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The topic of human-centric design of smart production and logistics processes is increasingly debated in both academic and industrial communities. However, there is still little existing research on the integration of this concept with companies' daily operations. The research presented in this paper aims to expand this area of research by proposing a framework designed for the logistics sector that includes human factors among the criteria for choosing the best technology to adopt based on the company's requirements. The framework, which is, therefore, a decision support tool for technology adoption, is subsequently validated through a multiple case study. The results of the validation highlight the framework's potential as a tool to better understand the human factors involved in tasks and technologies to be adopted in order to avoid possible critical issues related to technological changes (e.g. acceptability, need for additional investment in training, identification of required skills). [ABSTRACT FROM AUTHOR]

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. [ABSTRACT FROM AUTHOR]

Statement of the Problem. Lack of an artificial intelligence (AI)-driven approach to optimize the application of nature-based solutions (NbS) for enhancing energy sustainability in building projects. This gap can result in suboptimal decision-making processes. Purpose. This study aims at introducing a decision support system (DSS) framework that fuses NbS with the power of fuzzy – based AI. The proposal seeks to support decision-makers and empower them with the capabilities of AI. Method. Through a systematic literature review, the aim was to first capture models’ input variables for the development of a DSS framework using fuzzy logic. Conclusions. Our findings underscore the significance of an integrated approach in energy-efficient building projects. The integration of NbS with fuzzy-based AI showcases substantial promise in augmenting decision-making processes, promoting optimized designs that align with both environmental and technological objectives. Practical implications. The proposed DSS framework can lead to improved building project outcomes, reduced environmental footprints, and more resilient infrastructure regarding energy consumption. Future Research Directions: further research endeavors should delve deeper into the practical implementation of the DSS framework across diverse engineering projects. Exploring variations of fuzzy-AI could further enhance the decision support system. Additionally, investigating the potential barriers and challenges in adopting this approach will be crucial in ensuring its widespread adoption.

Information technology project managers (IT PM) have a critical influence on IT project success while attracting and selecting the right IT PM is challenging. We followed a four-level research design and firstly developed a taxonomy as an input for a cluster analysis to identify patterns in IT PM job advertisements. Based hereon, we developed a decision support framework for IT PM recruitment. We evaluated our findings in an online survey. We identified multiple design elements for IT PM job advertisements within five perspectives and deduced five IT PM archetypes. The decision support framework uses five questions to assist IT PM recruitment. We expand the knowledge base and consider not only IT PM requirements but also benefits. Our decision support framework is the first to holistically support IT PM recruitment, supports recruitment managers in structuring job interviews, identifies potential matches between applicants and recruiters, and assists in the final selection.

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft.

AbstractMillions of lives have been impacted by COVID-19, which has spread rapidly. Several vaccines have been developed to curb the severe prognosis induced by the virus. However, a part of the population (elderly and patients with coexisting conditions) is still at risk. It is crucial to identify these patients early since appropriate treatments can be provided to them to prevent the onset of severe symptoms such as breathlessness and hypoxia. Hence, this study utilizes machine learning and explainable artificial intelligence (XAI) to predict COVID-19 severity using biochemical, haematological and inflammatory markers. The patients are grouped into three classes: mild, moderate and severe. Four nature-inspired techniques have been utilized to select the best markers. The final stacked model obtained a maximum accuracy of 84. Demystifying the models has been done using four XAI techniques, including Shapley additive values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Lactate dehydrogenase (LDH), albumin, D-Dimer, c-reactive protein (CRP) and lymphocytes were considered important, according to them. The classifiers can be utilized as a prognostic decision support framework to aid the medical personnel in classifying COVID-19 patients.

Millions of lives have been impacted by COVID-19, which has spread rapidly. Several vaccines have been developed to curb the severe prognosis induced by the virus. However, a part of the population (elderly and patients with coexisting conditions) is still at risk. It is crucial to identify these patients early since appropriate treatments can be provided to them to prevent the onset of severe symptoms such as breathlessness and hypoxia. Hence, this study utilizes machine learning and explainable artificial intelligence (XAI) to predict COVID-19 severity using biochemical, haematological and inflammatory markers. The patients are grouped into three classes: mild, moderate and severe. Four nature-inspired techniques have been utilized to select the best markers. The final stacked model obtained a maximum accuracy of 84. Demystifying the models has been done using four XAI techniques, including Shapley additive values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Lactate dehydrogenase (LDH), albumin, D-Dimer, c-reactive protein (CRP) and lymphocytes were considered important, according to them. The classifiers can be utilized as a prognostic decision support framework to aid the medical personnel in classifying COVID-19 patients. [ABSTRACT FROM AUTHOR]

