analysis is represented to show the sensitiveness and robustness of the obtained results from changing the criteria weights, goals values, and consensus elimination. Thereby, the sensitivity analysis indicates that the obtained ranking results are sensitive to sustainability criteria unlike the technical criterion.

In recent years, the implementation of safety management has been increased in construction projects by institutions, and many companies have recognized environmental and social effects of injuries at project work systems. In this regard, a novel decision model is presented based on a new version of complex proportional assessment method with last aggregation under a hesitant fuzzy environment. The decision makers (DMs) assign their opinions by hesitant linguistic variables that are converted to the hesitant fuzzy elements. Also, the DMs’ judgments are aggregated in last step of decision making to decrease information loss. Since weights of the DMs or professional safety experts and evaluation criteria are not equal in practice, a new version of hesitant fuzzy compromise solution method is proposed to compute these weights. In addition, the criteria weights are determined based on proposed hesitant fuzzy entropy method. A real case study in developing countries about the safety of construction projects is considered to indicate the suitability and applicability of the proposed new hesitant fuzzy decision model with last aggregation approach. In addition, an illustrative example is prepared to show that the proposed approach is suitable and reliable in larger size safety problems

In recent years, it has been proven that promoting and observing environmental competence could play an instrumental role in enhancing companies/countries' industries in terms of sustainable development. In this study, a Green Open Location-Routing Problem with Simultaneous Pickup and Delivery (GOLRPSPD) is considered to minimize general costs. In addition to the significance of cost minimization, the objective function aims at promoting environmental competency in terms of the costs of CO2 emissions and fuel consumptions. Meanwhile, in a complex situation, using precise information could yield unreliable results in which considering uncertainty theories could prevent data loss. In this respect, this study assumed the pickup and delivery demand and travel time as probabilistic parameters. To address the issue, a robust stochastic programming approach was developed to reduce the deviations of imprecise information. Moreover, the proposed approach was applied based on five scenarios to decide the best decision in different situations. In addition, a practical example of the multi-echelon open-location-routing model was provided to represent the feasibility and applicability of the presented robust stochastic programming approach. Finally, comparative and sensitivity analyses were carried out to demonstrate the validity of the proposed approach and, also, to point out the robustness and sensitiveness of the obtained results regarding some significant parameters. (C) 2021 Sharif University of Technology. All rights reserved.

In recent years, selection of outsourcing services activities for information technology (IT) which are defined under some outsourcing projects is a challenge for companies under competitive and uncertain environments. To address the issue, assessment techniques via group decision-making could be provided to appropriately appraise the candidates regarding the incomplete information and existing complexity. This study proposes a new integrated group assessment approach based on interval-valued hesitant fuzzy (IVHF) environment to select the best outsourcing projects regarding risks and incomplete information. The proposed approach is established via some procedures to determine experts' weights, criteria weights and candidates' rankings. The interval-valued hesitant fuzzy-collective wisdom weighting (IVHF-CWW) method is presented to compute the weight of each expert/decision maker (DM). Moreover, the interval-valued hesitant fuzzy-preference weighting (IVHF-PW) technique is presented to determine weights of evaluation criteria. Furthermore, the IVHF-utility index approach is elaborated to rank the candidate outsourcing projects. Finally, a practical study is provided to check the feasibility and validity of the proposed interval-valued hesitant fuzzy-integrated group decision-making (IVHF-IGDM) approach.

This paper proposes a system dynamics model to study and analyse interactions among the variables of bioethanol and biodiesel supply chains. The proposed model is used for constructing scenarios to investigate appropriate policy options and their possible future effects on the market share of bioethanol and biodiesel in the USA. In order to increase bioethanol and biodiesel market shares and creating a reasonable balance between them, a scenario-based sensitivity analysis is conducted. The results of sensitivity analysis demonstrate that causing an increase in oil plant and biodiesel production and a decrease in corn and bioethanol production results in an increase in both bioethanol and biodiesel market shares as well as a fair balance between them. Policy implications related to increasing biofuel production in the USA are also provided. Additionally, for validating the proposed model, behaviour reproduction test is done.

A new hesitant fuzzy set (HFS)-ELECTRE for multi-criteria group decision-making (MCGDM) problems is developed in this paper. In real-world applications, the decision makers (DMs)’ opinions are often hesitant for decision problems; thus, considering the exact data is difficult. To address the issue, the DMs’ judgments can be expressed as linguistic variables that are converted into the HFSs, considered as inputs in the ELECTRE method. Meanwhile, an appropriate tool among the fuzzy sets theory and their extensions is the HFSs since the DMs can assign their judgments for an alternative under the evaluation criteria by some membership degrees under a set to decrease the errors. Introduced hesitant fuzzy ELECTRE (HF-ELECTRE) method is elaborated based on the risk preference of each DM with assigning some degrees. Moreover, the weight of each DM is computed and implemented in the proposed procedure to reduce judgments’ errors. Then, a new discordance HF index is provided. Pair-wise comparisons are used for outranking relations regarding HF information. Finally, the validation and verification of the proposed HF-ELECTRE method are demonstrated in a practical example of FMSs.

