A Parameter Reduction-Based Decision-Making Method with Interval-Valued Neutrosophic Soft Sets for the Selection of Bionic Thin-Wall Structures

Bio-inspired thin-wall structures with excellent mechanical properties, high-energy absorption capabilities, and a desirable lightweight level have been extensively applied to the passive safety protection of transportation and aerospace. Collaboration matching and the selection of optional structures with different bionic principles considering the multiple attribute evaluation index and engineering preference information have become an urgent problem. This paper proposes a parameter reduction-based indifference threshold-based attribute ratio analysis method under an interval-valued neutrosophic soft set (IVNS-SOFT) to obtain the weight vector of an evaluation indicator system for the selection of bionic thin-wall structures, which can avoid the problem of an inadequate subjective evaluation and reduce redundant parameters. An IVNS-SOFT-based multi-attributive border approximation area comparison (MABAC) method is proposed to obtain an optimal alternative, which can quantify uncertainty explicitly and handle the uncertain and inconsistent information prevalent in the expert system. Subsequently, an application of five bio-inspired thin-wall structures is applied to demonstrate that this proposed method is valid and practical. Comparative analysis, sensitivity analysis, and discussion are conducted in this research. The results show that this study provides an effective tool for the selection of bionic thin-wall structures.

[Prediction of microvascular invasion in hepatocellular carcinoma based on multi-phase dynamic enhanced CT radiomics feature and multi-classifier hierarchical fusion model]

OBJECTIVE

To predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using a model based on multiphase dynamic-enhanced CT (DCE-CT) radiomics feature and hierarchical fusion of multiple classifiers.

METHODS

We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January, 2016 and April, 2020. The volume of interest was outlined in the early arterial phase, late arterial phase, portal venous phase and equilibrium phase, and radiomics features of these 4 phases were extracted. Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase. According to the hierarchical fusion strategy, a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model. The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve (AUC), accuracy, sensitivity, and specificity. The prediction model was also compared with the fusion models using a single phase or multiple phases, models based on a single phase with a single classifier, models with different base classifier diversities, and 8 classifier models based on other ensemble methods.

RESULTS

The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers, with AUC, accuracy, sensitivity, and specificity of 0.828, 0.766, 0.877, and 0.648, respectively. Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.

CONCLUSION

The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review

BACKGROUND

Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques.

OBJECTIVE

The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field.

METHODS

The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined.

RESULTS

The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications).

CONCLUSIONS

Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.

Delighting Palates with AI: Reinforcement Learning's Triumph in Crafting Personalized Meal Plans with High User Acceptance

Eating, central to human existence, is influenced by a myriad of factors, including nutrition, health, personal taste, cultural background, and flavor preferences. The challenge of devising personalized meal plans that effectively encompass these dimensions is formidable. A crucial shortfall in many existing meal-planning systems is poor user adherence, often stemming from a disconnect between the plan and the user's lifestyle, preferences, or unseen eating patterns. Our study introduces a pioneering algorithm, CFRL, which melds reinforcement learning (RL) with collaborative filtering (CF) in a unique synergy. This algorithm not only addresses nutritional and health considerations but also dynamically adapts to and uncovers latent user eating habits, thereby significantly enhancing user acceptance and adherence. CFRL utilizes Markov decision processes (MDPs) for interactive meal recommendations and incorporates a CF-based MDP framework to align with broader user preferences, translated into a shared latent vector space. Central to CFRL is its innovative reward-shaping mechanism, rooted in multi-criteria decision-making that includes user ratings, preferences, and nutritional data. This results in versatile, user-specific meal plans. Our comparative analysis with four baseline methods showcases CFRL's superior performance in key metrics like user satisfaction and nutritional adequacy. This research underscores the effectiveness of combining RL and CF in personalized meal planning, marking a substantial advancement over traditional approaches.

