Multi-criteria Decision-making System Research Articles

Optimal Wind Farm Siting Using a Fuzzy Analytic Hierarchy Process: Evaluating the Island of Andros, Greece

In recent decades, fuzzy logic and fuzzy multi-criteria decision-making systems have been applied in several fields. This paper aims to determine the optimal wind farm siting solution in a fuzzy environment. Therefore, the main research question of the present paper is whether and to what extent the uncertainty in the researcher’s judgments affects the ranking of wind farm siting solutions. The fuzzy analytical hierarchy method is applied to an existing case study of wind farm siting on the island of Andros, examining the stability of the final priorities of the alternatives under a regime of gradual increases in ambiguity, as well as whether the introduced ambiguity in the model corresponds to any uncertainty the researcher has during the process of scoring the criteria and alternatives. Five assessment criteria (wind potential, ground slope, distance from road network, distance from high-voltage network, and social acceptance of local population) and eight eligible suitable alternatives (A1–A8) for wind farm siting are considered in the computations. The methodology includes the fuzzification of initial decision-maker judgments, the calculation of fuzzy intermediate priorities (weights), the defuzzification of fuzzy intermediate priorities (weights), and the synthesis of intermediate priorities into final priorities of alternatives, according to the procedures of the crisp AHP (CAHP). Under the assumptions of the initial case study, the results show that the final priorities are quite robust when faced with increased ambiguity. In almost all the examined cases, the alternative initially chosen as the best, A1, is dominant, followed by A3. In addition, in all cases, social acceptance favors alternative A1, and wind velocity favors alternative A8. Therefore, fuzzy multi-criteria methods can be applied to determine an optimal wind farm siting solution when criteria with qualitative characteristics are used and the manifestation of preferences involves strong elements of subjectivity.

Efficient fuzzy multi-criteria decision-making for optimal college location selection: A comparative study of min–max fuzzy TOPSIS approach

Ranking fuzzy values is often essential in fuzzy multi-criteria decision-making (FMCDM) systems since it is established on the comparison of hazy data pairs, even though the comparison needs temporal complexity. Then to provide a fuzzy MCDM approach under min–max operations for avoiding the fuzzy comparison. By using fuzzy TOPSIS’s min and max operations, the suggested method produces two positive and negative ideal solutions of each alternative. Here, before discussing the effectiveness of the matrices between the alternatives, first to analyze the weighted strength and weakness indices from both positive and negative indices. The results of the method have been described using a straightforward case study of a proper location for creating a college. The best location can be identified using this method based on various criteria. The outcomes are also compared with the suggested method for its effectiveness. Additionally, this problem analyzes the fuzzy TOPSIS approach in an interval-valued number for greater efficiency.

Evaluating the feasibility of constructing shopping centers on urban vacant land through a spatial multi-criteria decision-making model

Shopping centers are among the major economic and commercial places in cities in terms of social function, urban sustainability, environment, etc. Therefore, it is crucial to determine suitable locations for building new shopping centers. In this regard, urban vacant lands can be considered as high-potential locations to transform the urban landscape and enhance socio-economic development. The main purpose of this study is to assess the potential of urban vacant lands based on a spatial multi-criteria decision-making (SMCDM) system for building shopping centers in 22 districts of Tehran. In this study, first, 16 effective spatial criteria for locating the new shopping centers were identified using expert opinions and a literature review. The best-worst method (BWM) and the Min/Max method were used to calculate the weight and standardized values of each criterion. Then, the ordered weighted averaging (OWA) method was used to prepare a potential map of urban vacant lands for the construction of a shopping center under different decision-making scenarios, including very optimistic, optimistic, intermediate, pessimistic, and very pessimistic. Finally, the spatial distribution of potential locations in the 22 districts of Tehran was investigated. The results showed that among the different criteria, the distance from the highway networks and distance from public transportation stations had the most weight, whereas the distance from fault and distance from stream networks had the least weight. The number of vacant lands with a very high potential for building a shopping center in Tehran based on very pessimistic, pessimistic, intermediate, optimistic and very optimistic scenarios was obtained at 29, 95, 105, 122, and 224, respectively. An increase in the degree of optimism in the attitude of decision-makers or investors increased the number of available options in the very high potential category. Under all scenarios, all vacant lands in 10 of Tehran’s 22 districts with very high potential for building shopping centers. The results of the proposed model in this study can be useful and practical for a wide range of planners, decision-makers, and investors with different mental attitudes and risk tolerance. Close attention to these results can contribute to achieving sustainable urban development.