This present paper is an investigation of a framework for Safety-Critical Maritime Infrastructure (SCMI) evaluation. The framework contains three Multi-Criteria Decision-Making (MCDM) tools, namely: fuzzy Step-wise Weight Assessment Ratio Analysis (SWARA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregates Sum Product Assessment (WASPAS). It also contains five safety practice criteria: people's safety, property safety and monitoring capabilities, response to regular and irregular threats in a robust yet flexible manner, and breaches in physical security. The framework has four safety culture criteria: learning from experience and inter-element collaboration, lack of facility maintenance, and anticipating risk events and opportunities. Through the framework, an evaluation of the safety practices and safety culture of six Nigerian seaports is done. Then, data obtained from the ports in regard to their safety practices and culture were analysed in line with the framework identified and adopted. The results revealed that the safety of people's life is the most important safety practice and contributed about 47.90% to evaluate the SCMI. Results equally showed that the most significant safety culture is learning from experience, and accounted for approximately 53.20% in assessing the SCMI. Similarly, the TOPSIS method ranked Warri (A5) and Tin Can Island A1 as the best and worst safety practices performance, respectively. Results of analyses on the TOPSIS showed that Apapa port (A2) and Onne port (A6) had the best and worst performances correspondingly. WASPAS was also analysed. The results indicated that A6 performed the best safety practice performance, while A2 had the worst safety practice performance. Analysis of the WASPAS method was equally done. It showed that A1 had the worst safety culture performance, while A5 had the best safety culture performance. Therefore, the proposed framework could serve as a veritable tool for analysing SCMI by using safety practice and safety culture criteria.

This study enhances the domain of optimum energy storage system selection by offering a complete decision support framework that incorporates technical, economic, and environmental factors. The technical investigation examines energy and power density measurements, which demonstrate the exceptional volumetric energy storage capacities of lithium-ion batteries at 250 Wh/L. This highlights their appropriateness for applications that prioritize compactness. Lead-acid batteries are a cost-effective option that have a lower energy density of 80 Wh/L. This makes them particularly advantageous in situations where volume limitations are not as important. Flow batteries and supercapacitors are positioned in the middle, with flow batteries offering a well-balanced profile of energy and power density, while supercapacitors excel in applications that need fast charging and discharging. An in-depth evaluation of the startup costs and ongoing expenditures is conducted to thoroughly assess the economic feasibility of adopting an energy storage system. Although lithium-ion batteries have a higher upfront cost of $100,000, they demonstrate a well-balanced economic profile by offering a significant 66% decrease in operating expenditures compared to lead-acid batteries. Lead-acid batteries, which have an initial price tag of $60,000, demonstrate cost-effectiveness but result in higher ongoing costs. Flow batteries and supercapacitors are positioned as premium alternatives, with respective beginning prices of $150,000 and $200,000. Environmental sustainability is a fundamental factor that is assessed via life cycle analysis. Lithiumion batteries, while very efficient, give rise to problems over resource depletion and environmental damage linked to mining activities. Lead-acid batteries, due to their recyclability, provide a more ecologically sound alternative. Flow batteries, which use readily available and environmentally safe components, are very compatible with objectives aimed at promoting sustainability. Supercapacitors, which often use ecologically friendly materials, have potential in reducing negative environmental impacts. MultiCriteria Decision Analysis (MCDA) is used to integrate technical, economic, and environmental assessments. Lithium-ion batteries are considered the most favorable choice due to their exceptional technical performance and economic viability. Lead-acid batteries have a strong market position due to their cost-efficiency and ecofriendliness. Flow batteries and supercapacitors, while they have distinct benefits, are only suitable for certain applications. The stability of the framework is shown by sensitivity analysis, which identifies energy density and beginning costs as crucial factors. Engaging stakeholders helps confirm the framework’s usefulness, ensuring that it can be practically used and remains relevant in guiding well-informed choices in the ever-changing field of energy storage technologies. This study provides vital insights into the discussion on sustainable and efficient energy solutions, presenting a clear plan for decision-makers to navigate the intricacies of selecting energy storage systems.

