Systematic Decision-making Process Research Articles

A sustainable network design for municipal solid waste management considering waste-to-energy conversion under uncertainty

This research proposes a sustainable municipal solid waste management (MSWM) system to address population growth, consumerism, and resource scarcity. It introduces a two-phase decision structure combining multi-attribute decision-making (MADM) tools. Phase one develops a hybrid risk-oriented fuzzy MADM tool for selecting optimal waste-to-energy technologies, considering environmental and technological factors. Phase two uses a fuzzy bi-objective multi-period mixed-integer linear programming model to design the MSWM supply chain efficiently under uncertainty. A case study in North Tehran demonstrates the framework’s practical applicability and effectiveness in real-world scenarios, highlighting that effective source separation boosts recycling, reduces costs, and provides social benefits. Sensitivity analyses offer insights to enhance waste segregation practices. The study emphasizes integrating economic, environmental, and social sustainability in waste management decision-making. By offering a novel, holistic approach, this research addresses existing gaps in systematic decision-making processes and provides a robust tool for municipalities to optimize strategies under uncertainty.

Integrating socioeconomic and ecological data into restoration practice.

Driven by the United Nations Decade on Restoration and international funding initiatives, such as the Mangrove Breakthrough, investment in mangrove restoration is expected to increase. Yet, mangrove restoration efforts frequently fail, usually because of ad hoc site-selection processes that do not consider mangrove ecology and the socioeconomic context. Using decision analysis, we developed an approach that accounts for socioeconomic and ecological data to identify sites with the highest likelihood of mangrove restoration success. We applied our approach in the Biosphere Reserve Marismas Nacionales Nayarit, Mexico, an area that recently received funding for implementing mangrove restoration actions. We identified 468 potential restoration sites, assessed their restorability potential based on socioeconomic and ecological metrics, and ranked sites for implementation with spatial optimization. The metrics we used included favorable conditions for propagules to establish and survive under sea-level rise, provision of ecosystem services, and community dynamics. Sites that were selected based on socioeconomic or ecological metrics alone had lower likelihood of mangrove restoration success than sites that were selected based on integrated socioeconomic and ecological metrics. For example, selecting sites based on only socioeconomic metrics captured 16% of the maximum attainable value of functioning mangroves able to provide propagules to potential restoration sites, whereas selecting sites based on ecological and socioeconomic metrics captured 46% of functioning mangroves. Our approach was developed as part of a collaboration between nongovernmental organizations, local government, and academics under rapid delivery time lines given preexisting mangrove restoration implementation commitments. The systematic decision process we used integrated socioeconomic and ecological considerations even under short delivery deadlines, and our approach can be adapted to help mangrove restoration site-selection decisions elsewhere.

Enhancing healthcare supply chain management through artificial intelligence-driven group decision-making with Sugeno–Weber triangular norms in a dual hesitant q-rung orthopair fuzzy context

The healthcare industry faces numerous challenges in managing its supply chain efficiently, where critical decisions must be made promptly to ensure the availability of essential medical resources. This research introduces a novel artificial intelligence (AI) approach, utilizing the “Sugeno–Weber (SW) t-conorms and t-norms” (SWt-CNs&t-Ns) for decision-making in a Dual Hesitant q-Rung Orthopair Fuzzy (DHq-ROF) context. The SWt-CNs&t-Ns are chosen for their adaptability in data unification, serving as prominent operations for union and intersection processes. Developing a set of fundamental operations is imperative to effectively utilize SWt-CNs&t-Ns and hybrid aggregation operators in DHq-ROF settings. Following the introduction of these processes, several aggregating operators have been provided. These operators include DHq-ROF SW weighted averaging, ordered weighted averaging, hybrid averaging, and their geometric counterparts utilizing DHq-ROF data. The SW triangular norm-based approach aggregates group preferences, facilitating a systematic decision-making process. Triangular norms ensure a realistic representation of interrelationships among decision criteria, leading to optimal healthcare supply chain management solutions. Furthermore, the SW triangular norm-based approach aggregates group preferences, enabling a systematic and comprehensive decision-making process. Choosing the best healthcare supply chain management solutions is easier when you use triangular norms because they give a more accurate picture of how the decision criteria affect each other. The effectiveness of the proposed AI framework is demonstrated through a series of experiments and case studies, showcasing its ability to enhance decision accuracy, reduce uncertainty, and improve overall supply chain performance. The research findings underscore the potential of AI-driven solutions to revolutionize healthcare supply chain management, ultimately leading to better resource allocation, cost efficiency, and improved patient care.

