Multiple-criteria Decision-making Analysis Research Articles

Multicriteria Decision-Making in Public Security: A Systematic Review

The Multiple Criteria Decision-Making/Analysis (MCDM/A) methods have been widely used in several management contexts. In public security, their use enhances managerial decision-making by considering the decision-maker’s preference structure and providing a multidimensional view of problems. However, methodological support for their applications in this field lacks clarity, including selecting appropriate methods, addressing pertinent problematics, and identifying alternatives and criteria. To address this gap, this article conducts a Systematic Literature Review (SLR) to diagnose the state of the art and identify the main directions of the research in multicriteria models applied to public security management. The research methodology involves five main research questions, and the extraction and analysis of data from 51 articles selected through a structured filtering process. The analysis includes identifying the number of publications and citations, as well as listing the MCDM/A approaches and issues employed. Furthermore, the criteria used and the number of criteria considered are discussed, as well as the method employed. Finally, the identification of the main research directions in MCDM/A models applied to public security is presented. The findings suggest that prioritization and classification are common problematics, social criteria are frequently considered, and the AHP method is widely used, often employing fuzzy sets and hybrid models.

Outbound open innovation effectiveness measurement between big organizations and startups using Fuzzy MCDM

PurposeThis research aims to identify the key factors affecting Outbound Open Innovation between Startups and Big organizations using the multiple criteria decision-making analysis (MCDM) approach. The MCDM technique ranks the four key factors identified from the literature study that can help to improve collaboration opportunities with Startups.Design/methodology/approachIdentification of key factors affecting Outbound Open Innovation between Startups and big organizations based on extant literature. A questionnaire is prepared based on these four identified key factors to gather views of the startup's employees, from the designer level to the startup's founder. MCDM techniques are used to evaluate the questionnaire. The ensemble technique is used to rank the key factors coming from three different MCDM methods.FindingsThe findings from the MCDM approach and Ensemble techniques give insight to the big organizations to facilitate outbound Open Innovation effectively. It also provides insight into the requirements of the startups and the kind of support they seek from the big organizations. The ranking can help the big organization close the gaps and make an informed decision to increase the effectiveness of the collaborations and boost innovation.Originality/valueThis is a unique research work where the MCDM approach is used to identify the ranking of key factors affecting outbound open innovation between startups and big organizations. The MCDM technique is followed by the ensemble method to rationalize the findings. Technology Relevance ranks highest, followed by Innovation Ecosystem, Organization commitment and Knowledge Sharing.

GeoClust: Feature engineering based framework for location-sensitive disaster event detection using AHP-TOPSIS

Disaster event detection aims to identify events like terrorist attacks, fire incidents, stampede incidents, building collapse, etc., reported in the online news articles or social media. Place of occurrence of disaster event is a significant feature associated with events for location-sensitive disaster event detection. Efficient feature selection and their augmentation with location information can contribute towards the evolution of traditional approaches and their adoption for location-sensitive disaster event detection leading to improvement in the overall process as a whole. Since the evaluation of event detection techniques deliberates various intrinsic and extrinsic performance metrics, the decision-making for the selection of feature sets is treated as a Multiple-Criteria Decision Making (MCDM) problem. This paper proposes a framework, GeoClust, that is based on feature engineering of traditional textual features in order to enhance their capability for improved location-sensitive disaster event detection. The framework augments context-free and context-based textual feature sets with feature sets of place of occurrence of the events and evaluates their performance using unsupervised machine learning algorithms for various performance metrics. Finally, the best feature set is selected using AHP-TOPSIS technique of MCDM in order to tune the system for automatic and efficient location-sensitive disaster event detection in real-time. Extensive set of experiments have been performed in order to evaluate the framework on a dataset of online news articles reporting disaster events about terrorist attacks, fire incidents, stampede incidents, building collapse and maoist attacks happened at different locations in India. The results show that the location-augmented feature sets significantly improve performance of location-sensitive disaster event detection as compared with traditional feature sets. The results also demonstrate that the context-based feature sets with location-augmentation are ranked higher than the context-free feature sets in MCDM analysis.

