Decision-making Framework Research Articles

Peer Review of “A Framework for Modeling, Analyzing, and Decision-Making in Disease Spread Dynamics and Medicine/Vaccine Distribution (Preprint)”

Peer Review of “A Framework for Modeling, Analyzing, and Decision-Making in Disease Spread Dynamics and Medicine/Vaccine Distribution (Preprint)”

Leveraging online reviews and expert opinions for electric vehicle type prioritization

The rapid expansion of the electric vehicle (EV) market in recent years has presented manufacturers with the challenge of strategically prioritizing the types of EVs in which to invest under uncertain conditions. This study proposes an enhanced multi-attribute decision-making (MADM) framework to address this issue by leveraging online reviews and expert opinions. The proposed framework combines a novel linguistic representation, called calibrated basic uncertain linguistic information (CBULI), to capture uncertainty, a value function from cumulative prospect theory (CPT) with double reference points to model the manufacturers’ psychological preferences, and the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) for prioritizing EVs for investment. It extracts demand attributes from online reviews, applies CBULI to represent uncertain evaluations, and incorporates CPT to capture risk preferences. A case study of an EV manufacturing enterprise in Sichuan, China, was conducted to validate our framework, and the results demonstrated its effectiveness and practicability in identifying the most promising types of EVs for investment. The results of sensitivity and comparative analyses further confirmed the robustness and superiority of the model in comparison with prevalent methods. The work here contributes to methodological advancements in research on the choice of types of EVs in which to invest, and provides valuable insights for EV enterprises to make informed investment-related decisions that are aligned with user demands and enterprise development. The proposed MADM framework supports the strategic development of the EV industry by enabling manufacturers to prioritize investment in the appropriate types of EVs under uncertainty.

Developing and evaluating a risk-informed decision support system for earthquake early warning at a railway bridge

Earthquake early warning (EEW) systems provide timely information on the arrival of strong seismic waves at a site. Such information can help mitigate the consequences of earthquakes on the operation of infrastructure assets. EEW-informed mitigation actions should stem from risk-based decision-making protocols, and EEW benefits should be evaluated for the range of possible rupture scenarios that affect an asset of interest. This paper addresses these challenges specifically for railway bridges by: (1) developing a risk-informed EEW decision support system (DSS) for these assets; and (2) quantifying the effectiveness of the proposed EEW-DSS in mitigating seismic risks for railway bridges across relevant rupture scenarios. The proposed EEW-DSS combines information on site-specific seismic hazard, time-dependent EEW algorithm outputs, probabilistic seismic demand modeling, damage/derailment fragilities, and seismic loss models. These modeling components compose a multi-criteria decision-making framework. Value of information theory is then proposed to estimate the loss-mitigation benefits of the EEW-DSS, accounting for varied stakeholder risk priorities as well as dynamic lead-time/accuracy tradeoffs related to EEW performance. A railway bridge with multiple piers is adopted for the case study, which demonstrates the importance of risk-based, uncertainty-informed decision-making to the overall effectiveness of EEW in potentially reducing seismic losses.

Holistic approach for selecting chatbot development tools: combining AHP and TOPSIS methodologies

ABSTRACT The proliferation of chatbot development tools presents a significant challenge in identifying the most suitable ones for specific needs. This study addresses this issue by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to create a comprehensive methodology for selecting chatbot development tools. The study aims to provide a transparent, objective decision-making framework by evaluating tools across multiple dimensions including deployment characteristics, input processing capabilities, dialogue management, response generation, and economic considerations. Utilizing AHP for criteria weighting and TOPSIS for ranking alternatives, the methodology systematically evaluates various tools to identify the optimal choice. Key findings highlight the strengths and weaknesses of different tools, offering insights that guide developers and organizations in choosing the most suitable chatbot development tool aligned with their specific needs and strategic goals. The implications of this methodology are significant, enhancing decision-making processes within technological tool selection and contributing to the fields of artificial intelligence and automated systems development.

Service referral decisions for children involved in the child welfare system: modeling the decision-making ecology framework using latent profile analysis

ABSTRACT This paper empirically tests the Decision-Making Ecology (DME) framework to explore which characteristics of child maltreatment cases are most influential in the decision to refer children to services. We used latent profile analysis (LPA) to identify latent groups of child welfare organizations based on their climate and culture. We then use logistic regression models to determine whether organizational, external, case, and caseworker characteristics were predictive of children’s service referrals. Our findings revealed that organizational characteristics were a significant predictor of service referrals, as well as caseworker and external characteristics. These results support the DME framework in understanding service referral decisions.