The Food-Energy-Water Nexus: Assessing undergraduate students’ decision making about complex socio-hydrologic issues supported by Hydroviz

The Food-Energy-Water (FEW) Nexus envisions interactions between water, energy, and food systems, in which water allocation decisions constitute complex socio-hydrologic issues. Undergraduate students need to develop skills to understand and make effective decisions about the FEW Nexus. We implemented a comprehensive curriculum where undergraduate students engaged in a systematic decision-making process around a FEW Nexus challenge after collecting and processing information from an online data visualization tool—Hydroviz. We ask (a) what components of the decision-making process were students able to engage in most effectively, and (b) what factors (e.g., students’ demographics, prior knowledge, use of Hydroviz, and curricular resources, among others) support students’ overall decision-making outcomes? The study is based on a mixed-methods approach in which we analyzed n = 94 students’ responses to a decision-making scenario, and n = 13 semistructured interviews from the second iteration of the module in 2021. Results suggest that students may need additional support to better frame the decision-making problem to provide a more in-depth and localized description, including their use of collected evidence to support their description. Factors that supported students’ decision-making outcomes include (a) their personal values and experiences and (b) scaffolding elements of the module, in which they were able to decompose the elements of the problem, prioritize different values in their analysis, and question their initial approaches to further analyze their choices. More information about the energy sources, and the impacts on the community and environment, would be beneficial. No differences between demographic groups were observed. The study can provide insights to enhance instructors’ use of the curricular resources and curricular design, thereby supporting undergraduate instruction to foster students’ engagement in decision making about socio-hydrologic issues.

Understanding Early Intervention and Early Childhood Special Education Professional Access to and Utilization of Evidence-Based Practices

ABSTRACT Federal legislation and regulations addressing educational practices have provided clear mandates that educators when designing educational intervention use systematic decision-making processes to identify instructional strategies and methods that are evidence based. This study employed a survey to examine how familiar early interventionist/early childhood educators, who serve young children aged birth to eight, seek information on evidenced -based practices (EBP) prior to implementing a practice. This study also explored methods that educators use and prefer to access information about EBPs. A total of 223 educators completed the survey that included four identified subgroups (teachers, related service providers, administrators, and faculty/researchers/trainers). We found that most respondents, across the groups, reported being familiar or somewhat familiar with research evidence for their practices and chose discussion with colleagues as the most frequently used strategy to learn and make decisions about a new practice. Implications for future research and professional development are discussed.

Comparison of Using Modified and Nonmodified Books on Comprehension of Students With Extensive Support Needs

Modified books are frequently used as comprehension supports for students with extensive support needs (ESN), despite limited evaluations of their creation process or impact on comprehension. This study evaluates the impact of individualized book and comprehension question modifications made through a systematic decision-making process on student comprehension during nonfiction shared book reading in general education classes. A repeated acquisition single-case research design was used to compare student comprehension of nonmodified and modified books for one first-grade and one fourth-grade student with autism and moderate intellectual disability. Findings indicate student comprehension was higher after reading modified grade-level, nonfiction books compared with similarly matched nonmodified books. These findings show students with ESN can access grade-level books in general education settings with individualized modifications. Recommendations for research and practice are shared. These recommendations include a systematic process for identification of the type and intensity of book modifications that may support comprehension for students with ESN.

