Proposed Decision Framework Research Articles

Performance evaluation of concurrent supply chain resilience strategies

Despite the growing research on supply chain resilience (SCR) strategies, a comprehensive performance assessment for implementing multiple strategies simultaneously, considering both positive and negative synergistic effects, is still lacking. Given the complex nature of resilience, it is important for any such assessment to incorporate a multi-dimensional view of resilient behavior. To help address this gap in the literature, we propose an extended slack-based super-efficiency data envelopment analysis for evaluating the performance of SCR strategies when they might be utilized concurrently. The new approach characterizes resilience performance by considering the cost of implementing a set of resilience strategies (resilience cost) as an input to the assessment process, and by considering (1) the resulting cost of the disruption, (2) the impact on the service level, and (3) the associated recovery time as outputs. Taken together, these metrics allow for assessing the combined performance of each candidate set of resilience strategies under their synergistic effects. This new and comprehensive approach for resilience assessment is carefully incorporated into a framework that ranks different subsets of relevant strategies for any given supply chain, according to their relative effectiveness across a variety of different disruption scenarios. The proposed decision framework is able to help decision-makers identify the best ensemble of resilience strategies available. A real-world case study is used to illustrate the significant potential of the framework, and a number of important managerial insights are provided to help decision-makers more effectively analyze and implement their own optimal subsets of SCR strategies, particularly in the presence of network quality or budgetary constraints. Theoretical implications include advancing the understanding of multi-strategy interactions in SCR, while practical implications focus on guiding SC managers in selecting and implementing the most effective resilience strategies under various conditions.

Public Private Partnership to Brownfield Remediation Projects in China: A Combined Risk Evaluation Approach

Brownfields have been receiving significant attention all over the world because of their potential threats to the environment and public health. However, a funding shortage constitutes the main obstacle to the brownfield remediation (BR). In China, to ease financial dilemmas, the governments seek collaborations with private-sector companies, i.e., the Public Private Partnership (PPP) mode. Despite all the benefits, BR and PPP contain high risks, making stakeholders extremely cautious about investing in such projects. To support the decision-making process of the public and private parties, this paper designs a comprehensive approach to evaluate the risks of BR PPP projects in China. In more detail, several commonly used risk methods, such as TOPSIS, GRE, and FSE, are employed to construct a combined risk evaluation process, which applies multiple combined evaluation techniques to iteratively integrate individual results from those methods until a valid common result is achieved. To show the practical implementation procedure of the proposed combined approach, a hypothetical case study is performed to assess the risks of seven BR PPP projects. The analytical process also verifies that the consistency and reliability of the risk evaluation result can be achieved effectively and efficiently by jointly deploying multiple risk methods through combined techniques. The proposed decision framework facilitates a novel research idea in evaluating complicated risk situations, and can be applied to other similar scenarios where uncertainties and inconsistencies are inevitable.

Optimal Establishments of Massive Testing Programs to Combat COVID-19: A Perspective of Parallel-Machine Scheduling-Location (ScheLoc) Problem

Massive testing to identify COVID-19-infected people is crucial in combating COVID-19. However, from the perspective of facility location problems, many current massive testing programs are not properly set, leading to unreasonable travelling distances, long makespan, unbalanced workload, and long queues. This article proposes a decision framework for developing massive testing programs. Specifically, a biobjective parallel-testing-site Scheduling-location (ScheLoc) model is formulated, simultaneously minimizing the makespan and total travelling distance. The former can help reduce the time length of potential virus spread, and the latter can help alleviate the risk of virus spread and traveler inconvenience. To solve the proposed biobjective ScheLoc problem, in addition to the standard <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {\epsilon }$</tex-math></inline-formula> -constraint method, we further develop two novel strategies. The first one iteratively solves simpler approximate MIP models (IMIP). The second innovatively extends the classical logic-based Benders decomposition approach to solve biobjective problems (B-LBBD). A Hong Kong-based case study shows that the proposed decision framework can significantly reduce the makespan and travelling distance (with a mean of 13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 5.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , respectively) and enhance workload balancing. Besides, the developed solution methods, especially the B-LBBD, outperform the adapted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {\epsilon }$</tex-math></inline-formula> -constraint method in various aspects.