WIND ENERGY POTENTIAL IN YOBE, NORTHEASTERN NIGERIA: AN AIDED TECHNIQUE THROUGH GEOGRAPHIC INFORMATION SYSTEM AND REMOTE SENSING

The role of wind energy and other renewable energy sources in combating climate change cannot be overemphasized. Many countries including Nigeria have so far implemented policy measures to promote renewable energy. Moreover, the most important step to be taken in order to effectively harness renewable energy is to estimate their potentials in terms of viability and sustainability. This study was focused on evaluate the wind energy potential zones in Yobe State, northeast Nigeria, using integrated multiple criteria decision-making analysis (MCDA), Geographical Information System and Remote Sensing (GIS-RS), and Analytic Hierarchy Process (AHP) methods. Weibull distribution statistical method was applied to validate the GIS-RS based analysis. The study revealed a wind speed variation ranged from 1.48 to 7.15 m/s and an average annual wind power density of about 80 W/m2. About 90 % of study area has about 90 % wind power density of about 85 W/m2 and below while the remaining 10 % is covered by wind power density ranged between 85 and 616.89 W/m2. The results of the study showed that the area is viable for wind power generation and distribution. Furthermore, the wind energy resource will yield favorable economic gain for the study area. Thus the study concluded that Yobe is a suitable area for wind power generation both at medium scale generation, and standalone connection system.

Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices

Illegal dump sites (IDS) pose significant risks to human and the environment and are a pressing issue worldwide. Due to their secretive nature, the detection of IDS is costly and ineffective. In this study, an analytical framework was developed to detect probable IDSs in rural and remote areas using nighttime light (NTL) as a proxy for populated areas. An IDS probability map is produced by aggregation of Landsat-8 and Suomi NPP satellite imagery, multiple-criteria decision-making analysis, and classification tools. Six variables are considered, including modified soil adjusted index, land surface temperature, NTL, highway length, railway length, and the number of landfills. Vulnerability of the inhabitants on reserve lands was assessed using three sample regions. The method appears effective in reducing potential IDSs. Only about 7% of the 31,285 km2 study area are identified as probable IDS, being classified as “very high” and “high”. Landfills without permit are found more effective in lowering IDS occurrence. Spatial distributions of reserve lands and the maturity of highways network nearby may be more important than the length of railways when assessing the inhabitant vulnerability due to IDS. Highway length is the most decisive factor on IDS probability among all classes, with membership grades ranging from 0.99 to 0.55. Land surface temperature appears less effective for the identification of smaller scale IDS. NTL is more prominent on IDS probability in the “very high” class, with a membership grade of 0.80. The finding suggests that populated areas represented by NTL is a priori of IDS.

Novel multiple criteria decision-making analysis under m-polar fuzzy aggregation operators with application.

Aggregation is a very efficient indispensable tool in which several input values are transformed into a single output value that further supports dealing with different decision-making situations. Additionally, note that the theory of m-polar fuzzy (mF) sets is proposed to tackle multipolar information in decision-making problems. To date, several aggregation tools have been widely investigated to tackle multiple criteria decision-making (MCDM) problems in an m-polar fuzzy environment, including m-polar fuzzy Dombi and Hamacher aggregation operators (AOs). However, the aggregation tool to deal with m-polar information under Yager's operations (that is, Yager's t-norm and t-conorm) is missing in the literature. Due to these reasons, this study is devoted to investigating some novel averaging and geometric AOs in an mF information environment through the use of Yager's operations. Our proposed AOs are named as the mF Yager weighted averaging (mFYWA) operator, mF Yager ordered weighted averaging operator, mF Yager hybrid averaging operator, mF Yager weighted geometric (mFYWG) operator, mF Yager ordered weighted geometric operator and mF Yager hybrid geometric operator. The initiated averaging and geometric AOs are explained via illustrative examples and some of their basic properties, including boundedness, monotonicity, idempotency and commutativity are also studied. Further, to deal with different MCDM situations containing mF information, an innovative algorithm for MCDM is established under the under the condition of mFYWA and mFYWG operators. After that, a real-life application (that is, selecting a suitable site for an oil refinery) is explored under the conditions of developed AOs. Moreover, the initiated mF Yager AOs are compared with existing mF Hamacher and Dombi AOs through a numerical example. Finally, the effectiveness and reliability of the presented AOs are checked with the help of some existing validity tests.

