Decision Alternatives Research Articles

Effective decision-making in public health organizations: reference to the COVID-19 pandemic.

The role of public health organizations during the COVID-19 pandemic was crucial. These groups acted to slow the spread of infection through the implementation of initiatives, policies, research and more. However, the rapidly changing and uncertain climate of the pandemic resulted in suboptimal processes and decision-making within these organizations. These already complex organizations and networks of people became even more nuanced. Thus, organizational decision-making processes must be improved upon based on previous experiences and lessons learnt. With minimal peer-reviewed literature available, resources for effective organizational decision-making in these organizations are scarce. This served as the impetus for this review. To conduct this literature review, both peer-reviewed and grey literature were incorporated to better understand effective organizational decision-making practices for public health organizations. Recommendations found in the literature review were identified, coded and themed to provide a novel decision-making framework to be used by public health executives. Nine key themes of effective organizational decision-making were identified, including utilize decision-making tools, define the problem and acknowledge an imminent decision, establish decision rights, outline a clear escalation path, create a supportive organizational culture, set decision objectives and goals, and evaluate decision alternatives. These findings in conjunction with existing decision-making models were used to create a seven-step effective decision-making framework for public health organizations. The review and analysis of effective organizational decision-making practices is instructive. Public health executives and decision-makers should incorporate the themes identified and employ the proposed decision-making framework to encourage improved decision-making practices.

Influence of stream temperature and human disturbance on prespawn mortality of Chinook Salmon in the Willamette River basin

AbstractObjectivePremature mortality of adult female Chinook Salmon Oncorhynchus tshawytscha is a major barrier to population recovery. The Willamette River basin, Oregon, typifies the problems that are faced by fishery managers in the Pacific Northwest (USA). Adult salmon are trapped and transported upstream of dams to access historical spawning grounds, but annual rates of prespawn mortality (PSM) are high (often >40%) and may limit the recovery of natural populations. The purpose of this study was to identify potential factors related to PSM of female Chinook Salmon that are outplanted above dams and incorporate them into a modeling framework to facilitate adaptive management of outplanting operations.MethodsWe evaluated PSM in Fall Creek of the Willamette River basin prior to transport facility improvements in summer and fall of 2010–2017 and postimprovement during 2020–2021. We estimated PSM and conducted exploratory analyses to identify possible nontransport sources of stress that may contribute to the observed high PSM rates. Candidate factors included long‐term elevated temperature exposure, elevated temperature exposure below the trap, total number of outplanted fish, and monthly human disturbance of outplanted fish. We then developed and fit three models, each representing a hypothesis of a factor influencing PSM, incorporated them into a single alternative decision model, and conducted sensitivity analyses.ResultPrespawn mortality averaged 0.66 (ranging from 0.37 to 0.94) over the study period. According to the simulation results, the top two management actions were to exclude human activities—swimming and fishing—from Fall Creek in July and August.ConclusionExpected PSM rates were predicted to be 0.38 when human activity was excluded in July and 0.37 for August. Sensitivity analyses indicated that the most influential decision model component was the choice of the alternative model.

Value construction through sequential sampling explains serial dependencies in decision making.

Many decisions are expressed as a preference for one item over another. When these items are familiar, it is often assumed that the decision maker assigns a value to each of the items and chooses the item with the highest value. These values may be imperfectly recalled, but are assumed to be stable over the course of an interview or psychological experiment. Choices that are inconsistent with a stated valuation are thought to occur because of unspecified noise that corrupts the neural representation of value. Assuming that the noise is uncorrelated over time, the pattern of choices and response times in value-based decisions are modeled within the framework of Bounded Evidence Accumulation (BEA), similar to that used in perceptual decision-making. In BEA, noisy evidence samples accumulate over time until the accumulated evidence for one of the options reaches a threshold. Here, we argue that the assumption of temporally uncorrelated noise, while reasonable for perceptual decisions, is not reasonable for value-based decisions. Subjective values depend on the internal state of the decision maker, including their desires, needs, priorities, attentional state, and goals. These internal states may change over time, or undergo revaluation, as will the subjective values. We reasoned that these hypothetical value changes should be detectable in the pattern of choices made over a sequence of decisions. We reanalyzed data from a well-studied task in which participants were presented with pairs of snacks and asked to choose the one they preferred. Using a novel algorithm ( Reval ), we show that the subjective value of the items changes significantly during a short experimental session (about 1 hour). Values derived with Reval explain choice and response time better than explicitly stated values. They also better explain the BOLD signal in the ventromedial prefrontal cortex, known to represent the value of decision alternatives. Revaluation is also observed in a BEA model in which successive evidence samples are not assumed to be independent. We argue that revaluation is a consequence of the process by which values are constructed during deliberation to resolve preference choices.