A decision model for international market selection by entrepreneurs: a cross-country investigation

PurposeThis study aims to examine the international market selection process of entrepreneurs operating internationally.Design/methodology/approachFour small and medium-sized comparative and rich-information case studies were purposefully selected from among Australian and Arabian firms. Data were collected via in-depth personal interviews, follow-up interviews and questionnaire instrument.FindingsThe results revealed that entrepreneurs used a four-stage systematic decision-making process to attain profitable foreign market choices. The decision process was influenced by cognitive boundaries as entrepreneurs relied on the availability experiential, anchoring and adjustment heuristic.Research limitations/implicationsThe research’s findings and the proposed decision model will, significantly, assist entrepreneurs, willing to expand internationally, in enhancing their decision-making to attain profitable foreign market choices. Further, it provides benefits to foreign investment policymakers in host countries by assisting them to attract more inward foreign direct investments, and, accordingly, enhance the economic and social development movement in their countries.Originality/valueThis study provides a significant theoretical contribution to the literature on the internationalization process of entrepreneurs and small- and medium-sized enterprises through developing a decision model for selecting and entering foreign markets by entrepreneurs in a cross-country context. Further, the study provides significant methodological contributions with regard to the effectiveness of the qualitative case study method in capturing elements of the foreign market selection process.

A novel bionic decision-making mechanism for digital twin-based manufacturing system

As an innovative smart manufacturing system, the virtual entities-based decision-making process is the most typical difference between the digital twin-based manufacturing system (DTMS) and other smart manufacturing systems. Therefore, the accuracy of virtual entity-driven decision-making is the key to affecting the system reliability of the DTMS. Normally, the manufacturing process is often accompanied by complex state changes, which are collected by the perception module of the DTMS in the form of high-dimensional information. Then, the decision-making model needs to respond to these state changes in real-time and give reasonable decision results back to physical space, which has become an important scientific issue of DTMS. To fill this gap, a novel bionic decision-making mechanism for DTMS is put forward by introducing the biological sequential learning mechanism into the decision-making process. Subsequently, the systematic decision-making process imitates biological instinct and learning behavior mechanisms to explore the short-term and long-term process of decision-making. The bionic decision-making mode formed by combining the above two modes provides adaptive decision-making in different scenarios. It is believed that the bionic decision-making mechanism can help to quickly and accurately give decision-making feedback to guide on-site manufacturing and ensure product quality and manufacturing efficiency.

An update on drug-drug interactions for care of the acutely ill in the era of COVID-19.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To provide key pharmacological concepts underlying drug-drug interactions (DDIs), a decision-making framework, and a list of DDIs that should be considered in the context of contemporary acutely ill patients with COVID-19. DDIs are frequently encountered in the acutely ill. The implications of DDIs include either increased risk of drug toxicity or decreased effectiveness, which may have severe consequences in the acutely ill due to lower physiological and neurocognitive reserves in these patients. In addition, an array of additional therapies and drug classes have been used for COVID-19 that were not typically used in the acute care setting. In this update on DDIs in the acutely ill, we provide key pharmacological concepts underlying DDIs, including a discussion of the gastric environment, the cytochrome P-450 (CYP) isozyme system, transporters, and pharmacodynamics in relation to DDIs. We also provide a decision-making framework that elucidates the identification of DDIs, risk assessment, selection of alternative therapies, and monitoring. Finally, important DDIs pertaining to contemporary acute care clinical practice related to COVID-19 are discussed. Interpreting and managing DDIs should follow a pharmacologically based approach and a systematic decision-making process to optimize patient outcomes.

Developing a Decision-Making Process of Location Selection for Truck Public Parking Lots in Korea

As the number of truck registrations and the traffic volume have increased continuously in urban areas, the problem of truck illegal parking on the roadside has become more serious in Korea. Although the government presented the necessity and the construction plan of the truck public parking lots through the ‘4th Comprehensive Plan for Expansion of Truck Resting Facilities’, there is no detailed guideline for the local government of where to construct them. The previous studies on the location selection of the parking lots have mainly focused on passenger cars and buses, but not trucks, and the process for candidate location selection was not mentioned. To fill this gap, this study presents a decision-making process for the location selection of truck public parking lots in urban areas based on mixed-integer programming; it includes a candidate location selection by spatial analysis and an optimal location determination by the application of the competitive p-median algorithm. A case study of Incheon was conducted to validate the presented decision-making process. This study introduced a systematic decision-making process that includes standards establishment, data processing, and methodology application; it is universalistic enough to be utilized as a guideline for the government to efficiently construct truck public parking lots.