Dealing with Water Conflicts: A Comprehensive Review of MCDM Approaches to Manage Freshwater Ecosystem Services

This paper presents a comprehensive review of the application of Multiple-Criteria Decision-Making (MCDM) approaches exclusively to water-related freshwater ecosystem services. MCDM analysis has been useful in solving conflicts and it works well in this framework, given the serious conflicts historically associated with water use and the protection of freshwater ecosystems around the world. In this study, we present a review of 150 papers that proposed the use of MCDM-based methods for the social, economic, or ecological planning and management of water ecosystem services over the period 2000–2020. The analysis accounts for six elements: ecosystem service type, method, participation, biogeographical realm, waterbody type, and problem to solve. A Chi-square test was used to identify dependence between these elements. Studies involving the participation of stakeholder groups adopted an integrated approach to analysing sustainable water management, considering provisioning, regulating, and cultural services. However, such studies have been in decline since 2015, in favour of non-participatory studies that were strictly focused on ecological and provisioning issues. Although this reflects greater concern for the health of freshwater ecosystems, it is a long way removed from the essence of ecosystem services, which entails an integrated approach to the interrelationships between hydrology, landscapes, ecology, and humans.

Multimodal transportation via TSR for effective Northern logistics

PurposeThis paper aims to identify the factors that experts consider important for the effective operation of multimodal transport using the Trans-Siberian Railway (TSR) in the Russian Far East and to also establish operational strategies that enable Korean logistics companies to use TSR efficiently for multimodal transportation.Design/methodology/approachA total of 65 questionnaires were distributed to predominantly Korean logistics companies/shippers with experience in TSR and 19 replies were received. The majority of the respondents are senior executives with at least 10 years of work experience. To identify the factors and present strategies, this study applied the strengths, weaknesses, threats and opportunities -fuzzy analytic hierarchy process (FAHP) method, which is based on complex multiple-criteria decision-making (MCDM) that facilitates decision-makers in considering an operational strategy by calculating the importance of input factors.FindingsResults from the FAHP indicate that the Strengths is the main factor affecting TSR multimodal transport decisions, followed by the Threats, Opportunities and Weakness factors. In terms of rank, reduction of transportation distance and time are determined as the most crucial factor from the perspective of Korean logistics companies, followed by the northern logistics development plan, the Russian Government's willingness to the development plan, political instability and possible linkages with the Korean ports.Originality/valueThrough a hybrid MCDM analysis of Korean logistics companies, three operational strategies were proposed: foreign direct and small investments in the Russian Far East, Korean logistics companies implementing a cold-chain logistics service in the fishery industry and a government-led “Block Train Integration System” to secure the minimum threshold volume of cargo for small and medium-sized companies.

Mobility alternative selection for US automobiles potential evolution to autonomous vehicles

Quantitative and multiple-criteria decision-making analysis are performed to rank transportation modes for consumer needs. Each need is assumed to have fourteen mobility attributes. A consumer in 2018 can own a vehicle, call a taxi or hail ride. The quantitative analysis is cost per mile. For owning a vehicle, lifecycle cost includes vehicle price, registration and insurance charges, operating expenses, and financing. For taxi or hailing service, cost includes a base fare, booking fee, per minute charge and per mile charges. The cost to own a vehicle is about $1.43/mile while that for a taxi or a ride-hailing is around $3 per mile. For qualitative evaluation, mobility attributes are ranked for each transportation need, and weights of each attributes are calculated using linear programming. This evaluation shows owning an internal combustion engine is the most preferred transportation mode while ride hailing is the least preferred option in 2018. This study can be used to identify mobility alternatives given consumer needs in future as automobiles evolve to autonomous vehicles.

Mobility alternative selection for US automobiles potential evolution to autonomous vehicles

Quantitative and multiple-criteria decision-making analysis are performed to rank transportation modes for consumer needs. Each need is assumed to have fourteen mobility attributes. A consumer in 2018 can own a vehicle, call a taxi or hail ride. The quantitative analysis is cost per mile. For owning a vehicle, lifecycle cost includes vehicle price, registration and insurance charges, operating expenses, and financing. For taxi or hailing service, cost includes a base fare, booking fee, per minute charge and per mile charges. The cost to own a vehicle is about $1.43/mile while that for a taxi or a ride-hailing is around $3 per mile. For qualitative evaluation, mobility attributes are ranked for each transportation need, and weights of each attributes are calculated using linear programming. This evaluation shows owning an internal combustion engine is the most preferred transportation mode while ride hailing is the least preferred option in 2018. This study can be used to identify mobility alternatives given consumer needs in future as automobiles evolve to autonomous vehicles.