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors' Communication Optimization in an IoT-Fog Ecosystem

With the development of mobile communications and the Internet of Things (IoT), IoT devices have increased, allowing their application in numerous areas of Industry 4.0. Applications on IoT devices are time sensitive and require a low response time, making reducing latency in IoT networks an essential task. However, it needs to be emphasized that data production and consumption are interdependent, so when designing the implementation of a fog network, it is crucial to consider criteria other than latency. Defining the strategy to deploy these nodes based on different criteria and sub-criteria is a challenging optimization problem, as the amount of possibilities is immense. This work aims to simulate a hybrid network of sensors related to public transport in the city of São Carlos - SP using Contiki-NG to select the most suitable place to deploy an IoT sensor network. Performance tests were carried out on five analyzed scenarios, and we collected the transmitted data based on criteria corresponding to devices, applications, and network communication on which we applied Multiple Attribute Decision Making (MADM) algorithms to generate a multicriteria decision ranking. The results show that based on the TOPSIS and VIKOR decision-making algorithms, scenario four is the most viable among those analyzed. This approach makes it feasible to optimally select the best option among different possibilities.

Intelligent Emotion and Sensory Remote Prioritisation for Patients with Multiple Chronic Diseases

An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis.

Criteria for the selection of complementary private health insurance: the experience of a large organisation in Iran

Background

Expenses related to employee’s health benefit packages are rising. Hence, organisations are looking for complementary health financing arrangements to provide more financial protection for employees. This study aims to develop criteria to choose the most appropriate complementary health insurance company based on the experience of a large organisation in Iran.

Methods

This study was conducted in 2021 in Iran, in the Foundation of Martyrs and Veterans Affairs to find as many applicable criteria as possible. To develop a comprehensive list of criteria, we used triangulation in data sources, including review of relevant national and international documents, in-depth interviews of key informants, focus group discussion, and examining similar but unpublished checklists used by other organisations in Iran. The list of criteria was prioritised during focus group discussions. We used the best-worst method as a multi-criteria decision making method and a qualitative consensus among the key informants to value the importance of each of the finalised criteria.

Findings

Out of 85 criteria, we selected 28 criteria to choose an insurer for implementing complementary private health insurance. The finalised criteria were fell into six domains: (i) Previous experience of the applicants; (ii) Communication with clients; (iii) Financial status; (iv) Health care providers’ network; (v) Technical infrastructure and workforce; (vi) and Process of reviewing claims and reimbursement.

Conclusion

We propose a quantitative decision-making checklist to choose the best complimentary private health insurance provider. We invite colleagues to utilise, adapt, modify, or develop these criteria to suit their organisational needs. This checklist can be applied in any low- and middle-income country where the industry of complementary health insurance is blooming.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08777-7.

A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and Continuous Uncertain Evaluation

In recent years, Dempster-Shafer (D-S) theory has been widely used in multi-criteria decision-making (MCDM) problems due to its excellent performance in dealing with discrete ambiguous decision alternative (DA) evaluations. In the general framework of D-S-theory-based MCDM problems, the preference of the DAs for each criterion is regarded as a mass function over the set of DAs based on subjective evaluations. Moreover, the multi-criteria preference aggregation is based on Dempster's combination rule. Unfortunately, this an idea faces two difficulties in real-world applications: (i) D-S theory can only deal with discrete uncertain evaluations, but is powerless in the face of continuous uncertain evaluations. (ii) The generation of the mass function for each criterion relies on the empirical judgments of experts, making it time-consuming and laborious in terms of the MCDM problem for large-scale DAs. To the best of our knowledge, these two difficulties cannot be addressed with existing D-S-theory-based MCDM methods. To this end, this paper proposes a clustering MCDM method combining D-S theory with the analytic hierarchy process (AHP) and the Silhouette coefficient. By employing the probability distribution and the D-S theory to represent discrete and continuous ambiguous evaluations, respectively, determining the focal element set for the mass function of each criterion through the clustering method, assigning the mass values of each criterion through the AHP method, and aggregating preferences according to Dempster's combination rule, we show that our method can indeed address these two difficulties in MCDM problems. Finally, an example is given and comparative analyses with related methods are conducted to illustrate our method's rationality, effectiveness, and efficiency.