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

Flooded streets and homes, polluted surface waters, degraded aquatic habitats, stressed aquifers, and expensive drainage infrastructures are evidence that Canada's current approach to stormwater management requires rethinking. To address these challenges, the authors developed an integrated community-level urban stormwater decision support framework. The framework assesses low impact development infrastructure, traditional stormwater infrastructure, and community-level stormwater reuse schemes to better understand the interactions between various water resources in the context of various stormwater management strategies. The framework also incorporates stochastic variation in natural processes to predict a community's water footprint using the One Water Approach. A climate change sensitivity analysis is also incorporated to test proposed stormwater systems for resiliency to climate change. Fuzzy clustering analysis was then performed to identify the most appropriate stormwater management strategy. A case study demonstration of the framework was performed for a hypothetical 80-hectare mixed-use urban community in Kelowna, British Columbia. [ABSTRACT FROM AUTHOR]

Many countries have worked diligently to establish and implement policies and processes to regulate high consequence pathogens and toxins that could have a significant public health impact if misused. In the United States, the Antiterrorism and Effective Death Penalty Act of 1996 (Public Law 104-132, 1996), as amended by the Bioterrorism Preparedness and Response Act of 2002 (Public Law 107-188, 2002) requires that the Department of Health and Human Services (HHS) [through the Centers for Disease Control and Prevention (CDC)] establish a list of bacteria, viruses, and toxins that have the potential to pose a severe threat to public health and safety. Currently, this list is reviewed and updated on a biennial basis using input from subject matter experts (SMEs). We have developed decision support framework (DSF) approaches to facilitate selection of select toxins and, where toxicity data are known, conducted modelling studies to inform selection of toxin amounts that should be excluded from select agent regulations. Exclusion limits allow laboratories to possess toxins under an established limit to support their research or teaching activities without the requirement to register with the Federal Select Agent Program. Fact sheets capturing data from a previously vetted SME workshop convened by CDC, literature review and SME input were developed to assist in evaluating toxins using the DSF approach. The output of the DSF analysis agrees with the current select toxin designations, and no other toxins evaluated in this study were recommended for inclusion on the select agent and toxin list. To inform the selection of exclusion limits, attack scenarios were developed to estimate the amount of toxin needed to impact public health. Scenarios consisted of simulated aerosol releases of a toxin in high-population-density public facilities and the introduction of a toxin into a daily consumable product supply chain. Using published inhalation and ingestion median toxic dose (TD50) and median lethal dose (LD50) values, where available, a range of toxin amounts was examined to estimate the number of people exposed to these amounts in these scenarios. Based on data generated by these models, we proposed toxin exclusion values corresponding to levels below those that would trigger a significant public health response (i.e., amounts estimated to expose up to ten people by inhalation or one hundred people by ingestion to LD50 or TD50 levels of toxin in the modeled scenarios).

• A decision support framework was built to stratify pN2 NSCLC patients. • Patients at high LRR risk and without high-risk histology features benefit from PORT. • The use of PORT cannot improve survival for patients at low risk of LRR. • The benefit of PORT is limited in patients with high-risk histology features. Postoperative radiotherapy (PORT) plays a highly controversial role in pathological N2 (pN2) non-small cell lung cancer (NSCLC) disease. Recent studies reveal that not all patients can benefit from PORT. Further research is needed to identify predictors of PORT. A total of 1044 pathologic stage T1-3N2M0 NSCLC patients were analyzed. Risk factors of distant metastasis were identified by the log-rank tests and the multivariable Cox models. We integrated risk factors of distant metastasis and our previously published loco-regional recurrence (LRR) related prognostic index into a decision support framework (DSF) to predict the outcomes of PORT. An independent cohort was used to validate the DSF. We defined patients with more than two of three identified LRR-related features (heavy cigarette smoking history, clinical N2 status, and more than four positive lymph nodes) as a high LRR risk group. We found the high-intermediate-risk histological type (with micropapillary and/or solid components) was associated with a higher risk of distant metastasis (HR = 1.207, 95 % CI 1.062 to 1.371, P = 0.0038), but not LRR. We built the DSF by combining these two types of features. Patients were stratified into four groups by using the DSF. PORT significantly improved OS only in the subgroup without high-risk histological features (without micropapillary or solid components) and with a high risk for LRR (three-year OS: 66.7 % in the PORT group vs 50.2 % in the non-PORT group; P = 0.023). A particular pN2 subgroup with a high risk of LRR and without micropapillary or solid components could benefit from PORT. [ABSTRACT FROM AUTHOR]