Symmetry breaking and fate divergence during lateral inhibition in Drosophila.

Lateral inhibition mediates alternative cell fate decision and produces regular cell fate patterns with fate symmetry breaking (SB) relying on the amplification of small stochastic differences in Notch activity via an intercellular negative feedback loop. Here, we used quantitative live imaging of endogenous Scute (Sc), a proneural factor, and of a Notch activity reporter to study the emergence of Sensory Organ Precursor cells (SOPs) in the pupal abdomen of Drosophila. SB was observed at low Sc levels and was not preceded by a phase of intermediate Sc expression and Notch activity. Thus, mutual inhibition may only be transient in this context. In support of the intercellular feedback loop model, cell-to-cell variations in Sc levels promoted fate divergence. The size of the apical area of competing cells did not detectably bias this fate choice. Surprisingly, cells that were in direct contact at the time of SB could adopt the SOP fate, albeit at low frequency (10%). These lateral inhibition defects were corrected by cellular rearrangements, not cell fate change, highlighting the role of cell-cell intercalation in pattern refinement.

Comparison of 2 Free Light Chain Assays: Performance of the Free Light Chain Ratio as a Risk Factor for MGUS Progression.

New immunoglobulin free light chain (FLC) assays are available. Despite analytical differences, it seems possible to use free light chain ratios (FLCr) generated by different assays and apply similar cut-points for the diagnosis of multiple myeloma. It is still unknown if we can use different assays for risk stratification of patients with monoclonal gammopathy of undetermined significance (MGUS). Patients diagnosed with MGUS (N = 923) had FLC tested using a nephelometric FreeLite (Binding Site) assay on BNII instruments (Siemens) and a Sebia FLC assay (Sebia) on a DS2 ELISA analyzer (Dynex). Patients were followed up for progression to any plasma cell dyscrasia (PCD) for several decades. The Mayo MGUS risk stratification model for progression was assessed with both assays (M-spike >1.5 g/dL; non-IgG isotype and abnormal FLCr), using package insert reference intervals (RI) and a new metric called principal component 2 (PC2). There were 94 events of progression to PCD in the cohort during a median of 38 years of follow-up. Freelite and Sebia FLC showed similar hazard ratios in the risk models for elevated FLCr. An alternative clinical decision point lower than the package insert RI was evaluated for the Sebia assay, which improved risk stratification for patients with a low FLCr. The PC2 metric showed similar performance to the FLCr in models, without superior benefit. The Sebia ELISA-based FLC assay can be employed in an MGUS risk stratification model with similar performance to the original 2005 risk stratification model using the FreeLite assay.

A-231 A modified Sampson LDL-C equation to improve concordance with the reference method at low levels of LDL-C