A multi-stage group decision making approach for sustainable supplier selection based on probabilistic linguistic time-ordered incentive operator.

This study proposes a novel multi-stage multi-attribute group decision making method under a probabilistic linguistic environment considering the development state and trend of alternatives. First, the probabilistic linguistic term set (PLTS) is used by decision makers (DMs) to describe qualitative evaluation information. Subsequently, the weights of DMs for different attributes in different periods are determined by the credibility degree, which is combined with the hesitancy degree and the similarity degree. The evaluations of different DMs for alternatives and the evaluations of DMs' intentions to reward or punish are then aggregated. Later, the trend change level and the trend change stability of alternatives are measured through the means of reward and punishment incentives. Additionally, the probabilistic linguistic time-ordered incentive operator is proposed to aggregate the development state evaluation information and development trend evaluation information in different periods, and alternatives are prioritized by the extended TOPSIS method in the probabilistic linguistic environment. Finally, the practical use of the proposed decision framework is validated by using a sustainable supplier selection problem, and the effectiveness and the applicability of the framework are discussed through comparative analysis. The results show that the proposed approach can select suitable sustainable suppliers by considering their development state and trend in multiple stages.

Parametric optimization of hybrid artificial roughness used in solar air heaters using multiple criteria decision making techniques

Solar air heaters (SAHs) are widely accepted and used for drying and heating purposes. Numerous methodologies are available for augmentation of the performance of a SAH but the application of artificial roughness underside the absorber plate is reported to bean efficient one. This study aims to present the effects of using hybrid artificial roughness combining the transverse ribs along with discrete inclined ribs, on the thermo-hydraulic performance of roughened SAH. The response surface methodology (RSM) is utilized for the design of experiments to investigate the influence of relative roughness height (0.018 ≤ e/ D ≤ 0.036), relative roughness pitch (5 ≤ p/ e ≤ 15), attack angle (45° ≤ α ≤ 75°), and Reynolds number (1,900 ≤ Re ≤ 20,300) as input parameters. The empirical equations are developed for Nusselt number, friction factor, and thermo-hydraulic performance parameter based on the regression analysis. An integrated analytical hierarchy process-based weighted aggregated sum product assessment (AHP-WASPAS) method is used to evaluate an optimal parametric combination ( e/ D = 0.036, p/ e = 15, α = 75°, and Re = 20,300). The results are supported by the corresponding experimental outcomes. Also, the sensitivity analysis and rank reversal tests performed as qualitative assessment procedures for the results obtained from the proposed strategy. The proposed decision framework is reported to be very sensitive to several criteria weight fluctuations with maximum and least deviations of 15.41% and 2.56% for δWjp = +50% and δWjp = −10%, respectively. Further, the obtained results confirm the robustness of aopted decisional scheme with just one rank reversal.

A Decision Support Framework for Pollution Source Detection via Coupled Forward‐Inverse Optimization and Multi‐Information Fusion

AbstractDue to the uncertainty in sensor data, low model accuracy, and high parameter heterogeneity in water quality modeling, pollution source detection (PSD) typically results in a problem of multiple possible solutions, which is the so‐called non‐uniqueness effect. Identifying unique solution to PSD problems is fundamentally essential for water quality control in surface water and groundwater systems. This study proposes a decision support framework to reduce the impact of uncertainty and identify a unique solution using a consensus‐based multiple information fusion method. Multiple water quality information sources are fused in the framework via spatial clustering and temporal Bayesian updating. Considering the real‐world complexity, the recession‐curve displacement method is used to handle multi‐sources scenario to enhance the accuracy of the framework. Meanwhile, a coupled forward‐inverse model instead of random sampling is used to improve the solving efficiency of PSD. The framework is validated by a real water pollution event and a number of semi‐hypothetical case studies. The results show that the prediction accuracy of the single‐source and double‐source pollution problems are 88.40% and 82.69%, with an average relative error of 9.75% and 11.42%, respectively. In addition, the uncertainty contribution quantified by a variance decomposition approach suggests that the interaction between parameter and measurement uncertainties has the greatest impact on the PSD results. The results reveal the advantages of the proposed decision framework for government organizations and communities involved in water environment management.