Additive vs. Conventional Manufacturing of Metal Components: Selection of the Manufacturing Process Using the AHP Method

Additive manufacturing (AM) processes have experienced significant technological developments over the past decade. Today, 3D-printed metal parts can almost achieve the mechanical properties of conventionally manufactured components; process times have been shortened, and the range of available materials has been widely expanded. The decision between conventional manufacturing and AM is therefore becoming more complex, considering technical and economic criteria along the entire product life cycle. To reflect the vision of the manufacturer, each decision needs to be based on individual preferences and strategies. The present research introduces a standardised and systemised multi-criteria decision-making process to choose between additive and conventional production. Multi-criteria decision models from within the literature are analysed and a holistic decision matrix is developed based on the analytic hierarchy process (AHP). The key novelty of the present research is the consideration of technical and economic categories along the whole product life cycle for decision making. The matrix allows an individual weighting of individual criteria along the product life cycle, starting with the conceptualisation of the product, and ending with marketing and after-sales. The approach is evaluated using two scenarios, including a control unit housing and a flat metal gasket, with different scopes of application. In conclusion, the developed multi-criteria decision matrix provides sufficient and repeatable results. The systematic decision process allows users to clearly identify the best production method for their individual use case.

Analysing dimensions and indicators to design energy education framework in Malaysia using the analytic hierarchy process (AHP)

In order to progress towards a more sustainable energy future, Malaysia should reduce its reliance on fossil-based energy sources and shifting to green and renewable energy. Therefore, focus on energy education awareness programmes emphasising sustainable behaviours and frameworks are required. These programmes connect with Sustainable Development Goal 7 of the United Nations, which is to provide affordable and clean energy. In this study, dimensions and indicators of a framework for energy education were studied and evaluated in an effort to improve decision making. This study systematic approach comprised a review of relevant literature and consultation with an ad hoc panel of energy education experts via surveys, interviews, and questionnaires. This study also included an analytic hierarchy process to evaluate the framework’s criteria selection, alternative indicators, and priority or weights. The results indicated that the “Aim to minimise climate change” dimension is deemed the most essential criterion for selecting energy education dimension criteria. On the other hand, “National policy makers” is the most essential stakeholder across all parties. It has been determined that the use of analytic hierarchy process to the design of the framework for energy education in Malaysia could help simplifies a systematic decision-making process.

A novel fuzzy framework for technology selection of sustainable wastewater treatment plants based on TODIM methodology in developing urban areas

Optimal technology selection of wastewater treatment plants (WWTPs) necessitates the adoption of data-driven scientific approaches that satisfy the sustainability requirements of the urban ecosystem. Such approaches should be able to provide actionable insights to decision makers constrained by factors such as population growth, land scarcity, and loss of functionality of wastewater treatment plants. The framework in this study proposes a hybrid fuzzy multi-criteria decision making (MCDM) model consisting of the analytical hierarchy process (AHP) and the TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) by using alpha cut series which takes into account the risk aversion of decision makers (DMs) to overcome uncertainties of environmental conditions. The literature to date indicates that the study is the first to presents how a systematic decision-making process is approached by interpreting the interaction of criteria for the selection of wastewater treatment technology through the membership function of Prospect Theory. The proposed methodology reveals that the prominent reference criterion manipulates other sub-criteria according to the function of risk-aversion behavior. The fuzzy sets based on alpha cut series are employed to evaluate both the criteria weight and the rank of the alternatives in the decision-making process to obtain compromise solutions under uncertainty. The dominance degrees of the alternatives are achieved by fuzzy TODIM integrated with the fuzzy analytic hierarchy process (FAHP) which deals with the uncertainty of human judgements. According to the ranking results determined by the dominance degree of alternatives, anaerobic–anoxic–oxic (A2O) without pre-clarification was the most effective process in relation to the sludge disposal cost (C25) calculated as reference criteria. The ranking of four full-scale WWTPs in a metropolitan city of an EMEA country based on 24 sub-criteria listed under the four main criteria, namely the dimensions of sustainability, is used as a case study to verify the usefulness of the fuzzy approach. Motivated by the literature gap related to the failure to consider the psychological behavior of DMs in technology selection problem for wastewater treatment, it is discussed how the proposed hybrid MCDM model can be utilized by reflecting human risk perception in wastewater treatment technology selection for developing urban areas.