Abstract Background Patients at high risk for Atherosclerotic Cardiovascular Disease (ASCVD), who do not achieve adequate lowering of LDL-cholesterol (LDL-C) with maximally tolerated statins or who are statin-intolerant, are often treated with proprotein convertase subtilisin/kexin (type 9) serine protease inhibitor (PCSK9-inh) therapy. The LDL-C reference method, Beta-Quantitation (BQ), is not routinely performed in these patients. Because low values are measured less accurately with current methods, a variety of equations have been developed to calculate LDL-C from the standard lipid panel test results (total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C)). We further modified the Sampson LDL-C (S-LDL-C) equation by a novel approach by combining logistic regression (LR) and linear regression to minimize errors when compared to BQ for low LDL-C. Methods A dataset of BQ results from Mayo Medical Labs was divided randomly into a training set (TG ≤ 1500 mg/dL, TC ≤ 1050 mg/dL, n=19,639) and a validation set (TG ≤ 800 mg/dL, BQ-LDL-C ≤ 130 mg/dL, n=12,910). The training dataset was analyzed by LR, using BQ-LDL-C to classify patients as being above or below the 70 mg/dL cut-point. Dependent variables in the LR model were the S-LDL-C, HDL-C, TC, TG and an interaction term (TG x NonHDL-C). The sum of the exponential terms in the trained LR equation for the dependent variables were then fitted by least squares linear regression to match BQ-LDL-C to develop the modified S-LDL-C equation (mS-LDL-C). We used the validation dataset to compare Friedewald (F-LDL-C), extended Martin (eM-LDL-C), Sampson (S-LDL-C) and modified Sampson (mS-LDL-C) equations by Cohen’s kappa coefficient for agreement with BQ-LDL-C and normalized Matthew’s correlation coefficient (MCC), which considers any imbalances in the number of false high versus false low results. Results The mS-LDL-C equation (mS-LDL-C = (TC/0.9423 - HDL-C/0.9582 - TG/9.397 - ((TGxNonHDL-C)/1305) + TG2/8569) - 5.688), primarily differs from the S-LDL-C equation in the value of the coefficients that include TG as a variable. For S-LDL-C ≤100 mg/dL, mS-LDL-C had the best concordance with BQ-LDL-C in terms of the total percent of patients misclassified as either high or low (mS-LDL-C: 21.4%, S-LDL-C: 24.4%, eM-LDL-C: 25.4% F-LDL-C: 30.8%). mS-LDL-C also had the best kappa score when compared to BQ (mS-LDL-C: 0.833, S-LDL-C: 0.810, eM-LDL-C: 0.789, F-LDL-C: 0.743). MCC was also the highest for mS-LDL-C (mS-LDL-C: 0.916, S-LDL-C: 0.906, eM-LDL-C: 0.894, F-LDL-C: 0.879). Finally, mS-LDL-C also showed the best concordance with BQ-LDL-C when an alternative lower LDL-C therapeutic decision cut-point of 55 mg/dL is used (kappa of 0.766 versus 0.724) but showed an increasingly poorer performance than S-LDL-C for higher LDL-C whenever it exceeded 150 mg/dL. Conclusions A strategy to maximize the overall accuracy of estimated LDL-C could provide an automatic reflex in a laboratory information system to the mS-LDL-C equation whenever S-LDL-C is <100 mg/dL. By doing so, the use of the more accurate mS-LDL-C equation for low LDL-C should improve PCSK9-inh therapeutic decision making over the other LDL-C equations.

Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane’s attributes evolve over time according to a Markov chain model, and the demand quantity at each demand point is calculated based on the hurricane’s attributes (intensity and location) at the terminal stage, which corresponds to the hurricane’s landfall. We introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adapt their logistics decisions over time according to the evolution of the hurricane’s attributes. In addition, we develop a novel extension of the standard MSP model to address the challenge of having a random number of stages in the planning horizon due to the uncertain landfall time of the hurricane. We benchmark the performance of the adaptive decision policy given by the MSP models with alternative decision policies, including a static policy, a rolling-horizon policy, a wait-and-see policy, and a decision-tree-based policy, all based on two-stage stochastic programming models. Our numerical results and sensitivity analyses provide key insights into the value of MSP in the hurricane disaster relief logistics planning problem.

A Multi-Criteria Decision Making for Employee Selection Using SAW and Profile Matching

Multi-criteria decision making process has been one of the fastest growing areas during the last decades depending on the change in the business sector. Multi-criteria decision making is the most important branch of operation research by which people make complex decision daily life. The major steps in the decision- making approach are selecting the most preferred alternative for the decision-maker, ranking alternatives in order of importance for selection problems, and screening alternatives for the final decision. Employees are an important element of a company that determines its progress. Conventional (manual) recruitment methods are vulnerable to non-technical factors, such as frequent duplication or invalid data. In this study, simple additive weighting and profile matching are proposed to solve the employee selection problem. This study was conducted at the (UPT) Career Development and Entrepreneurship Universitas Brawijaya Malang using data collected from written test selection in 2019. The effectiveness of both methods was analyzed using confusion matrix. The SAW method provides an accuracy rate of 94.7%, a precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, profile matching method obtained the Accuracy rate of 90.4%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. Thus, it can be concluded that both methods have high accuracy values accompanied by high precision values when used for the selection process. This system can also effectively reduce the bias rate of the same data very well, as can be seen from the high recall and F-measure rates.