A Multi-Stage Decision Framework for Optimal Energy Efficiency Measures of Educational Buildings: A Case Study of Chongqing

Buildings consume large amounts of energy resources and emit considerable amounts of greenhouse gases, especially existing buildings that do not meet energy standards. Building retrofitting is considered one of the most promising and significant solutions to reduce energy consumption and greenhouse gas emissions. However, finding suitable energy efficiency measures for existing buildings is extremely difficult due to the existence of thousands of retrofit measures and the need to meet various objectives. In this paper, a multi-stage decision framework, including a multi-objective optimization model, and a ranking method are proposed to help decision-makers select the optimal energy efficiency measures. The multi-objective optimization model considers the economic and environmental objectives, expressed as the retrofit cost and energy consumption, respectively. The entropy weight ideal point ranking method, an evaluation and ranking method that combines the entropy weight method and ideal point method, is adopted to sort the Pareto front and make a final decision. Then, the proposed decision framework was implemented for the retrofit planning of an educational building in Chongqing, China. The results show that decision-makers can quickly identify near-optimal energy efficiency measures through multi-objective optimization and can select suitable energy efficiency measures using the ranking method. Moreover, energy consumption can be reduced by building retrofitting. The energy consumption of the case building was 64.20 kWh/m2 before retrofitting, and the value can be reduced by 6.79% through retrofitting. Furthermore, the reduction in building energy consumption was significantly improved by applying the decision framework. The highest value of energy consumption was 59.84 kWh/m2, while the lowest value was 27.11 kWh/m2 when implementing the multi-stage decision framework. Thus, this paper provides a useful decision framework for decision-makers to formulate suitable energy efficiency measures.

The waste management supply chain: A decision framework

ABSTRACTThe alarmingly increasing trends in worldwide waste generation call for a holistic analysis of waste management supply chains. Using a comprehensive end‐to‐end (i.e., waste generation to waste disposal) decision framework, this article analyzes key decisions of a waste management firm (WMF) focused on the proportions of dry (and wet) waste to recycle (and compost). This framework is applied to assess the impact of: (i) the preprocessing of generated waste at source (i.e., the “upstream factors”) and (ii) the market prices of recycled dry and composted wet wastes (i.e., the “downstream factors”) on WMF's decisions. One key insight is that the WMF will choose to process more waste when the market prices for processed wastes are high, and/or when more waste is preprocessed at source. Improvements in presorting can be more economical and offer a long‐term sustainable solution to efficient waste management. From a policy perspective, we observe that taxing a WMF for waste disposal could dissuade the WMF from participating in waste processing especially when its marginal processing costs are high. The decision framework and the corresponding model are calibrated to different world regions using secondary data on these regions, classified by their income levels. It is observed from our data analysis that uniform “one‐size‐fits‐all” policies are dominated by region‐specific tailored policies for efficient waste management. Hence, prescriptions should be carefully formulated based on the type of waste generated and the processing/disposal options available in a region. For example, composting more wet waste at source is a better choice in low‐, low‐middle‐, and middle‐high‐income regions, whereas this is not the case in high‐income regions. The proposed decision framework also provides an explanation of the negative impact on recycling initiatives at a local level stemming from decreasing recycled material prices. Given that this is the first study to characterize and analyze the waste management supply chain, the article also highlights some areas for future research.

A New Decision Framework of Online Multi-Attribute Reverse Auctions for Green Supplier Selection under Mixed Uncertainty