A scenario-based decision framework for the order promising process in hybrid MTS/MTO production systems considering product substitution

ABSTRACT Considering the increasing competition among manufacturers and the diversification of customer orders, it is important to use the optimal production system. Hybrid manufacturing systems have proven to be a viable solution to achieve a satisfactory level of customization while maintaining adequate production volumes. This article determines the optimal strategy for decision makers in order to choose the appropriate method of supplying customers’ orders. In this article, a hybrid MTS-MTO production system has been studied in two different scenarios. For this purpose, a mixed-integer linear bi-objective model with product substitution and replaceable materials has been developed and solved by a Lagrangian Relaxation algorithm. The findings of this study reveal that enabling product substitution by manufacturers leads to an increase in the number of accepted orders, along with resulting in a higher average level of customer dissatisfaction. The system prioritizes the fulfillment of orders from permissive customers. Also, with the increase of uncertainty in each of the supplying methods, manufacturers are using other sources to supply raw materials to satisfy the accepted orders.

Salinity tolerance of the invasive blue crab Callinectes sapidus: From global to local, a new tool for implementing management strategy

The latest report from the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) raises concerns about the global proliferation of non-native species (NIS) driven by increasing temperatures. In 2023, the invasive blue crab Callinectes sapidus experienced a significant range expansion in the French Mediterranean lagoons, now present in over 20 lagoons and numerous estuaries with different invasion gradients. It has been established that this species is eurytherm (tolerates a wide range of temperatures; 0–40 °C), rendering temperature a non-limiting factor for its proliferation. On the other hand, salinity is a critical factor to consider, as C. sapidus requires brackish water for copulation and saline water for egg maturation. In this study, we present the salinity tolerance of C. sapidus using the metabolic performance to define the tolerance of the species to a large range of salinities (13 levels from 0 to 65 psu). Results showed that C. sapidus tolerates a large range of salinities with a minimum critical threshold (CTmin) at 0 psu, an optimum at 18.5 psu, and a CTmax at 62.4 psu. Using the performance curve equation, we applied the curve to real salinity data measured each month in 2023 in 20 lagoons invaded. Using this approach, we were able to map, on a fine spatial scale, the distribution of suitable habitats for C. sapidus in the year 2023. Additionally, we have developed a tool to prioritize the lagoons based on their suitability, thereby providing a decision-making framework for management measures in each lagoon. It emerged that 50 % of lagoons were priority areas, 10 % were intermediate and 40 % required monitoring. On the basis of this approach, we can respond to European regulations to help decision-makers implement action plans and/or early detection programs for invasive species management.

Sustainable management of polyethylene terephthalate waste flow using a fuzzy frank weighted assessment model

The consequences for the ecosystem of polyethylene terephthalate (PET) waste are becoming increasingly significant and widespread. Companies managing PET waste strive to enhance sustainability in all areas. The development of systematic decision-making approaches and frameworks for PET waste management is strongly needed. This research aims to present a new methodological framework for the categorization of the most efficient PET waste management solutions. The introduced fuzzy Frank weighted sum product assessment (FWESPA) model enables rational and flexible reasoning by nonlinearly processing uncertain information. A nonlinear aggregation function is proposed for the fusion of fuzzy strategic options. It is advantageous in simulating the impact of strategic options on a final decision. An integral part of the introduced fuzzy FWESPA model is a reverse sorting algorithm. This innovative algorithm can improve the performance of traditional normalization techniques. Also, an improved fuzzy Frank ordinal priority approach linear model is formulated to define the significance of evaluation criteria. The comprehensive real-life study demonstrates the proposed decision-analytics-based approach. The results showed the following rankings of considered alternatives: “recycling” (ℤA2 = 0.8565) > “energy recovery” (ℤA1 = 0.7364) > “remanufacturing” (ℤA4 = 0.690) > “incineration” (ℤA3 = 0.6592). Based on the results presented, alternatives “recycling” (A2) and “energy recovery” (A1) represent dominant alternatives with a slight advantage of recycling. Research findings can be used when deciding the appropriate way to enhance PET waste handling. The findings also describe the benefits and limitations of each treatment option for PET waste, as well as highlight the crucial challenges.

Remote data collection of infant activity and sleep patterns via wearable sensors in the HEALthy Brain and Child Development Study (HBCD)

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Wearable and remote sensing technologies have advanced data collection outside of laboratory settings to enable exploring, in more detail, the associations of early experiences with brain development and social and health outcomes. In the HBCD Study, the Novel Technology/Wearable Sensors Working Group (WG-NTW) identified two primary data types to be collected: infant activity (by measuring leg movements) and sleep (by measuring heart rate and leg movements). These wearable technologies allow for remote collection in the natural environment. This paper illustrates the collection of such data via wearable technologies and describes the decision-making framework, which led to the currently deployed study design, data collection protocol, and derivatives, which will be made publicly available. Moreover, considerations regarding actual and potential challenges to adoption and use, data management, privacy, and participant burden were examined. Lastly, the present limitations in the field of wearable sensor data collection and analysis will be discussed in terms of extant validation studies, the difficulties in comparing performance across different devices, and the impact of evolving hardware/software/firmware.