Land degradation and desertification affect around one-third of the land used for agriculture, undermining productivity, and farmers' livelihoods, and resilience. People in rainfed and dryland areas are the most vulnerable to the environmental and social impacts of degradation, yet opportunities to expand agricultural areas in order to meet the needs of an ever-growing population are limited and/or not feasible. On the bright side, there are opportunities to avoid, reduce and reverse land degradation (LD), however, it will require identifying areas that are suited to implement sustainable land management (SLM) practices and create an enabling environment that encourages such initiatives. In Morocco, latest available data from 2015 showed that 19 percent of its land over the total land area is degraded. In order to assess the level of degradation in rainfed landscapes of Morocco and identify the appropriate technological solutions in response to the driving forces and pressures, and to come up with a territorial planning strategy, analyses were carried out at different levels to identify the land degradation “hot spots” as well as the SLM “bright spots.” This paper sheds light on the decision support framework, co-developed with partners and applied in Morocco, the findings from national and subnational assessments of LD, as well as the process of participatory planning in selected communities of the Souss-Massa region for evaluation and scaling up of SLM good practices. The process led to the development of a “Territorial Planning Pact” as a tool for land use planning and a 3-year action plan to help decision-makers implement and mainstream SLM in the selected communities and guide similar processes throughout the region. The “Territorial Planning Pact” forms a base for planning and implementing the development program in the region led by key stakeholders. The paper presents the actions taken and concludes by highlighting the importance of integrating both biophysical and socio-economic information in a multi-level participatory process to identify the actions and responsibilities of various stakeholders and ensure sustainable management of limited natural resources and improved livelihoods for all.

• The concept of urban adaptation is analysed using complex adaptive systems theory. • The connotation of urban adaptation is divided into three dimensions. • A combination of quantitative and qualitative methods to assess urban adaptation. • Gap between quality of urban adaptive development and scale of construction. • Adaptive decision-making frameworks can help managers develop targeted strategies. As urban diseases continue to emerge with the acceleration of urbanization, the transformation of urban governance concepts and systems must be urgently promoted. In dealing with the complex systemic problem of city governance, the concept of adaptation in complex adaptative system (CAS) theory has shown obvious advantages. This approach provides a new perspective for solving this problem in the face of the unprecedented complexity of current disasters. However, research on urban adaptation is currently hindered by imprecise terminology, significant bias, and insufficient empirical evidence. This study reinterprets the concept of urban adaptation in conjunction with CAS theory. The formula for the functional expression of urban adaptation is derived using dissipative structure theory. Indicators are then selected to describe the system of urban adaptive capacity. An adaptive decision support framework is established that covers 29 quantitative and 8 qualitative indicators. The adaptive performance of 12 sample cities is analyzed. Finally, this study provides corresponding planning paths for optimizing urban adaptation based on the evaluation results. The research findings can assist city managers in comprehending the actual situation of cities and provide a basis for formulating targeted governance programs. [ABSTRACT FROM AUTHOR]

Financial institutions benefit from the advanced predictive performance of machine learning algorithms in automatic decision-making for credit scoring. However, two main challenges hamper machine learning algorithms' applicability in practice: the complex and black-box nature of algorithms that hinder their understandability and the inability to guide rejected customers to have a successful application. Regarding customer relationship management is one of the main responsibilities of financial institutions; they must clarify the decision-making process to guide them. However, financial institutions are not willing to disclose their decision-making procedure to prevent potential risks from customers or competitors side. Hence, in this study, a decision support framework is proposed to clarify the decision-making process and model strategic decision-making to guide rejected customers simultaneously. To do so, after classifying customers in their corresponding groups, the capability of Shapley additive exPlanations method is exploited to extract the most impactful features to the prediction's outcome globally and locally. Then, based on the benchmarking approach, the equivalent approved peer is found for the rejected customer for target setting to modify the application. To find the optimal modified values for a counterfactual prediction, a multi-objective gamed-based counterfactual explanation model is developed using the prisoner's dilemma game as the constraint to simulate strategic decision-making. After optimization, the decision is reported to the customers concerning the credential background. A public data set is used to elaborate on the proposed framework. This framework can generate counterfactual predictions successfully by modifying perspective features. [ABSTRACT FROM AUTHOR]