An OPA-F-Based Single-Valued Neutrosophic Fuzzy MARCOS Approach with Dombi Aggregation Operators for Evaluating Indoor Sex Work Risk in the Economy

The primary risk factor affecting the economy of indoor sex work is examined in this study. Policymakers must thoroughly examine this factor's complex effects since they have a direct bearing on the financial security of employees and the industry's economic dynamics. Interventions can be designed to improve overall economic well-being in the setting of indoor sex work by addressing and mitigating this significant risk. A hybrid multi-criteria decision-making (MCDM) called Fuzzy Ordinal Priority Approach-Single-Valued Neutrosophic Fuzzy Measurement of Alternatives and Ranking according to COmpromise Solution (OPA-F-SVNF-MARCOS) is created to determine the most important parameter in the study. A thorough multi-criteria decision-making (MCDM) system, such as the MARCOS approach, allows for the consideration of a wide range of elements that impact the danger of indoor sex work, such as social dynamics, legal frameworks, economic indicators, and public health concerns. In the suggested method, OPA-F establishes the weights for the criteria, and SVNF-MARCOS assigns a ranking to each option. Furthermore, Dombi aggregation operators capture the interaction between risk variables and the degrees of uncertainty they are linked with. This study helps those involved in this delicate field—policymakers, law enforcement, and social care providers—make decisions that are better informed and grounded in evidence.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

Survey of MCDM-Based Software Engineering Method

Multi-criteria decision making (MCDM) is an operation research subfield (OR). Decision-making is primarily a process involving various actors, including individuals, groups of individuals, institutions, and the state. As a discipline, multi-criteria decision-making is relatively new Many researchers have devoted their time to developing new MCDM models and techniques since the 1950s and 1960s, when the foundations of modern multi-criteria decision-making methods were laid. Research and development in the area have intensified recently and appear to be developing tremendously. Few attempts have been made to systematically convey the theoretical foundations and advancements of multi-criteria decision-making systems, despite the widespread and intense development. The framework for evaluating judgments and the methodological options are still up for debate. In this paper reviews of MCDM/MADM technologies will be included describing their scenarios. Making a decision that is more appropriate than others to solve a particular problem is the goal of decision-making (DM), so this study presents a review of the advantages and disadvantages of different decision-making techniques, specifically in the field of software engineering.

Use analytic hierarchy and gray relational analysis for the evaluation of oily sludge treatment technology

ABSTRACTThere are many oily sludge treatment technologies with great development prospects, and due to the lack of an objective and scientific technology evaluation system, there have been different opinions and views on the selection of technologies. To address this issue, this paper establishes a multi-criteria decision system based on hierarchical analysis and gray decision-making, discusses the weights of oily sludge treatment technologies in terms of economic benefits, technology, environment and social indicators, and categorizes the relevant impact indicators into target level, criterion level and program level, etc. So as to analyze them qualitatively and quantitatively. The results show that in the hierarchical analysis decision system, the order of influence of the weight of each indicator on the research objectives is as follows: technical and environmental impact indicators occupy an absolute advantage for the treatment and disposal of oily sludge, with a weight of 0.5886 for environmental indicators and 0.2074 for technical indicators, whose main impact indicators include the resourceization of oily sludge with a weight of 0.6104 and the pollution degree of soil with a weight of 0.5115. Because X2 shows a high correlation in the evaluation model, the preferred oily sludge and blast furnace slag catalytic pyrolysis treatment, a comprehensive evaluation of oily sludge treatment technologies will help researchers to have a good understanding of the recent developments and future research directions.

Selection of Pin Component Sub Suppliers for Checking Fixture Products Using an Analytical Hierarchy Process Approach

The objective of this study is to employ a multi-criteria decision-making system technique to identify the most optimal supplier in the automotive components sector. When selecting a supplier, it is crucial to evaluate the pros and downsides of each source in order to secure the most suitable one. Bahana, Ltd is a company that specializes in the production of plastic components, molds, jigs, and equipment. During the production process of inspecting fixture items, pin components are required to assemble the components that need to be assembled. At now, there are no explicit guidelines for choosing suppliers, and the selection process relies solely on subjective evaluations. Consequently, it is necessary to establish supplier selection criteria through the utilization of the Analytical Hierarchy Process (AHP) methodology. This strategy is highly suitable for resolving decision-making challenges. Bahana, Ltd employs pairwise comparisons to evaluate four criteria: pricing, quality, communication, and location. Bahana, Ltd has the highest criteria value of 0.36 in terms of quality, followed by a value of 0.33 in terms of pricing. Hence, the paramount criteria in supplier selection are quality and pricing. According to the AHP calculation results, PT A was determined to be the optimal choice as it obtained the highest weight value in comparison to other suppliers.