In order to realize the “dual carbon” goal proposed for the world and to seek the low-carbon and sustainable development of the economy and society, the green supply chain management problem faced by Chinese enterprises and governments is particularly important. At the same time, how to quickly and efficiently select the suitable green supplier is the most basic and critical link in green supply chain management, as well as an important issue that Chinese government and enterprises must face in the process of green material procurement. In addition, there are various uncertainties emerging in the current market environment that hinder the green suppliers and the buyer to make the efficient decisions. Therefore, in order to find a more suitable and efficient method for green supplier selection, from the standpoint of the buyer, a new decision framework of online multi-sourcing, multi-attribute reverse auction (OMSMARA), which effectively improves the procurement efficiency and reduces procurement costs and risks, is proposed under the mixed uncertainty. Specifically, the main innovation work includes three aspects: Firstly, the trapezoidal fuzzy numbers are applied to describe the uncertain bidding attribute values by the green suppliers. Secondly, the hesitant fuzzy sets theory is introduced to characterize the buyer’s satisfaction degrees of the bidding evaluation attribute information, and the attribute weights are determined by using the hesitant fuzzy maximizing deviation method. Thirdly, a fuzzy multi-objective mixed integer programming model is proposed to solve the green supplier selection and quantity allocation question in OMSMARA. A numerical example is given to demonstrate the feasibility and effectiveness of the proposed decision framework, and the sensitivity analysis and comparison analysis further show the robustness and reliability of the proposed solution method.

Blockchain knowledge selection under the trapezoidal fermatean fuzzy number.

Blockchain knowledge signifies a useful fundamental knowledge to safeguard faith in transboundary transmittals for main banks and financial institutions. In the study of group decision-making, the most important issue is how to coordinate opinions from different blockchains to reach a compromise under uncertainty. To tackle uncertainties surrounding multi-attribute group decision-making (MAGDM) problems in real-life scenes, we introduce a trapezoidal fermatean fuzzy set which generalizes trapezoidal fuzzy sets and fermatean fuzzy sets. The trapezoidal fermatean fuzzy model enables the degrees of membership, abstention, and non-membership to be expressed by linguistic terms. We define the operational laws of trapezoidal fermatean fuzzy numbers, and Einstein aggregation operator based on the trapezoidal fermatean fuzzy number. This makes it more flexible and descriptive to model the attitudes of Blockchain knowledge in MAGDM applications. Since multi-input arguments are interconnected and Blockchain knowledge has a lot of options perception, we also define the TOPSIS technique to facilitate the fusion of trapezoidal fermatean fuzzy information. With the aid of the trapezoidal fermatean fuzzy-TOPSIS technique, the main goal of this research is to present a general MAGDM framework by integrating the step with the complex proportional assessment. A trapezoidal fermatean positive ideal solution always wants the maximum value of the benefit criteria and the minimum value of the cost criteria. On the other hand, the trapezoidal fermatean negative ideal solution always wants the maximum value of the cost criteria and the minimum value of the benefit criteria. An integrated trapezoidal fermatean fuzzy-TOPSIS framework is established. In the proposed decision framework, the trapezoidal fermatean fuzzy-TOPSIS method is utilized to identify the subjective weights of decision attributes, and the trapezoidal fermatean fuzzy-TOPSIS approach is used to rank alternatives. Lastly, a case study concerning blockchain knowledge assessment is presented to demonstrate that the suggested scheme is feasible and effective. Furthermore, sensitivity and comparison analyses are conducted to show the robustness and superiority of the proposed method.

The probabilistic linguistic decision framework of distributed energy storage system project plan based on the sustainability perspective

Countries throughout the world are aggressively encouraging the development of renewable energy. Renewable energy power generation presents distributed characteristics, which increases the difficulty of power coordination and optimization. Compared to the thermal power stations, distributed energy storage system (DESS) can help reduce the aforementioned difficulty without environmental pollution and energy consumption, it has good social and environmental benefits. However, due to the high operating cost of DESS, its investment is cold. Therefore, the investment decision-making of DESS must be considered from the perspective of sustainability, but there is still a blank in this aspect. This problem belongs to the typical research category of decision theory, it can be solved by constructing a decision framework composed of decision index system and decision-making model. So the decision index system of DESS project plan was established based on the sustainability theory and the real DESS operational scenario; for the decision model, the probabilistic linguistic term set (PLTS) is taken as the expression of decision data of DESS, the fuzzy measure and VIKOR were used to reflect the importance of criteria and integrate the decision data respectively. The decision framework proposed in this paper has the following advantages: the proposed decision index system content 3 attributes, 7 criteria and 21 sub-criteria, can provide scientific guidance for DESS project plan decision; second, it can not only deal with uncertainty effectively but also is convenient for experts to express their own preferences; third, it can solve the problem that the independence assumption of criteria is difficult to meet in reality; forth, it reflect the overall utility and local disadvantages of the plan of DESS at the same time. Through comparative analysis and sensitivity analysis, the proposed decision framework can provide more scientific decision-making results of DESS project plan for energy enterprises, and the results have better robustness.