The Influence of Machine Learning on Enhancing Rational Decision-Making and Trust Levels in e-Government

The rapid growth in the use of AI techniques, mainly machine learning (ML), is revolutionizing different industries by significantly enhancing decision-making processes through data-driven insights. This study investigates the influence of using ML, particularly supervised and unsupervised learning, on rational decision-making (RDM) within Jordanian e-government, focusing on the mediating role of trust. By analyzing the experiences of middle-level management within e-government in Jordan, the findings underscore that ML positively impacts the rational decision-making process in e-government. It enables more efficient and effective data gathering, improves the accuracy of data analysis, enhances the speed and accuracy of evaluating decision alternatives, and improves the assessment of potential risks. Additionally, this study reveals that trust plays a critical role in determining the effectiveness of ML adoption for decision-making, acting as a pivotal mediator that can either facilitate or impede the integration of these technologies. This study provides empirical evidence of how trust not only enhances the utilization of ML but also amplifies its positive impact on governance. The findings highlight the necessity of cultivating trust to ensure the successful deployment of ML in public administration, thereby enabling a more effective and sustainable digital transformation. Despite certain limitations, the outcomes of this study offer substantial insights for researchers and government policymakers alike, contributing to the advancement of sustainable practices in the e-government domain.

Combined effect of climate change and labor relation on optimally managing labor productivity under uncertainty

Heat stress amplified by climate change causes excessive reductions in labor capacity, work injuries, and socio-economic losses. Yet studies of corresponding impact assessments and adaptation developments are insufficient and incapable of effectively dealing with uncertain information. This gap is caused by the inability to resolve complex channels involving climate change, labor relations, and labor productivity. In this paper, an optimization-based productivity restoration modeling framework is developed to bridge the gap and support decision-makers in making informed adaptation plans. The framework integrates a multiple-climate-model ensemble, an empirical relationship between heat stress and labor capacity, and an inexact system costs model to investigate underlying uncertainties associated with climate and management systems. Optimal and reliable decision alternatives can be obtained by communicating uncertain information into the optimization processes and resolving multiple channels. Results show that the increased heat stress will lead to a potential reduction in labor productivity in China. By solving the objective function of the framework, total system costs to restore the reduction are estimated to be up to 248,700 million dollars under a Representative Concentration Pathway of 2.6 (RCP2.6) and 697,073 million dollars under RCP8.5 for standard employment, while less costs found for non-standard employment. However, non-standard employment tends to restore productivity reduction with the minimum system cost by implementing active measures rather than passive measures due to the low labor costs resulting from ambiguities among employment statuses. The situation could result in more heat-related work injuries because employers in non-standard employment can avoid the obligation of providing a safe working environment. Urgent actions are needed to uphold labor productivity with climate change, especially to ensure that employers from non-standard employment fulfill their statutory obligations.

Identity Claims and the Legal Order: Secular or Religious?

Abstract The problem addressed in this article is the challenge that identity claims stemming from identity politics, commonly recognized as left liberalism, pose to the secularity of the modern legal order. The paper: (1) postulates human dignity as the highest value and assesses the potential of philosophy and law to find a balance among the conflicting demands posed by this value; (2) identifies constitutional principles and/or jus cogens as the basis for the identification and appraisal of identity claims; (3) describes major identity claims embodied in the Istanbul Convention and appraises them on the basis of the principles; (4) ascribes identity politics and its claims to a worldview with traits of a religion, termed culturalism, as their condition. The conclusion proposes alternative decisions (de lege ferenda) more in accord with the principles, most notably with the freedom of thought, conscience and religion.

Three-way concept lattice from adjunctive positive and negative concepts

Three-way concept lattices (TCLs) have been widely explored due to their clear hierarchical structures, concise visual description and good interpretability. In contrast to classic formal contexts, lattice-valued fuzzy contexts exhibit great capability in describing and representing concepts with uncertainty. Different from conventional approaches to research of TCLs, this paper focuses on investigating the algebraic structure and properties of three-way concept lattice (TCL) stemmed from the positive concept lattice and negative concept lattice in a lattice-valued formal context. Several associated concept lattices such as the Cartesian product of positive concept lattice and negative lattice (i.e., pos-neg lattice), lattices induced from the partition of the pos-neg lattice, and their relationship are explored. Specifically, the isomorphism, embedding and order-preserving mappings between them are built. The quotient set of pos-neg lattice when being defined a specific equivalence relation on it is a complete lattice and each equivalence class is a lower semi-lattice. It is further declared that the structure of TCL is intrinsically and determined wholly by the pos-neg lattice. A practical application of the built theory of TCL is provided to sort alternatives in multi-criteria decision making.