A novel hybrid decision-making framework based on modified fuzzy analytic network process and fuzzy best–worst method

A novel hybrid decision-making framework based on modified fuzzy analytic network process and fuzzy best–worst method

Rough set theory-based multi-class decision-making framework for cost-effective treatment

Rough set theory-based multi-class decision-making framework for cost-effective treatment

Circular economy strategies: a fuzzy DEMATEL decision framework for the fast fashion footwear manufacture

The fast fashion industry significantly impedes sustainability by its rapid production cycles, low-cost products, and heavy carbon footprint. Nowadays, circular economy (CE) has become an interesting concept, which follows the “take-make-dispose” model and allows products, materials, and resources to be in use as long as possible. There is a paucity of research in the identification of circular economy strategies (CES) and mapping them based on their interrelationships, which is very important from a managerial perspective. In this vein, this study identifies nine CES and analyzes their causal relationships using the Fuzzy Decision-Making Trial and Evaluation Laboratory method in the context of fast fashion footwear manufacture. The key contributions include providing actionable insights for implementing CE practices, enhancing the sustainability of the footwear industry, and offering a strategic roadmap for industry stakeholders. The results and implications of this study will guide footwear industry decision-makers in making a strategic map considering the cause-and-effect interrelationships among the CES for implementing CE business practices in the footwear industry.

The effect of incorporating infectious disease dynamics into a social cost–benefit framework for COVID-19 policy decisions

In ‘A social cost–benefit framework for COVID-19 policy decisions', Heatley proposes a framework for cost–benefit analyses for policy decisions regarding COVID-19 responses. He illustrates this in a worked example, considering the New Zealand Cabinet’s decision in April 2020 to extend alert level 4 restrictions by five days. In his analysis he compares the extension to a counterfactual scenario where alert level 3 is resumed immediately, finding that this scenario outperforms the extension made by Cabinet. However, using a simple extension of Heatley’s method that better captures the stochastic dynamics towards the end of an outbreak, I find the opposite: Cabinet’s decision significantly outperforms Heatley’s counterfactual. I conclude that an improved treatment of disease dynamics better describes the costs and benefits of the policy choices facing Cabinet in April 2020. This improved framework or similar may be of use to future decision-makers.

Next-generation data center energy management: a data-driven decision-making framework

In the era of society’s ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, Decision, and Action) loop and based on insights from an Ericsson-operated DC in Linköping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs’ building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency.

Does the decision to disclose non-suicidal self-injury align with decision-making frameworks of personal information disclosure? A directed content analysis.

Non-suicidal self-injury (NSSI) is the deliberate damage caused to one's own body tissue, without the intent to die. Voluntary disclosure of one's NSSI can catalyze help-seeking and provision of support, although what informs the decision to disclose NSSI is not yet well understood. There is currently no existing framework specific to the process of NSSI disclosure, and the aim of this study was to assess the fit between factors involved in the decision to disclose NSSI and two broader frameworks of disclosure: the Disclosure Decision-Making and Disclosure Processes models. A directed content analysis was used to code interview transcripts from 15 participants, all of whom were university students aged between 18 and 25 (M = 20.33, SD = 1.88), with 11 identifying as female. All participants had lived experience of NSSI which they had previously disclosed to at least one other person. All codes within the coding matrix, which were informed by the disclosure models, were identified as being present in the data. Of the 229 units of data, 95.63% were captured in the existing frameworks with only 10 instances being unique to NSSI disclosure. Though factors that inform the decision to disclose NSSI largely align with the aforementioned models of disclosure, there are aspects of disclosure decision-making that may be specific to NSSI.

Multiple energy planning in the energy hub considering renewable sources, electric vehicles and management in the daily electricity market with wind multi-objective optimization algorithm