With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval 2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods. [ABSTRACT FROM AUTHOR]

With the growing social and environmental concerns, the integration of sustainability in supplier selection and order allocation is of paramount importance. This research presents a holistic multi-phase decision-support framework to solve the sustainable supplier selection and order allocation problem for the multi-echelon supply chain. The framework comprises a multi-objective mixed-integer nonlinear programming mathematical model augmented with fuzzy multi-criteria decision making techniques and forecast demand. The various economic, environmental and social objectives were optimized for a multi-modal transportation network of a multi-echelon supply chain. The results of the mathematical model highlighted the impact of multi-modal transportation on the total cost and total travel time. The results also demonstrated the relationship between the multi-modal transport network and the environmental impact of the supply chain. The proposed multi-phase holistic decision support framework can be used in the comprehensive sustainability-based analysis of supply chains. [ABSTRACT FROM AUTHOR]

The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided. [ABSTRACT FROM PUBLISHER]

Objective: The effectiveness of Emergency Medical Teams (EMTs) is strongly related to their time of arrival, and usually only few teams arrive within 24-48 h postdisaster. The decision to deploy and the scale of deployment rely heavily on context and nature of the event and consequently a rapid assessment of needs/gaps is critical to an appropriate and customized response. Methods: In this study, we describe a desk-based study that provides: (1) knowledge about the medical needs that can be anticipated according to the phases of the disaster that is not rich in literature; and (2) a decision support framework for the deployment of EMTs to earthquakes that combines the results of a literature research and a Delphi study involving the opinion of 12 experts in the field. Results: The resulting framework is a tool that will help better mapping the configuration to the needs on the ground at the time the team becomes operational in the field and will assist those responsible for deploying and/or accepting EMTs in making informed decisions on deployment after an earthquake. Conclusions: With additional research the framework approach may be adapted to other types of international relief such as to deploy a Search And Rescue (SAR) team. [ABSTRACT FROM AUTHOR]

The current state of sustainability is promoting the status of the supply chain from traditional economic objectives related to the cost, quality, and time to the multidimensional opportunities in terms of economic, social and environmental fronts. The paper deals with the development of a decision support framework for the prioritization of suppliers on sustainability factors. The framework is based on the combined approach of the analytical hierarchical process (AHP) and the fuzzy inference system (FIS) to evaluate the supplier for the benefit of the manufacturer. The role of AHP is to select the significant factors as criteria, while the FIS mechanism works to measure the sustainability index of each supplier for prioritization from the combined effect of the selected factors. In the ranking process, experts' opinions on the importance of deciding the criteria (developed by the AHP) are considered in linguistic terms. To handle the subjectivity of decision makers assessments, fuzzy logic has been applied using FIS. In addition, uncertainties in the decision making support system are overcome by considering the fuzzy set theory for the selected sustainable factors. A numerical experiment is carried out to consider seven suppliers working with the goalkeeping gloves manufacturing firm for the pragmatic application of the proposed framework. The methodology of the integrated AHP–FIS approach is utilized to rank the suppliers by calculating the sustainability index value. The proposed approach provides a platform for the manufacturer to better understand the capability, sustainable suppliers must possess to continue working with them for the sustainable supply chain management. [ABSTRACT FROM AUTHOR]

In an effort to achieve sustainable operations, green supply chain management has become an important area for firms to concentrate on due to its inherent involvement with all the processes that provide foundations to successful business. Modelling methodologies of product supply chain environmental assessment are usually guided by the principles of life cycle assessment (LCA). However, a review of the extant literature suggests that LCA techniques suffer from a wide range of limitations that prevent a wider application in real-world contexts; hence, they need to be incorporated within decision support frameworks to aid environmental sustainability strategies. Thus, this paper contributes in understanding and overcoming the dichotomy between LCA model development and the emerging practical implementation to inform carbon emissions mitigation strategies within supply chains. Therefore, the paper provides both theoretical insights and a practical application to inform the process of adopting a decision support framework based on a LCA methodology in a real-world scenario. The supply chain of a product from the steel industry is considered to evaluate its environmental impact and carbon ‘hotspots’. The study helps understanding how operational strategies geared towards environmental sustainability can be informed using knowledge and information generated from supply chain environmental assessments, and for highlighting inherent challenges in this process. [ABSTRACT FROM PUBLISHER]