Evaluation of supply sustainability of vaccine alternatives with multi-criteria decision-making methods

BACKGROUND

The treatment of the COVID-19 epidemic, whose contagious features are changing day by day, is the most current problem today throughout of the world. In order to be protected from COVID-19 and reduce its spread, it is of great importance to follow the rules such as mask, distance and hygiene. In addition, one of the most important ways to prevent the epidemic is to develop population immunity. The most important tool in having population immunity is vaccination.

AIMS

During the COVID-19 pandemic, there have been problems in the supply process of many products in food and health sectors. Vaccine is also one of the most difficult tools to supply. In this context, the study focused on the selection of the vaccine provided by the countries within the scope of population vaccination studies.

MATERIALS & METHODS

At the selection point, the criteria affecting the purchasing process were determined and the weights of these criteria were calculated using the AHP method. Then, the criteria weights obtained were used to rank the alternatives in an integrated manner in the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The results of both methods were analyzed comparatively.

RESULTS

According to the TOPSIS Method, the first alternative is Oxford-AstraZeneca for all countries, and BioNTech for all countries in the PROMETHEE method.

DISCUSSION

The vaccine storage conditions criterion is the most important in vaccine supply. The criterion with the lowest importance is Supply Cost. It has been revealed that cost elements remain in the background under pandemic conditions.

CONCLUSION

Vaccine evaluation studies and policy recommendations are presented by considering public health in the selection of vaccine alternatives.

Evaluation of Factors to Respond to the COVID-19 Pandemic Using DEMATEL and Fuzzy Rule-Based Techniques

Since social and environmental conditions have changed dramatically in recent years, the spectrum of diseases caused by infections is also evolving rapidly. The outspread of COVID-19 has resulted in an emergency situation across the globe with significant effects on the population’s lives, families, and societies, leading to concerns the World Health Organization. Accordingly, the virus has substantially threatened the Malaysians’ public health and contributed considerably to increased healthcare expenses. Since the novel coronavirus was found in China, Malaysia’s government has started its actions according to the World Health Organization procedures and concentrated on addressing and preventing the spread of the infection. The present paper aims to find and evaluate the factors to respond to the COVID-19 outbreak in Malaysia, limiting the outspread of the disease in this country. This study used the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy Rule-Based techniques to evaluate the factors through a set of questionnaires completed by the health care professionals. According to the data analysis results, movement control order, international travel restrictions, and the mass gathering cancellations were of most importance in the prevention of COVID-19 infections transmission.

Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP-BWM Model

Frequent lane changes cause serious traffic safety concerns for road users. The detection and categorization of significant factors affecting frequent lane changing could help to reduce frequent lane-changing risk. The main objective of this research study is to assess and prioritize the significant factors and sub-factors affecting frequent lane changing designed in a three-level hierarchical structure. As a multi-criteria decision-making methodology (MCDM), this study utilizes the analytic hierarchy process (AHP) combined with the best–worst method (BWM) to compare and quantify the specified factors. To illustrate the applicability of the proposed model, a real-life decision-making problem is considered, prioritizing the most significant factors affecting lane changing based on the driver’s responses on a designated questionnaire survey. The proposed model observed fewer pairwise comparisons (PCs) with more consistent and reliable results than the conventional AHP. For level 1 of the three-level hierarchical structure, the AHP–BWM model results show “traffic characteristics” (0.5148) as the most significant factor affecting frequent lane changing, followed by “human” (0.2134), as second-ranked factor. For level 2, “traffic volume” (0.1771) was observed as the most significant factor, followed by “speed” (0.1521). For level 3, the model results show “average speed” (0.0783) as first-rank factor, followed by the factor “rural” (0.0764), as compared to other specified factors. The proposed integrated approach could help decision-makers to focus on highlighted significant factors affecting frequent lane-changing to improve road safety.