Supply chain analysis of e-waste processing plants in developing countries.

e-Waste is generated at the staggering rate of 6.1 kg per person. In 2016, 44.7 million tonnes was generated globally, and the amount is estimated to reach 52 million tonnes annually by 2021. The management of this huge quantity of e-waste is a major problem across the world, primarily in developing countries. An effective e-waste management system is linked to the sustainability of e-waste processing plants, and currently it is plagued with different supply chain network (SCN) issues. To address the issues and challenges of SCN, there is a need to prioritize and address the constructs. The study thus combines two multiple criteria decision making (MCDM) tools, the analytical hierarchy process (AHP) and quality function deployment, to prioritize the constructs identified from literature and supported by field studies. The house of quality (HOQ) has been used for analyzing different stakeholders' requirements. The degree of importance for HOQ-1 has been derived using the AHP tool. The study revealed the most intriguing issues and challenges of e-waste processing plants considering the requirements of different stakeholders. The results showed formal collection, storage, semi-informal collection, and e-waste quality to be the major issues of processing plants. The findings obtained from MCDM analysis have been validated though two case studies in India and China. If the results obtained are applied to the existing e-waste SCN, a holistic e-waste management system can be achieved for developing countries.

Dispersion of relative importance values contributes to the ranking uncertainty: Sensitivity analysis of Multiple Criteria Decision-Making methods

Multiple Criteria Decision-Making (MCDM) methods are widely used in research and industrial applications. These methods rely heavily on expert perceptions and are often sensitive to the assumptions made. The reliability and robustness of MCDM analysis can be further tested and verified by a computer simulation and sensitivity analysis. In order to address this, five different MCDM approaches, including Weighted Sum Model (WSM), Weighted Product Model (WPM), revised Analytic Hierarchy Process (rAHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and COmplex PRoportional ASsessment (COPRAS) are explored in the paper. Real data of the case study for assessing housing affordability are used for testing the robustness of alternative ranking and finding the most sensitive criteria to the change of criterion weight. We identify the most critical criteria for any and best ranking alternatives. The paper highlights the significance of sensitivity analysis in assessing the robustness and reliability of MCDM outcomes. Furthermore, randomly generated and model-based data sets are used to establish relationship between the dispersion of relative importance values of alternatives and ranking uncertainty. Our findings demonstrate that the dispersion of relative importance values of alternatives correlate with the Euclidian distances of aggregated values. We conclude that the dispersion of relative importance values contributes directly to the ranking uncertainty and can be used as a measure for identifying critical criteria.

Mapping the Conceptual Relationship among Data Analysis, Knowledge Generation and Decision-making in Industrial Processes

In accordance with Industry 4.0, data should be used for process and/or system integration, including the decision-making process. In this context, this paper aims to relate the process of decision-making with process mining through concept maps, assisting the areas of Data Engineering and Analytics. To do so, some Multiple-Criteria Decision-Making/Analysis (MCDM/A) methods will be addressed in order to conceptually highlight the relationship between knowledge and decision dimensions. As a result, the definitions about these fields of study allow perceiving an information and knowledge conciliation approach promoted by the integrated view of process mining and MCDM/A methods.

Multiple criteria decision making in fisheries management: Experiences and implications

Abstract The management of renewable resources takes place in a paradigm where several (conflicting) objectives are relevant to take into account. This feature is shared with other fields of management, involving economic, environmental and social issues. Hence, it is natural to employ a management model incorporating multiple objectives in the decision process. Multiple criteria decision making (MCDM) has attained interest within a wide range of management fields. This article reviews applications related to fisheries. The purpose is to clarify how the different methodologies can contribute to management. It is hoped, this leaves the field of MCDM more tangible, visible and attractive to managers. It will be emphasized that an MCDM analysis can be carried out in many different ways and for different purposes, and that the outcome depends on the assumptions made.