Selecting the most suitable optimal point among Pareto optimal points could help experts make an appropriate decision in an uncertain and complex situation. In this paper, an evaluation and ranking approach is proposed based on a hesitant fuzzy set environment to assess the Pareto optimal points obtained through the proposed bi-objective multi-echelon supply chain model by locating distribution centers. In this respect, the proposed model has been utilized for perishable products based on fuzzy customers' demand. To address this issue, the possibilistic chance-constrained programming approach has been utilized based on the trapezoidal fuzzy membership function. Moreover, the proposed hesitant fuzzy ranking approach is constructed based on group decision analysis and the last aggregation approach. Thereby, the last aggregation approach by aggregating the experts' opinions in the last step could prevent the data loss. However, a case study about the perishable dairy products is considered to indicate the applicability of the proposed bi-objective multi-echelon supply chain model by locating distribution centers. Finally, a comparative analysis is provided between the obtained results and the current practice to show the feasibility and efficiency of the proposed approach. (C) 2019 Sharif University of Technology. All rights reserved.

Brick manufacturing is an important industry which produces some fundamental building materials. Since brick production industries have environmental adverse effects such as air pollution, excessive energy consumption as well as waste production, they have become more challenging to manage as they face increasing pressure to improve economic performance and the green and clean production concerns. Concerning these issues and taking into account the current state of the literature in this area, this paper, proposes a hybrid hierarchical fuzzy multiple-criteria group decision making (HH-FMCGDM) model based on a modified fuzzy analytic hierarchy process (MFAHP) to evaluate and rank the related alternative technologies based on various criteria including economic, environmental, market related, technical advantages, and so on. In the proposed method MFAHP is utilized to determine the weights of the criteria. Also, a new method is presented to assign a weight factor for each decision maker (DM) in the group decision-making process. Also, we propose a fuzzy extended version of TOPSIS method as an evaluation tool to calculate the local priority of candidates of brick manufacturing technologies. Afterward, in order to avoid the data loss of DMs' judgments, the final rank of alternatives are obtained based on the proposed last aggregation approach. To illustrate the efficiency of the proposed approach, a real-life case study in a brick industry is done based on our approach. Then, a sensitivity analysis is done to assess the robustness and sensitiveness of the obtained results from the proposed approach. Finally, some managerial insights are suggested to increase the applicability of the proposed HH-FMCGDM approach for real cases.

Sustainable evaluation of construction projects in strategy-focused condition is the main issue for municipalities to appropriately improve public sector services. In this respect, the group decision-making methods could help experts to select the suitable sustainable projects and to schedule them regarding their ranking results. Therefore, the objective of this study is to present a hybrid group decision-making approach based on hesitant fuzzy sets theory to select the best strategic project for Tehran municipality. Hesitant fuzzy sets theory regarding the other modern fuzzy sets could assist the experts in assessing the candidate strategic projects based on evaluation criteria by assigning some membership degrees under a set to decrease the judgments’ errors in vague environments. In this proposed approach, the weight of each decision maker (DM) is determined according to the proposed hesitant fuzzy collective wisdom weighting (HFCWW) method. Besides, the evaluation criteria weights are determined based on the presented hesitant fuzzy preference weighting (HFPW) technique. Hence, hesitant fuzzy utility index method is defined to rank the candidate strategic projects. Finally, a real case study about the sustainable strategic project selection for Tehran municipality is provided to represent the feasibility and applicability of the proposed framework.

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.

Complex proportional assessment (COPRAS) methodology is one of the well-known multiple criteria group decision-making (MCGDM) frameworks that can focus on proportional and direct dependences of the significance and utility degree of candidates under the presence of mutually conflicting criteria in real-worldcases. This studyelaboratesa newintuitionistic fuzzy modified group complex proportional assessment (IF-MGCOPRAS) method.This group decision-making methodologymakes the suitable decision by considering both concepts of the intuitionistic fuzzy positive ideal and negative ideal solutions.The performance of the candidates with respect to various criteria and corresponding criteria weights are linguistic termsthat expressed as intuitionistic fuzzy numbers. Then,intuitionistic fuzzy weighted averaging (IFWA) relation is employed to aggregate individual opinions of experts.Furthermore, a new intuitionistic modified relativeindex is manipulatedto specify the most appropriate candidate for a particular engineering application in a manufacturing industry.In this respect, an illustrative example for group decision making in an equipment selection problem is considered to demonstrate theprocedure ofproposed complex assessment method. The obtainedresults of IF-MGCOPRAS method represented that a reasonable and satisfactory assessmentfor equipment decision makingproblem is occurred. Finally, a comparative analysis and discussion with the intuitionistic fuzzy group TOPSIS method is provided.