Performance Evaluation of Construction Companies Using Integrated Entropy-Fuzzy VIKOR Model

The construction sector plays an important role in a country’s economic development. The financial performance of a company is a good indicator of its financial health and status. In Malaysia, the government encourages the construction industry to develop an advanced infrastructure related to health, transport, education and housing. In view of the COVID-19 pandemic, the operations and financial performance of construction sector companies have been affected recently. Additionally, uncertainty plays a vital role in the multi-criteria decision-making (MCDM) process. Based on previous studies, there has been no comprehensive study conducted on the evaluation of the financial performance of construction companies by integrating entropy and fuzzy VIKOR models. Therefore, this paper aims to propose an MCDM model to evaluate and compare the financial performance of construction companies with an integrated entropy–fuzzy VIKOR model. A case study is carried out by evaluating the listed construction companies in Malaysia with the proposed model. The findings of this paper indicate that the company ECONBHD achieves the best financial performance over the study period. The significance of this paper is to determine the priority of the financial ratios and ranking of the construction companies with the proposed entropy–fuzzy VIKOR model.

Multi-Criteria Decision Making Approaches Applied to Waste Electrical and Electronic Equipment (WEEE): A Comprehensive Literature Review

The global demand for electrical and electronic equipment has undergone continuous growth in recent years due to the effect of industrialization and technological development. This indicates substantial quantities of e-waste that need to be managed properly to reduce their environmental impact and to avoid inappropriate forms of disposal. The purpose of this paper is to review the most popular multi-criteria decision-making approaches applied to the management of waste electrical and electronic equipment, analyzing how they are used to contribute to the improvement of management strategies along the entire supply chain. For this purpose, a methodological protocol for the collection, selection, and analysis of the scientific literature was applied, identifying 44 papers on which to conduct this study. The results showed that numerous authors have developed multi-criteria approaches, with particular attention to recycling phase. The analytic hierarchy process is the most widespread multi-criteria approach, often coupled with VIKOR, DELPHI, and TOPSIS methods. The numerous decision making criteria adopted cover different reference dimensions: environmental, economic, social, technical, and legal. Considering environmental aspects also in decision making processes means enhancing the relevance of this dimension, as well as encouraging practices that reduce the impact of toxic substances on the environment and living organisms.

Evaluation of the selection of proper metro and tram vehicle for urban transportation by using a novel integrated MCDM approach

This paper presents a novel integrated multi-criteria decision-making model consists of the CRITIC (CRiteria Importance Through Intercriteria Correlation) technique and the EDAS (The Evaluation based on Distance from Average Solution) method to evaluate the selection of the urban rail vehicles operated in the public transport systems. In order to determine the selection criteria in a more realistic perspective, a board of experts consists of seven members, who perform as senior executives in the public transport institutions of the country, was constructed and many round tables meetings were organized with together the members of the board for determining the procedure to follow to reach meaningful and applicable results. At the beginning of the research, approximately fifty selection criteria were determined by the research team and some of them were eliminated during the preparation process of this study. Finally, 22 factors were determined as the selection criteria, which will be used in this research. These factors are the selection criteria directly or indirectly affecting the selection process and results. Also, rail tram types currently operated in the various cities of Turkey were determined as decision alternatives. These options were evaluated by using the proposed integrated MCDM approach. When the obtained results are evaluated, it can be seen that the proposed model has the potential to give very successful results for evaluating the selection process of the urban rail systems. Moreover, it can be implemented as a decision support system by decision-makers, who perform in the public transport authorities. Finally, it can also be applied for decision-making problems faced in the various fields.