Implementasi Pengambilan Keputusan Taktis Melalui Analisis Biaya Relevan Untuk Meningkatkan Pendapatan (Studi Kasus: Pabrik Rumahan Roti Hangat Keliling di Kota Jambi)

The Mobile Warm Bread Home Factory is a Micro, Small and Medium Enterprise (MSME) that processes raw materials into finished products, namely warm bread. The factory produces 2,100 loaves of bread a day, with flavor variants such as chocolate, strawberry, coconut, green bean, srikaya, and messes. In the production process, the mobile hot bread home factory still uses gas oven production tools or machines, in the midst of the development of equipment or Machines that use electrical energy are electric ovens. The aim of this research is to analyze the application of relevant costs in decision making to continue or replace a gas oven with an electric oven. This research methodology uses descriptive qualitative, with data collection through in-depth interviews, observation and data analysis. Decision making will always be related to the future so business owners need to review these decisions. To help business owners make the right decisions, by choosing the best decision alternative among the available decision alternatives. The results of this analysis show that the most appropriate decision is to continue using a gas oven because it obtains the most relevant total results and benefits rather than replacing it with an electric oven.

A group decision-making model for architectural programming in megaprojects

PurposeDue to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects.Design/methodology/approachThis study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process.FindingsThis study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming.Originality/valueAPMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste.

A Novel Method for Measuring the Performance of Decision Alternatives in Multi-Criteria Decision Making: Proportional Superiority Approach (PSA)

In the face of increasing complexity and uncertainty, new multi-criteria decision making (MCDM) methods facilitate making informed and rational decisions by enhancing problem-solving skills. Therefore, the discovery of new MCDM methods is of great importance. In this context, this study introduces a new MCDM model (Proportional Superiority Approach-PSA) based on the fundamental logic of measuring the performance of decision alternatives, which relies on the proportional increase of decision alternatives to each other, aiming to expand the modeling logic of MCDM and enrich MCDM literature. Initially, a comparative analysis of the proposed method was conducted. According to the findings, although the relationship of PSA with other MCDM methods included in the study was high, it was found to be different from those MCDM methods. Therefore, based on the results of the comparative analysis, it was observed that the proposed method is credible and reliable. In the scope of the simulation analysis, 10 scenarios were obtained, and it was found that as the number of scenarios increased, the relationship levels of the PSA method with other methods differed and decreased. Furthermore, the PSA method was found to be capable of discriminating between the performances of decision alternatives through variance measurement. Finally, in the analysis, the level of variance of the PSA method was measured within the scenarios, and it was found that the variances of the PSA method were homogeneous within the scenarios. Therefore, according to the results of the simulation analysis, it was evaluated that the PSA method is robust and stable.

‘It’s not making a decision, it’s prompting the discussions’: a qualitative study exploring stakeholders’ views on the acceptability and feasibility of advance research planning (CONSULT-ADVANCE)

BackgroundHealth and care research involving people who lack capacity to consent requires an alternative decision maker to decide whether they participate or not based on their ‘presumed will’. However, this is often unknown. Advance research planning (ARP) is a process for people who anticipate periods of impaired capacity to prospectively express their preferences about research participation and identify who they wish to be involved in future decisions. This may help to extend individuals’ autonomy by ensuring that proxy decisions are based on their actual wishes. This qualitative study aimed to explore stakeholders’ views about the acceptability and feasibility of ARP and identify barriers and facilitators to its implementation in the UK.MethodsWe conducted semi-structured interviews with 27 researchers, practitioners, and members of the public who had participated in a preceding survey. Interviews were conducted remotely between April and November 2023. Data were analysed thematically.ResultsParticipants were supportive of the concept of ARP, with differing amounts of support for the range of possible ARP activities depending on the context. Six main themes were identified: (1) Planting a seed – creating opportunities to initiate/engage with ARP; (2) A missing part of the puzzle – how preferences expressed through ARP could help inform decisions; (3) Finding the sweet spot – optimising the timing of ARP; (4) More than a piece of paper – finding the best mode for recording preferences; (5) Keeping the door open to future opportunities – minimising the risk of unintended consequences; and (6) Navigating with a compass – principles underpinning ARP to ensure safeguarding and help address inequalities. Participants also identified a number of implementation challenges, and proposed facilitative strategies that might overcome them which included embedding advance research planning in existing future planning processes and research-focused activities.ConclusionsThis study provides a routemap to implementing ARP in the UK to enable people anticipating impaired capacity to express their preferences about research, thus ensuring greater opportunities for inclusion of this under-served group, and addressing the decisional burden experienced by some family members acting as proxies. Development of interventions and guidance to support ARP is needed, with a focus on ensuring accessibility.