This article presents a decision-making framework leveraging randomness to manage short-term intelligent energy hub (EH) within regenerated power systems integrating EH input electricity and natural gas. EH outputs, addressing electricity and heat needs, are managed via battery storage systems (BSS) and electric vehicle (EV) fleets in regulation markets. The energy hub operator optimizes decisions concerning natural gas and energy, minimizing costs in electricity supply and thermal energy carriers across day-ahead (DA) markets and regulations. Emphasizing the benefits of demand-side management planning in cost reduction, the model incorporates a conditional value at risk (CVaR) method to assess and manage uncertainties. Moreover, leveraging electric vehicle battery storage capacity is explored to mitigate wind and solar energy production uncertainties. An energy optimization strategy based on a Multi-Objective Wind-Driven Optimization (MOWDO) is proposed, demonstrating the effectiveness of the model and method across various system conditions and scenarios. Results indicate that increasing the number of EVs from 2000 to 10,000 reduces the expected cost by 1.5 % and the CVaR by 1.5 %. Additionally, optimizing the BSS discharge cost coefficient from $45/MWh to $15/MWh decreases the expected cost by 0.5 % and the CVaR by 0.3 %. The system achieves a 20 % reduction in peak-hour electricity purchases by leveraging the BSS and EV fleet during high price conditions. These findings underscore the positive impact of demand-side management programs in enhancing renewable resource utilization amidst uncertainty.

Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.

Evaluating environmental and economic benefits of using biochar in concrete: A life cycle assessment and multi-criteria decision-making framework

While several studies have assessed the applicability of using biochar in cement mortar and concrete preparation based on the best mechanical properties, this evaluation method should be improved to consider the techno-economic feasibility and the environmental impacts. To address this research gap, this study attempts to compare between the conventional concrete with 100 % cement as a control mixture (CM) and dumping of sludge into open sites (scenario_1), and biochar concrete using alum sludge biochar (ASB) as a partial cement replacement (scenario_2), regarding the project life cycle assessment (LCA) and economic feasibility. A multi-criteria decision-making (MCDM) framework was used to assign scores to the environmental LCA criteria, material functional performance tests, and cost considerations. The two scenarios were arranged in SimaPro LCA software, and then the different criteria were analyzed using the TOPSIS technique to choose the best alternative. The study demonstrated that the functional properties of the cement mixture containing 5 % of ASB were almost comparable to those of CM, owing to the presence of significant amounts of quartz (SiO2) that bonded strongly with cement molecules. This ASB mixture also enjoyed the least environmental impacts, reducing the global warming potential (GWP) by 32.2 % compared with the CM scenario. The scores of the “Cost” criterion, considering the price of raw materials, energy, production, and maintenance, for the two alternatives were almost comparable, depicting that scenario_2 achieved the highest closeness coefficient of 0.95 in the MCDM overall scoring. This study concluded that biochar could be incorporated into cementitious mixtures due to its lower impact on global warming to maintain a sustainable and economic concrete industrial strategy. Future studies should focus on studying the long-term durability of the biochar-based concrete mixtures and integrating into the MCDM strategy; in addition, determining the correlation between sustainable construction implementation and sustainable development goals achieved.

A decision-making framework to maximise the evolutionary potential of populations - Genetic and genomic insights from the common midwife toad (Alytes obstetricans) at its range limits

Anthropogenic habitat modification and climate change are fundamental drivers of biodiversity declines, reducing the evolutionary potential of species, particularly at their distributional limits. Supportive breeding or reintroductions of individuals are often made to replenish declining populations, sometimes informed by genetic analysis. However, most approaches utilised (i.e. single locus markers) do not have the resolution to account for local adaptation to environmental conditions, a crucial aspect to consider when selecting donor and recipient populations. Here, we incorporate genetic (microsatellite) and genome-wide SNP (ddRAD-seq) markers, accounting for both neutral and putative adaptive genetic diversity, to inform the conservation management of the threatened common midwife toad, Alytes obstetricans at the northern and eastern edges of its range in Europe. We find geographically structured populations (n = 4), weak genetic differentiation and fairly consistent levels of genetic diversity across localities (observed heterozygosity and allelic richness). Categorising individuals based on putatively adaptive regions of the genome showed that the majority of localities are not strongly locally adapted. However, several localities present high numbers of private alleles in tandem with local adaptation to warmer conditions and rough topography. Combining genetic diversity and local adaptations with estimates of migration rates, we develop a decision-making framework for selecting donor and recipient populations which maximises the geographic dispersal of neutral and putatively adaptive genetic diversity. Our framework is generally applicable to any species, but especially to amphibians, so armed with this information, conservationists may avoid the reintroduction of unsuitable/maladapted individuals to new sites and increase the evolutionary potential of populations within species.

Public Health Policy and Advocacy: A Management Perspective

The division of authority and accountability between the federal government and the states is one of the public health governance system's most significant advantages and concurrent problems. The policy management suggests that the best course of action is to develop institutional frameworks for consultative decision-making, allocate resources in an equitable manner, and implement policies in a coordinated manner. The proposed methods include improved fiduciary risk management, capacity building, technical support for state-specific strategic plans, active local self-government engagement, and community-based health output monitoring. The idea put forth in this paper is that HR rules, which are centred around the requirement that individuals with a background in public health management hold high positions in the field, should be tightened.