Fair and Reasonable Allocation of Trans-Boundary Water Resources Based on an Asymmetric Nash Negotiation Model from the Satisfaction Perspective: A Case Study for the Lancang-Mekong River Bain

The issue of trans-boundary water conflict has become an important factor affecting the relations between basin countries. The key to solve the current conflict problem is to realize the fair and reasonable allocation of trans-boundary water resources. Based on the satisfaction perspective, we developed an asymmetric Nash negotiation model to obtain an optimal and feasible allocation scheme for the trans-boundary water resources. Firstly, based on the two international water laws, we analyzed the influencing factors including water demand differences, resource endowment differences, and water efficiency differences, and by combing with the flexible weight constraint, we built the fair and reasonable allocation pattern for trans-boundary water resources. Secondly, under the constraint of the allocation pattern, we determined the ideal negotiation scheme of each basin country by considering their selection preference. Thirdly, we built a satisfaction degree function and established an asymmetric Nash negotiation model. This is used to build a fair negotiation mechanism among basin countries, and the allocation scheme after negotiation is regarded as the optimal allocation scheme. Lastly, we took the Lancang–Mekong river basin as an example. For this example, the results indicate the following: (1) after considering multiple factors comprehensively, China and Thailand obtained a higher proportion of trans-boundary water resource quota under different preference scenarios, while Myanmar obtained a lower proportion of trans-boundary water resource quota; (2) taking each basin country as the negotiation agent, the optimal allocation scheme with the introduction of fair negotiation mechanism has a higher degree of satisfaction, with an average of over 87.19%, which is more stable and easy to be accepted by all basin countries; (3) from the perspective of the change rate and the average satisfaction of the basin countries, the optimal allocation scheme under the resource endowment preference scenario obtained the highest satisfaction among basin countries. This study aims to improve the practicability and acceptability of trans-boundary water resources allocation, thus providing technical support for reducing trans-boundary water resources conflicts.

Intuitionistic Fuzzy Hierarchical Multi-Criteria Decision Making for Evaluating Performances of Low-Carbon Tourism Scenic Spots

Low-carbon tourism is an effective solution to cope with the goal conflict between developing tourist economy and responding to carbon emission reduction and ecological environment protection. Tourism scenic spots are important carriers of tourist activities and play a crucial role in low-carbon tourism. There are multiple factors affecting the low-carbon performance of a tourism scenic spot, and thus the performance evaluation and ranking of low-carbon tourism scenic spots can be framed as a hierarchical multi-criteria decision making (MCDM) problem. This paper develops a novel method to tackle hierarchical MCDM problems, in which the importance preferences of criteria over the decision goal and sub-criteria with respect to the upper-level criterion are provided by linguistic-term-based pairwise comparisons and the assessments of alternatives over each of sub-criteria at the lowest level are furnished by positive interval values. The linguistic-term-based pairwise comparison matrices are converted into intuitionistic fuzzy preference relations and an approach is developed to obtain the global importance weights of the lowest level sub-criteria. A multiplicatively normalized intuitionistic fuzzy decision matrix is established from the interval-value-based assessments of alternatives and a method is proposed to determine the intuitionistic fuzzy value based comprehensive scores of alternatives. A case study is offered to illustrate how to build a performance evaluation index system of low-carbon tourism scenic spots located at Zhejiang Province of China and show the use of the proposed intuitionistic fuzzy hierarchical MCDM method.

Implementation of fuzzy, Simos and strengths, weaknesses, opportunities and threats analysis for municipal solid waste landfill site selection: Adana City case study

The selection of suitable landfill locations for municipal solid waste has become a top priority, especially in developed countries as a result of rapid population growth, unplanned urbanisation, increasing waste production and the limited area available. However, determining the location of landfill sites is a complex decision-making problem for municipalities and depends on social, environmental, technical and economic factors and regulations. In this study, we combined a geographic information system (GIS), multi-criteria decision-analysis techniques and fuzzy logic to determine the best location for landfill sites in Adana, Turkey, in four steps. Firstly, the threshold values and the coefficient weights of 15 criteria, grouped into environmental and socio-economic factors, were determined by a literature review and expert opinion to select suitable landfill locations. Secondly, selection criteria were standardised using fuzzy logic. Thirdly, we assessed the criteria weights based on their effectiveness on the selection of potential landfill sites using the Simos method. According to the weight coefficients, environmental factors are more important than socio-economic factors. Final maps for each criterion were calculated and overlaid by a GIS. As a result, the final suitability results were divided into four discrete categories: very high, high, moderate and low suitability areas, representing 1%, 76%, 17% and 6% of the location options, respectively. Finally, four different alternative areas were identified as being very highly suitable for landfill locations, which were evaluated in detail using a strengths, weaknesses, opportunities and threats analysis. Three key aspects affect the final decision of a landfill site, in decreasing order of importance: environmental protection, minimising the negative impact on urban life quality and economic issues. Consequently, these results can guide decision-makers (ministries, municipalities, planners, etc.) during the selection of suitable landfill sites in both national and international studies.