A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis

International Electrotechnical Commission (IEC) proposed the IEC three-ratio method based on Dissolved Gas Analysis (DGA), which is one of the most effective tools for Power Transformer Fault Diagnosis (PTFD). However, the PTFD accuracy is generally limited because the classification boundary could be too stiff to classify samples located near the boundary. The Support Vector Machine (SVM) was applied to PTFD to improve diagnosis accuracy, while traditional SVM multi-classification methods and parameter optimization algorithms are subject to poor training efficiency. As a result, the SVM-based PTFD model is difficult to update frequently with the accumulation of fault data. A new SVM-based PTFD decision framework is proposed in this paper which can significantly boost the training efficiency and ensure the accuracy. In the proposed framework, a multi-step feature extraction process consisting of characteristic gas concentration and its ratios is applied. Based on the feature distribution of various samples, a proper SVM multi-classification method is presented using a hierarchical decision tree structure. In addition, according to the principles of SVM and radial basis kernel function, a Support Vector feature-based parameter optimization algorithm (SVFB) is proposed. IEC TC 10 data and the historical data of online transformer monitoring provided by the State Grid Corporation of China are adopted as sample sets. The simulation results demonstrate that the proposed decision framework can reach high diagnosis accuracy while shortening the training time.

Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model

Hydrogen technology has recently attracted great attention as a new energy technology with a potential to transform existing energy systems. However, since hydrogen technology is based on a concept encompassing a broad range of topics, such as hydrogen production, storage, distribution, and utilization, it is difficult for individual field experts to analyze it from an integrated viewpoint. In this study, we used a semiautomated, unsupervised learning approach for patent data analysis to identify specific technology topics in various fields of hydrogen technology and to analyze the technological focus of key countries. Thus, we collected 17 281 hydrogen technology patents from the last decade (2010–2019) from the United States Patent and Trademark Office (USPTO) and input the text in the title and abstract of the collected patents along with their metadata to a structural topic model. Consequently, we identified various hydrogen technology topics estimated based on the co-occurrence pattern of words within patents and represented as probabilistic word distribution. Furthermore, the metadata of the collected patents were used to identify topics that were newer or more impactful and appeared more frequently in patents from a certain country. After identifying technology topics, we also estimated the technology maturity rate (TMR) of each topic to measure its remaining potential. Among the 40 latent technology topics identified from the collected patents, 11 topics showed increasing proportion over time (new and trending) and five topics were highly cited by other patents (impactful). Furthermore, based on the analysis results, implications were presented for hydrogen research and development (R&D) strategy by (1) comparing the technology portfolios of key countries, (2) performing a technology correlation analysis of the identified hydrogen technology topics, and (3) proposing a decision framework for policymakers to categorize identified topics. We found that the primary technological focus was on fuel cell technologies for South Korea and Japan whereas hydrogen production technologies for France and the United States. Furthermore, technological rivalry patterns between key countries may differ notably depending on their specific technology topics or fields, highlighting the need for considering such aspects in developing R&D strategies. Finally, based on the proposed decision framework, we identified which technology topic to continue to focus on and consider easier expansion.