How Can Risk-Averse and Risk-Taking Approaches be Considered in a Group Multi-Criteria Decision-Making Problem?

We propose an alternative decision-making methodology based on adopting a mixed risk-averse and risk-taking behavior, improving the objectivity of decision-making. We demonstrate the methodology by prioritizing Iranian tourism centers’ activity under pandemic conditions, providing insights to policymakers on those to keep active or reduce the activity of – hence, those worth developing ahead of future disease outbreaks. This research follows a three-step methodology. First, criteria for evaluation are identified and categorized into tourist attractions, infrastructure, and healthcare dimensions. Second, criterion weights are calculated based on expert opinions, collected using a best-worst method-based questionnaire. Third, tourism centers are evaluated by employing risk-averse and risk-taking best-worst methods. We identify popular attractions, general services, and drugstore accessibility as the primary indicators of tourist attractions, infrastructure, and healthcare, respectively. By clustering tourism centers using K-means algorithm, we find that, in order, the cities of Semnan, Kerman and Zahedan are the tourism centers most suited to staying active during disease outbreaks. For multi-criteria decision-making problems that rely on experts’ evaluations, the proposed methodology can improve the reliability of decision-making. The methodology and framework presented can be used to support various types of decision-making, including evaluation, ranking, selection or sorting.

An integrated AHP-TOPSIS approach for bamboo product evaluation and selection in rural communities

This study introduces a decision support model integrating the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select an economic tree product champion (bamboo) to benefit rural communities. An extensive literature review and expert discussions identified sixteen sub-criteria distributed across five main criteria. The study proposes four categories of bamboo products as alternatives, emphasizing community-level production capacity. The AHP determines priority weights, while TOPSIS prioritizes alternatives conducive to becoming the product champion within a community case study. The findings affirm the efficacy of Multi-Criteria Decision-Making (MCDM) in identifying a champion, with “Value addition potential,” “Domestic market demand,” and “International market (export) demand” identified as pivotal criteria. Bamboo culm-based products for energy-related applications emerged as the chosen product champion in a community case study in Thailand. This study offers practical implications for rural communities and potential investors in economic tree ventures, allowing the customization of decision criteria and alternatives for specific contexts. Socially, the focus on bamboo highlights diverse benefits along the entire supply chain, from upstream to downstream. The research pioneers a decision support model, providing insights into market opportunity analysis and supply chain network design based on the selected product champion.

A web-based multi-criteria decision support system for benchmarking marketing decisions alternatives

A web-based multi-criteria decision support system for benchmarking marketing decisions alternatives

Group decision making method for third-party logistics management: An interval rough cloud optimization model

Group decision making in third-party logistics service selection plays an essential role for improving service quality, increasing efficiency and reducing the net cost. Fuzzy and uncertain linguistic variables are commonly used to represent experts‘rankings in optimization problems. To recognize the limits of human cognition and subjectivity of human evaluations, several optimization approaches have been studied to select remanufacturing alternatives in decision making processes, however these methods have certain deficiencies such as lacking manipulation tools of diverse information, randomness, use of predefined parameters increasing uncertainty, interpersonal relations among evaluation criteria. The integration of interval numbers, rough approximations, and cloud model theory plays a significant role to model incomplete and inadequate information occurring in decision making problems. This research paper focuses on the integration of dual interval rough integrated cloud model with best-worst optimization technique, Multi-Attributive Border Approximation area Comparison (MABAC) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches. A novel min–max optimization model based dual interval rough integrated cloud values is designed to compute the weight coefficients and consistency ratio for each criteria. The consistency of proposed optimization model is checked using a consistency ratio test. Secondly, the alternatives are ranked using the proposed DIRI cloud based MABAC and WASPAS approaches using interval clouds based weighted sum and weighted product coefficients, approximation area values and distance formulae. The significance of the proposed model is highlighted with a case study of third-party logistics service management of an electronic firm. The rationality and out-performance of the proposed methodology is studied by a comparative analysis with existing approaches and detailed sensitivity analysis on different variations of criteria weights and parameter.