Prioritization of Information Security Controls through Fuzzy AHP for Cloud Computing Networks and Wireless Sensor Networks

With the advent of cloud computing and wireless sensor networks, the number of cyberattacks has rapidly increased. Therefore, the proportionate security of networks has become a challenge for organizations. Information security advisors of organizations face difficult and complex decisions in the evaluation and selection of information security controls that permit the defense of their resources and assets. Information security controls must be selected based on an appropriate level of security. However, their selection needs intensive investigation regarding vulnerabilities, risks, and threats prevailing in the organization as well as consideration of the implementation, mitigation, and budgetary constraints of the organization. The goal of this paper was to improve the information security control analysis method by proposing a formalized approach, i.e., fuzzy Analytical Hierarchy Process (AHP). This approach was used to prioritize and select the most relevant set of information security controls to satisfy the information security requirements of an organization. We argue that the prioritization of the information security controls using fuzzy AHP leads to an efficient and cost-effective assessment and evaluation of information security controls for an organization in order to select the most appropriate ones. The proposed formalized approach and prioritization processes are based on International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001:2013. But in practice, organizations may apply this approach to any information security baseline manual.

Waste management barriers in developing country hospitals: Case study and AHP analysis

Healthcare waste management is an essential field for both researchers and practitioners. Although there have been few studies using statistical methods for its evaluation, it has been the subject of several studies in different contexts. Furthermore, the known precarious practices for waste management in developing countries raise questions about its potential barriers. This study aims to investigate the barriers in healthcare waste management and their relevance. For this purpose, this paper analyses waste management practices in two Brazilian hospitals by using case study and the Analytic Hierarchy Process method. The barriers were organized into three categories - human factors, management, and infrastructure, and the main findings suggest that cost and employee awareness were the most significant barriers. These results highlight the main barriers to more sustainable waste management, and provide an empirical basis for multi-criteria evaluation of the literature.

Therapy preferences of patients with lung and colon cancer: a discrete choice experiment

OBJECTIVES

There is increasing interest in studies that examine patient preferences to measure health-related outcomes. Understanding patients' preferences can improve the treatment process and is particularly relevant for oncology. In this study, we aimed to identify the subgroup-specific treatment preferences of German patients with lung cancer (LC) or colorectal cancer (CRC).

METHODS

Six discrete choice experiment (DCE) attributes were established on the basis of a systematic literature review and qualitative interviews. The DCE analyses comprised generalized linear mixed-effects model and latent class mixed logit model.

RESULTS

The study cohort comprised 310 patients (194 with LC, 108 with CRC, 8 with both types of cancer) with a median age of 63 (SD =10.66) years. The generalized linear mixed-effects model showed a significant (P<0.05) degree of association for all of the tested attributes. "Strongly increased life expectancy" was the attribute given the greatest weight by all patient groups. Using latent class mixed logit model analysis, we identified three classes of patients. Patients who were better informed tended to prefer a more balanced relationship between length and health-related quality of life (HRQoL) than those who were less informed. Class 2 (LC patients with low HRQoL who had undergone surgery) gave a very strong weighting to increased length of life. We deduced from Class 3 patients that those with a relatively good life expectancy (CRC compared with LC) gave a greater weight to moderate effects on HRQoL than to a longer life.