Integrating supplier selection, lot sizing and facility location decisions under a TBL approach: a case study

Triple bottom line (TBL) is one of the well-known frameworks for measuring the sustainability of a system in which environmental and social aspects are also considered together with the economic aspect. In this study, we consider the integrated supplier selection, lot sizing and facility location decisions and propose a two-phase decision framework based on TBL approach. In the first phase of the study, we use a trapezoidal type-2 fuzzy AHP algorithm for the initial evaluation of the suppliers based on various economic, environmental and social criteria, whereas in the second phase of the study, we propose a two-stage stochastic programming model for the integrated supplier selection, lot sizing and facility location decisions under supply and demand uncertainties. We also present a real-life case study to test the proposed decision framework. Computational results, on one hand, show the applicability of the proposed decision framework on real-life cases and, on the other hand, bring significant managerial insights. Particularly, we observe that all three pillars (economic, environmental and social) of sustainability are in conflict with each other and thus being both environmentally and socially responsible is much more costlier than being solely environmentally or socially responsible. Moreover, we observe that ignoring the uncertainties in the system may yield to higher costs and impractical results.

Optimizing coral reef recovery with context-specific management actions at prioritized reefs

Assisting the natural recovery of coral reefs through local management actions is needed in response to increasing ecosystem disturbances in the Anthropocene. There is growing evidence that commonly used resilience-based passive management approaches may not be sufficient to maintain coral reef key functions. We synthesize and discuss advances in coral reef recovery research, and its application to coral reef conservation and restoration practices. We then present a framework to guide the decision-making of reef managers, scientists and other stakeholders, to best support reef recovery after a disturbance. The overall aim of this management framework is to catalyse reef recovery, to minimize recovery times, and to limit the need for ongoing management interventions into the future. Our framework includes two main stages: first, a prioritization method for assessment following a large-scale disturbance, which is based on a reef's social-ecological values, and on a classification of the likelihood of recovery or succession resulting in degraded, novel, hybrid or historical states. Second, a flow chart to assist with determining management actions for highly valued reefs. Potential actions are chosen based on the ecological attributes of the disturbed reef, defined during ecological assessments. Depending on the context, management actions may include (1) substrata rehabilitation actions to facilitate natural coral recruitment, (2) repopulating actions using active restoration techniques, (3) resilience-based management actions and (4) monitoring coral recruitment and growth to assess the effectiveness of management interventions. We illustrate the proposed decision framework with a case study of typhoon-damaged eastern outer reefs in Palau, Micronesia. The decisions made following this framework lead to the conclusion that some reefs may not return to their historical state for many decades. However, if motivation and funds are available, new management approaches can be explored to assist coral reefs at valued locations to return to a functional state providing key ecosystem services.

Sustainable oil selection for cleaner production in Indian foundry industries: A three phase integrated decision-making framework

Oil in casting industries is one of the major sources of environmental pollution as its burning directly results in the emission of toxic gases such as carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulphur dioxide (SO2) and suspended particulate matters (SPM). The aim of this work is to select the best sustainable oil for a cleaner and environment friendly production system in small scale casting industries of Agra city located in India. Performance, quality and cost are considered as the most influential parameters, while jatropha, vegetable oil, karanja oil and light diesel oil are considered as the alternatives. A novel integrated three phase decision-making framework is proposed, encompassing fuzzy analytical hierarchy process (FAHP), fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and fuzzy evaluation based on distance from average solution (FEDAS) methods. FAHP is implemented for computing weights of the considered criteria and sub-criteria. These weights are subsequently used in FTOPSIS and FEDAS methods to derive ranking of the considered oils. Light diesel oil emerges out as the best oil under the considered criteria with the highest closeness coefficient (0.9807) and appraisal score (0.6683) values. For validating the results of the proposed decision framework, sensitivity analysis (SA) has been performed. From the twelve experiments of SA, it is observed that light diesel oil retained its highest score in nine experiments which substantiate its top priority as well as FTOPSIS and FEDAS-based results.

Determination of Difficulty Level for Garment Model with Fuzzy Logic Method

The purpose of this study is a rule-based fuzzy logic approach is proposed for determining model difficulty in manufacturing top clothing for ladies. A decision framework concerned with different scenarios (main pattern types and material types) is proposed for determining the model difficulty. Each scenario modeled as a Mamdani type fuzzy inference system which is known as one of the best approximator fuzzy logic models. The fuzzified input variables are unit operation time, second quality rate and fabric weight. Moreover, two different defuzzification methods which are centroid and middle of maxima are compared for finding best fuzzy logic structure over the six different test instances. According to the results, both deffuzzification methods find similar model difficulty determinations. A graphical user interface of the proposed decision framework is designed in order to apply this to real-life applications. Finally, six different clothing models are identified to be simple, medium-hard, hard and very hard. The results of this study showed that defuzzification methods is not significantly effected the model difficulty decisions off is systems regarding different test instances. The model difficulty values range between 0-10. In order to find a useful difficulty assignment (linguistic), the model difficulty is determined by using the closeness to center value (a2) of membership functions. This research offers a solution to determine the difficulty levels of the garment models.