Refurbishment and remanufacturing are the industrial processes whereby used products or parts that constitute the product are restored. Remanufacturing is the process of restoring the functionality of the product or a part of it to “as-new” quality, whereas refurbishment is the process of restoring the product itself or part of it to “like-new” quality, without being as thorough as remanufacturing. Within this context, the EU-funded project RECLAIM presents a new idea on refurbishment and remanufacturing based on big data analytics, machine learning, predictive analytics, and optimization models using deep learning techniques and digital twin models with the aim of enabling the stakeholders to make informed decisions about whether to remanufacture, upgrade, or repair heavy machinery that is toward its end-of-life. The RECLAIM project additionally provides novel strategies and technologies that enable the reuse of industrial equipment in old, renewed, and new factories, with the goal of saving valuable resources by recycling equipment and using them in a different application, instead of discarding them after use. For instance, RECLAIM provides a simulation engine using digital twin in order to predict maintenance needs and potential faults of large industrial equipment. This simulation engine keeps the virtual twins available to store all available information during the lifetime of a machine, such as maintenance operations, and this information can be used to perform an economic estimation of the machine's refurbishment costs. The RECLAIM project envisages developing new technologies and strategies aligned with the circular economy and in support of a new model for the management of large industrial equipment that approaches the end of its design life. This model aims to reduce substantially the opportunity cost of retaining strategies (both moneywise and resourcewise) by allowing relatively old equipment that faces the prospect of decommissioning to reclaim its functionalities and role in the overall production system.

This paper introduces a new decision support framework for transport projects appraisal, which is based on the multicriteria decision analysis (MCDA) and the cost-benefit analysis (CBA) to address both monetary and non-monetary characteristics of the problem at the same time. Substantial inaccuracies are present while obtaining non-monetary and monetary inputs to the MCDA and the CBA, respectively. To provide the MCDA part of the framework with uncertainty, an interval-valued Pythagorean fuzzy set (IVPFS) is used as an attractive uncertain modeling tool. Additionally, a new multi-attribute group decision-making (MAGDM) method with partially known attribute weights and unknown expert weights under the IVPFS environment is proposed to find the best sustainable transport project from the non-monetary criteria point of view. Furthermore, a novel uncertainty modeling tool, namely an interval-valued Pythagorean trapezoidal fuzzy set, is introduced to address ill-known quantities and inaccuracies that exist in the estimation of monetary impacts. Finally, this framework is used to handle a real case study of a complex decision process of finding the best transport project to fund several candidate projects. This approach has made it possible to handle the decision process from economic, social, environmental points of view at the same time. The result of this case study shows that the decision-making framework can be used as a valuable decision support tool. Moreover, it is shown that the presented framework can be applied to effectively support appraisals of transport projects. [ABSTRACT FROM AUTHOR]

In the operational phase of public-private partnership (PPP) contracts, undue delay in addressing real needs may lead to poor service outcomes; conversely, commencing variations to a PPP agreement on the whim of end-user runs the risk of reducing the value created by detailed structuring and considerations undertaken in establishing agreement. This difficulty is exasperated as there is generally a lack of understanding by the end-user as to the specifics of service delivery performance requirements contracted. In order to address this question, this study, for the first time, develops a reliability-based decision support framework (RDSF) that incorporates end-user's perceived service quality (i.e., how satisfied it is with the space, operation and maintenance activities) with those specified in the PPP agreement, and further identifies when the gap between end-user's expectations and contractual obligations warrants reconsideration. This developed framework is then implemented to test the data gathered from three PPP schools in Australia based on both a current snapshot of performance data, i.e., abatements as gathered through contract documents and end-user's perception through in-depth interviews, and a projected scenario of the future as well. Reliability analysis used here compares time-dependent risk profiles of current and expected performance and thereby identifies major changes in a PPP contract that would sensibly require reconsideration. The specific results indicate there is no current difficulty between end-user's perception and the contract. However, the projected long-term scenario demonstrates how the decision framework can identify areas for review and changes if end-users are more dissatisfied with the service being achieved. The RDSF is capable of quantifying current service performance, considering the engagement of the end-user. Thus it enriches theories in the field of performance management system (PMS), and also contributes to knowledge regarding an evidence-based test for justifying possible agreement modifications or additional works in social PPPs operations. In addition, guidance for performance improvement strategies in aspects of the dissatisfied area is also provided. Application of this approach would assist in maintaining the long-term value for money of social infrastructure PPP agreements. [ABSTRACT FROM AUTHOR]