CONCLUSION

Overall survival was the most important attribute of therapy for patients with LC or CRC. Differences in treatment preferences between subgroups should be considered in regard to treatment and development of guidelines. Patients' preferences were not affected by sex or age, but were affected by the cancer type, HRQoL, surgery status, and the main source of information on the disease.

Assessment of economic instruments for countries with low municipal waste management performance: An approach based on the analytic hierarchy process

Economic instruments provide significant potential for countries with low municipal waste management performance in decreasing landfill rates and increasing recycling rates for municipal waste. In this research, strengths and weaknesses of landfill tax, pay-as-you-throw charging systems, deposit-refund systems and extended producer responsibility schemes are compared, focusing on conditions in countries with low waste management performance. In order to prioritise instruments for implementation in these countries, the analytic hierarchy process is applied using results of a literature review as input for the comparison. The assessment reveals that pay-as-you-throw is the most preferable instrument when utility-related criteria are regarded (wb = 0.35; analytic hierarchy process distributive mode; absolute comparison) mainly owing to its waste prevention effect, closely followed by landfill tax (wb = 0.32). Deposit-refund systems (wb = 0.17) and extended producer responsibility (wb = 0.16) rank third and fourth, with marginal differences owing to their similar nature. When cost-related criteria are additionally included in the comparison, landfill tax seems to provide the highest utility-cost ratio. Data from literature concerning cost (contrary to utility-related criteria) is currently not sufficiently available for a robust ranking according to the utility-cost ratio. In general, the analytic hierarchy process is seen as a suitable method for assessing economic instruments in waste management. Independent from the chosen analytic hierarchy process mode, results provide valuable indications for policy-makers on the application of economic instruments, as well as on their specific strengths and weaknesses. Nevertheless, the instruments need to be put in the country-specific context along with the results of this analytic hierarchy process application before practical decisions are made.

Identified research directions for using manufacturing knowledge earlier in the product lifecycle

Design for Manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within Computer-Aided Design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customization on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities, or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is how can manufacturing knowledge, depending on its definition, be used earlier in the product lifecycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behavior, and context characteristics of product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product lifecycle.

The application of a multi-parameter analysis in choosing the location of a new solid waste landfill in Serbia

This article presents a process of evaluation and selection of the most favourable location for a sanitary landfill facility from three alternative locations, by applying a multi-criteria decision-making (MCDM) method. An incorrect choice of location for a landfill facility can have a significant negative economic and environmental impact, such as the pollution of air, ground and surface waters. The aim of this article is to present several improvements in the practical process of landfill site selection using the VIKOR MCDM compromise ranking method integrated with a fuzzy analytic hierarchy process approach for determining the evaluation criteria weighing coefficients. The VIKOR method focuses on ranking and selecting from a set of alternatives in the presence of conflicting and non-commensurable (different units) criteria, and on proposing a compromise solution that is closest to the ideal solution. The work shows that valuable site ranking lists can be obtained using the VIKOR method, which is a suitable choice when there is a large number of relevant input parameters.

Shared decision-making--transferring research into practice: the Analytic Hierarchy Process (AHP)

OBJECTIVE

To illustrate how the Analytic Hierarchy Process (AHP) can be used to promote shared decision-making and enhance clinician-patient communication.

METHODS

Tutorial review.

RESULTS

The AHP promotes shared decision-making by creating a framework that is used to define the decision, summarize the information available, prioritize information needs, elicit preferences and values, and foster meaningful communication among decision stakeholders.

CONCLUSIONS

The AHP and related multi-criteria methods have the potential for improving the quality of clinical decisions and overcoming current barriers to implementing shared decision-making in busy clinical settings. Further research is needed to determine the best way to implement these tools and to determine their effectiveness.

PRACTICE IMPLICATIONS

Many clinical decisions involve preference-based trade-offs between competing risks and benefits. The AHP is a well-developed method that provides a practical approach for improving patient-provider communication, clinical decision-making, and the quality of patient care in these situations.