Evaluation of Road Infrastructure Projects: A Life Cycle Sustainability-Based Decision-Making Approach

Economic growth, social wellbeing, and infrastructure are strongly interrelated and jointly contribute to national development. Therefore, evaluation and selection of a road infrastructure project direly need a comprehensive sustainability assessment integrating holistic decision criteria. This study presents an elaborate life cycle sustainability-based project evaluation tool, comprising an assessment framework, an integration model, and a decision framework. In the first phase, a life cycle sustainability assessment (LCSA) framework for road infrastructure is established using mixed methods. In the second phase, interviews are conducted to obtain pairwise comparisons among impact categories and subjective reasoning of their priorities. Analytical hierarchy process (AHP) is adopted to develop the LCSA integration model. The minimum threshold limits of impact categories are evaluated and integrated into the proposed decision framework. Further, thematic and cross-sectional analyses are performed on the interview findings to rationalize the proposed decision framework. The findings include a detailed and customized project assessment framework, an integration model, and a decision framework for the assessment of different project alternatives. This study helps policy- and decision-makers in selecting the project alternative by maximizing sustainability in road infrastructure projects. Insights into environmental and social externalities and their quantitative interpretation throughout the life of the road are also achieved.

A Time-Dependent Sustainable–Flexible Supplier Selection Considering Uncertainty and TODIM Method in Iranian Dairy Industries

Nowadays, by increasing awareness on environmental and social responsibilities, stakeholders and people put more pressure on the enterprises and companies to respect these issues. Owing to the fact that suppliers as the upstream of the supply chain play an important role in success and prosperity, the sustainability of suppliers would be a must and enterprises need to create and utilize an integrated assessment model based on related criteria in every specific industry. However, these sustainability and environmental criteria especially would be critical in the cold chain and dairy industries; to our knowledge, there are not enough surveys related to this category. Interval triangular fuzzy numbers have been utilized to overcome uncertainty of the linguistic notions of the experts in this area. As criterion’s weight and even its matter would be changed during the time horizon, fuzzy time function would be applied to illustrate these modifications during the time. For solving our integrated sustainable–flexible multi-criteria model, we take advantage of TODIM, a discrete multi-criteria method based on prospect theory in uncertainty which includes gain and loss of decision simultaneously. Finally, managerial insight and associated sensitivity analysis of the ability and robustness of the proposed decision framework have been illustrated in the small- and medium-scale dairy industries in Iran.

Identifying and determining crowdsourcing service strategies: An empirical study on a crowdsourcing platform in China

&lt;p style='text-indent:20px;'&gt;The crowdsourcing platforms, as mediators and service providers, play a critical role in crowdsourcing initiatives. The service quality of a platform has a direct impact on solver satisfaction, and ultimately affects the platform's continuous operation. Service quality can be measured by service quality attributes (SQAs). Thus, identifying and quantifying SQAs are crucial to enhance solver satisfaction. Besides, choosing pertinent strategies and determining priorities for the SQAs are another core issue. To address these issues, this study proposes a novel decision framework that combines the Fuzzy Analytical Kano (FAK) and the Importance-performance analysis (IPA) models. Firstly, 24 related SQAs are identified from five dimensions of service quality. Secondly, we quantify these SQAs into a polar form representation scheme in accordance with the FAK model. In addition, the pertinent service strategies and priorities of the SQAs are confirmed by using the IPA model and Kano categories. Finally, decision priority rules for corresponding strategies and priorities of SQAs are constructed. An empirical study is presented to demonstrate our proposed decision framework on ZBJ platform, which is one of the most widely used online crowdsourcing platform in China.&lt;/p&gt;