Sustainability assessments of flood mitigation projects are crucial for achieving sustainable development of floodplains. This article presents the application of an innovative sustainability assessment (SA) framework for flood mitigation projects throughout its life. The research employed a literature review, consultation with experts, and a case study of a flood mitigation project in Australia. The sustainability assessment framework includes five stages: (a) contextualizing the project; (b) SA at the planning and implementation stage; (c) SA during a flood event; (d) SA at regular intervals; and (e) SA during a change or modification phase. The results of the sustainability assessment at the first two stages of the flood mitigation project suggest how the sustainability index (SI) could be used to choose the best design options. Also, the study presents how the achievement toward sustainability of the finally constructed project could be compared with the planned project using the SI score. Sustainability assessment at Stages 3–5, carried out with possible scenarios, demonstrates that the project's sustainability could be hindered by the growing number of vulnerable population and property development in the floodplain without an upgrade of the project. The findings suggest the applicability of the SA framework for better decision‐making for sustainable flood risk management. [ABSTRACT FROM AUTHOR]

This paper investigates the link between off-shoring, postponement, and inventory. First, the reasons for and risks of off-shoring are stated. Then the benefits and costs of postponement are outlined, followed by how off-shoring and postponement impact inventory levels and an investigation into how the two can interact is provided. Following this the inventory impact of a set of scenarios encompassing various levels of postponement for an off-shore manufactured product are explored. Using a set of real-world data applied to a simplified product the benefits of each postponement in terms of total cost are examined. Finally, the best scenario is selected. In addition, a sensitivity analysis on the key variables is performed. Also, the fact that the combined uncertainty (due to performance cycle and demand) is the most influential input to each scenario is illustrated. A quick method for determining which term of the combined uncertainty equation will be dominant in altering the inventory level of a given strategy is identified. [ABSTRACT FROM AUTHOR]

Information technology project managers (IT PM) have a critical influence on IT project success while attracting and selecting the right IT PM is challenging. We followed a four-level research design and firstly developed a taxonomy as an input for a cluster analysis to identify patterns in IT PM job advertisements. Based hereon, we developed a decision support framework for IT PM recruitment. We evaluated our findings in an online survey. We identified multiple design elements for IT PM job advertisements within five perspectives and deduced five IT PM archetypes. The decision support framework uses five questions to assist IT PM recruitment. We expand the knowledge base and consider not only IT PM requirements but also benefits. Our decision support framework is the first to holistically support IT PM recruitment, supports recruitment managers in structuring job interviews, identifies potential matches between applicants and recruiters, and assists in the final selection., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products' aspects along with customers' sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4% precision. [ABSTRACT FROM AUTHOR]

The objectives of this research were 1) to analyze factors that influence consumer behavior on healthy beverages for an aging society and 2) to develop a consumer behavior model and decision support framework on healthy beverages for an aging society. Qualitative and quantitative approaches were used to collect data. The research tools were interview questions and questionnaires. The consultants for the in- depth interview were experts in healthcare and the beverage industry. The participants who answered the questionnaires were seniors who live in city and urban areas around Thailand. The data were analyzed using structural equation modeling. The results showed that the influential factors of consumer behavior on healthy beverages were composed of personal factors and psychological factors. Personal factors have a direct influence on psychological factors with a coefficient of 0.93 at a significance level of 0.05. Psychological factors have a coefficient to consumer behavior on healthy beverages of 0.84 at a significance level of 0.05. [ABSTRACT FROM AUTHOR]

Abstract In todays' dynamically changing environment and competitive landscape, organisations are adopting sustainable practices for attaining long-term economic viability. However, there is a misalignment between sustainable practices and organisations' strategies and capabilities, especially when sustainability requirements of the stakeholders changes over time. Grounded in dynamic capability view (DCV), this paper addresses the changes in supply chain sustainability requirements of stakeholders in the context of sourcing products from apparel manufacturers in a low cost country Bangladesh. To this end, this study develops a decision support (DS) framework for supply chain sustainability (SCS) that identifies and prioritises optimal strategies for SCS in a dynamic environment. This study adopts a mixed method approach, with the qualitative approach being a field study, and the quantitative approach using fuzzy Quality Function Deployment (QFD) integrated optimisation technique. Our DS framework addresses the stakeholders' sustainability requirements over time in the context of a case company. The findings show that concomitant with the changes in the stakeholders' priorities of the sustainability requirements, the organisational sustainability practices, strategies and capabilities also change over time. The SCS DS framework brings a richer conceptual understanding of the dynamic changes in stakeholder requirements and allow managers to choose and select optimal strategies and make astute decisions whilst balancing the economic, social and environmental viability simultaneously. [ABSTRACT FROM AUTHOR]