University selection: Rational and emotional aspects of the decisionmaking process

University selection is complex, and for undergraduate students, they possess no prior lived experience to provide the foundation for their decision making. It would be easy to assume, therefore, that a rational and systematic decision-making process takes place. Yet despite repeated attempts by the UK Government to provide various factual and comparable UK university performance metrics, the usage of comparable data remains low. Students are influenced by where they feel they will fit in, where they will be safe and supported and for the overall student experience they perceive to be on offer. In the international domain, decision-making models and market research demonstrate both factual and emotional factors involved in overseas university selection. The emotional aspect of the decision, coupled with an overwhelming choice, necessitate universities to have a strong brand. The paper provides a case study of how these concepts were addressed at Leeds Beckett University, a modern university in England with over 25,000 students. At Leeds Beckett, implementing a personalisation marketing strategy has grown student engagement and enrolments, and led to a major turnaround in international student recruitment.

Migration planning and control in the context of manufacturing network reconfiguration

In order to remain competitive many companies are forced to exploit the existing potential for improvement across their entire manufacturing network in terms of efficiency, cost reduction, robustness and agility. In particular, the lack of a systematic decision-making process and the fact that decisions are often based on individual experience and imitative behavior lead to unfavorably structured manufacturing networks. Additionally, in increasingly volatile business environments, companies are forced to constantly rethink and thus continuously reconfigure their existing manufacturing network. Today, there are several planning approaches on factory and network level that build on modern decision support tools to enable targeted and transparent reconfiguration of manufacturing networks despite high complexity. However, the subsequent migration process (transformation of the existing network to a new target state) is mostly neglected in literature. This contribution presents an original framework for a systematic reconfiguration process of manufacturing networks. The framework combines existing approaches in literature with identified challenges in the context of three conducted manufacturing network reconfiguration use cases. To close identified gaps in literature the focus is put on the migration process. The elaborated framework is based on a rolling planning and control approach, with decision support tools (i.e. simulation & optimization) taking the leading role due to the operational, complex and dynamic nature of migration processes.

Üniversitelerin Şehir İçi Yer Seçimine Yönelik Metodolojik Bir Yaklaşım

Emerging as urban institutions in the medieval Europe, universities began to offer education in college buildings in the center of the cities where they were originally established. Over time, with the effect of the American campus model, they have become independent settlements that can grow and develop in large areas outside of cities. Today, however, many university campuses are functionally, economically, and socially obsolete and in need of restructuring. This study aims to develop a systematic decision-making process for the evaluation of urban settlement of universities in accordance with some parameters. Thus, based on the national and international research on the location selection of universities, ‘25 subparameters under four main parameter groups affecting the urban location selection’ were identified. The analysis of these parameters was performed by applying the AHP method, the evaluation of alternative decisions was carried out by applying the TOPSIS method, and a decision-making methodology was developed for the location selection of universities within the city. To test this methodology, ‘Selçuk University’ was determined as the sampling site. With the method developed in the study, it was determined that the interaction between the university and the city and the accessibility of the university were critical in the urban settlement of the universities. Although the establishment of a university campus has become easier with the growth and development of higher education today, the fact that universities exist for the society and cannot be isolated from it should not be forgotten. Therefore, the return of the university to the city must be ensured and the university and the city must be reconciliated.

Beyond Tier Three: Individualized Levels of Support During Headsprout® Early Reading Instruction for Children with Autism Spectrum Disorder.