Abstract In recent years, a need for product traceability in the cold supply chain has emerged. The purpose of this study was to identify and map out different kinds of identification technologies and techniques used for cold supply chain traceability. This was done by looking into what traceability solutions are available right now through literature review. The results from this review were then further analyzed to obtain a basis for the current state of knowledge, technical solutions and to identify possible traceability structures in the cold chain. A Decision Support Framework (DSF) was constructed for choosing a suitable technical solution. It consists of a table listing different functions and attributes of technologies and a decision-tree. The DSF created from this work will help the user to identify what kind of traceability technology and structure best suits his products. This is important, as it can often be difficult for the user to decide which technology is most beneficial for his company. That is why this decision support framework will enable him to decide what is technologically feasible, practical, economical, can sustain reputation, quality and safety of the products. Highlights • Overview of current traceability technologies • A framework leading the user to identify the most suitable traceability technology • When the technology is selected, identify further needs of that technology [ABSTRACT FROM AUTHOR]

The construction of domestic sewage treatment facilities plays a prominent role in solving rural water pollution and thus keeping the environment cleaner. Considering the applicability of the technology, the selection based on the sewage treatment decision framework is necessary. Although there are various sewage treatment technologies (STTs) available, the decision frameworks that adapt to local conditions for different rural areas are limited. This study proposed a new decision support framework for the suitability selection of rural domestic STTs. It integrated the exploratory factor analysis (EFA), the mathematical model of quantitative relationship (MMQR), and the gray relational analysis (GCA) into the framework. EFA was applied to construct different application scenarios, quantitative relationships between scenarios and technical applications were established through MMQR, and GCA was used to identify the correlation between different standards. The framework has been tested and validated on real case studies in rural areas of the Yangtze River Economic Belt (YREB). The results clearly explain the technology selection process and justify the framework's user-friendliness and reliability in case studies. The proposed framework can provide scientific decision-making support for the selection of alternative technologies for rural sewage treatment in the YREB and also offer help for relevant research in other areas with unbalanced economic development. [Display omitted] • A decision framework for technical alternatives adapted to local conditions. • Solved the problem of indicators redundancy by EFA. • Constructed the correlations among different regional emission standards by GCA. • Case verification confirms that the framework works well. [ABSTRACT FROM AUTHOR]

In the operational phase of public-private partnership (PPP) contracts, undue delay in addressing real needs may lead to poor service outcomes; conversely, commencing variations to a PPP agreement on the whim of end-user runs the risk of reducing the value created by detailed structuring and considerations undertaken in establishing agreement. This difficulty is exasperated as there is generally a lack of understanding by the end-user as to the specifics of service delivery performance requirements contracted. In order to address this question, this study, for the first time, develops a reliability-based decision support framework (RDSF) that incorporates end-user’s perceived service quality (i.e., how satisfied it is with the space, operation and maintenance activities) with those specified in the PPP agreement, and further identifies when the gap between end-user’s expectations and contractual obligations warrants reconsideration. This developed framework is then implemented to test the data gathered from three PPP schools in Australia based on both a current snapshot of performance data, i.e., abatements as gathered through contract documents and end-user’s perception through in-depth interviews, and a projected scenario of the future as well. Reliability analysis used here compares time-dependent risk profiles of current and expected performance and thereby identifies major changes in a PPP contract that would sensibly require reconsideration. The specific results indicate there is no current difficulty between end-user’s perception and the contract. However, the projected long-term scenario demonstrates how the decision framework can identify areas for review and changes if end-users are more dissatisfied with the service being achieved. The RDSF is capable of quantifying current service performance, considering the engagement of the end-user. Thus it enriches theories in the field of performance management system (PMS), and also contributes to knowledge regarding an evidence-based test for justifying possible agreement modifications or additional works in social PPPs operations. In addition, guidance for performance improvement strategies in aspects of the dissatisfied area is also provided. Application of this approach would assist in maintaining the long-term value for money of social infrastructure PPP agreements.