We previously conducted a series of conceptual replications to parse out which-and to what degree-adaptations and individualized supports were needed for children to access and learn early reading skills using Headsprout®. Here we developed a systematic decision-making process for providing levels of support to individuals with autism who require additional instruction in order to access and/or advance through Headsprout®. Using a series of single-case AB, ABC, and multiple-baseline designs we examined strategies from our previous research and additional strategies iteratively developed during the study on six students with autism, all of whom demonstrated progression through Headsprout® and increased reading skills. We provide practical implications for teachers and suggestions to research these supports with other computer-based programs.

Determinants of Farmers’ Level of Interaction with Agricultural Extension Agencies in Northwest Ethiopia

This research identifies critical determinants for interactions between farmers and extension agencies. Cross-sectional farm household-level data from three hundred household heads were collected between September 2019 and March 2020 and triangulated with data from workshops with farmers and extension agents. The data were analyzed using Spearman’s correlation coefficient, Kruskal–Wallis analysis of variance and the ordered probit model. Farmers’ socio-economic characteristics significantly affect their degree of interaction with extension agencies. Recognition of the determinants of the level of farmers’ interactions can inform policymakers about how to formulate and improve the effectiveness of extension programs, enhance information and knowledge dissemination and facilitate development in collaboration with local communities by focusing on a better interaction between farmers and extension agencies. The level of a farmer’s interactions is based on a systematic decision-making process. Although personal and demographic characteristics are important, farmers’ interaction levels require conducive institutional and household assets, groups/social capital and access to extension agents’ contexts. These contexts will differ by household, country and region. Therefore, extension agencies should create and design contextually appropriate strategies for substantial interactions with farmers for the dissemination of farm information. This research is original and valuable in identifying the factors associated with the level of farmers’ interactions with extension agencies in the Amhara region, Ethiopia. It also provides a new pathway for operationalizing farmer-oriented agricultural extension policies and strategies and to help agricultural policymakers formulate extension service programs.

A process knowledge representation approach for decision support in design of complex engineered systems

Process knowledge is of considerable significance to the digitalization and intelligentization of the manufacturing industry. Current research on the process knowledge representation of decision-making in engineering design has predominantly focused on either mathematical models of individual decisions at the micro-level or organizational models of group decision consensus at the macro-level. However, the management of complexity and uncertainty in the model-based realization of engineered systems is critical to achieving rational, comprehensive, and robust decisions, especially in terms of knowledge-intensive design. The efficiency and effectiveness of decisions in system design are intrinsically linked to the process, knowledge, and system concepts involved, necessitating a more flexible and systematic decision process representation scheme that supports both the management of complexity and uncertainty. Hence, in this paper, we propose a decision-centric design process representation scheme named the Phase-Event-Information X (PEI-X) diagram and its corresponding systematic design guidance method for designing decision workflows. Using the proposed method, designers have the ability to (1) model hierarchical decision processes that cover vertical and horizontal interaction patterns, and (2) exploit the synthesis of the “Formulating-Identifying-Reusing-Exploring” iterative process to extend the understanding and prediction of decision process behaviors in design. We achieve the aforesaid abilities through the implementation of a knowledge-based design guidance system for collaborative decision support and we demonstrate the efficacy by adopting a specific multi-stage manufacturing process design problem, hot rod rolling system design, and carry out an integrated design of materials, products, and related manufacturing processes.

Ethical Decision-Making in Marriage and Family Therapy: A Review of the Literature

It is common knowledge that marriage and family therapists are ethically obligated to provide competent care to their clients and that they need to engage in systematic decision-making processes to ensure they are providing the best care. Yet, a comprehensive guide for ethical decision-making specifically accounting for the unique nuances of working with relational-systemic clients from a relational-systemic conceptual lens is lacking. Thus, the aim of this article is to outline the current understandings of the unique ethical issues experienced when working with relational-systemic clients and to review currently utilized ethical decision making models in the hopes of providing guidance regarding the development of a specific relational-systemic ethical decision-making model.