Imbalance-learning road crash assessment under reduced visibility settings: A proactive multicriteria decision-making system

Road crash prediction is a fundamental key in designing efficient intelligent transportation systems. There has been a pronounced progress in the use of machine learning models for crash events assessment by the transportation safety research community in recent years. However, little attention has been paid so far to evaluating reduced-visibility crash occurrences within a heuristic ensemble system. This study presents a proactive multicriteria decision-making system that can predict crash occurrences based on real-time roadway properties, land zones’ characteristics, vehicle telemetry, driver inputs and weather conditions collected using a desktop driving simulator. A key novelty of this work is implementing a genetic algorithm-based feature selection approach along with ensemble modeling strategies using AdaBoost, XGBoost and RF techniques to establish effective crash predictions. Furthermore, since crash events occur in rare instances tending to be underrepresented in the dataset, an imbalance-learning methodology to overcome the issue was adopted on the basis of several data resampling approaches to increase the predictive performance namely SMOTE, Borderline-SMOTE, SMOTE-Tomek Links and ADASYN strategies. To our knowledge, there has been a limited interest at adopting an ensemble-based imbalance-learning strategy examining the impact of real-time features’ combinations on the prediction of road crash events under reduced visibility settings.

A Scenario-Based Multi-Criteria Decision-Making Approach for Allocation of Pistachio Processing Facilities: A Case Study of Zarand, Iran

Site selection and allocation of manufacturing and processing facilities are essential to sustainable economic productivity of a given product while preserving soil, the environment, and biodiversity. An essential criterion when evaluating various approaches to model land suitability for pistachio processing facilities is their adaptability to accommodate diverse perspectives and circumstances of managers and decision makers. Incorporating the concept of risk into the decision-making process stands as a significant research gap in modeling land suitability for pistachio processing facilities. This study presents a scenario-based multi-criteria decision-making system for modeling the land suitability of pistachio processing facilities. The model was implemented based on a stakeholder analysis as well as inclusion of a set of influential criteria and restrictions for an Iranian case study, which is among the top three producers. The weight of each criterion was determined based on the best-worst method (BWM) after the stakeholder analysis. Then, the ordered weighted averaging (OWA) model was used to prepare maps of spatial potential for building a pistachio processing factory in different decision-making scenarios, including very pessimistic, pessimistic, intermediate, optimistic, and very optimistic attitudes. Finally, the sensitivity analysis of very-high- and high-potential regions to changes in the weight of the effective criteria was evaluated and proved that the most important criteria were proximity to pistachio orchards, proximity to residential areas, proximity to the road network, and proximity to industrial areas. Overall, 327 km2 of the study area was classified as restricted, meaning that they are not suitable locations for pistachio processing. The average estimated potential values based on the proposed model for very pessimistic, pessimistic, intermediate, optimistic, and very optimistic scenarios were 0.19, 0.47, 0.63, 0.78, and 0.97, respectively. The very-high-potential class covered 0, 0.41, 8.25, 39.64, and 99.78 percent of the study area based on these scenarios, respectively. The area of suitable regions for investment decreased by increasing risk aversion in decision making. The model was more sensitive to changes in the weights of proximity to residential areas, proximity to pistachio orchards, and proximity to transportation hubs. The proposed approach and the achieved findings could be of broader use to respective stakeholders and investors. Given the suitability of arid regions for planting pistachio and its relatively high profitability, the local authorities and decision makers can promote further expansion of the orchards, which can lead to better welfare of farmers and reducing rural-urban migration in the region.

SecSkyline: Fast Privacy-Preserving Skyline Queries Over Encrypted Cloud Databases

The well-known benefits of cloud computing have spurred the popularity of database service outsourcing, where one can resort to the cloud to conveniently store and query databases. Coming with such popular trend is the threat to data privacy, as the cloud gains access to the databases and queries which may contain sensitive information, like medical or financial data. A large body of work has been presented for querying encrypted databases, which has been mostly focused on secure keyword search. In this paper, we instead focus on the support for secure skyline query processing over encrypted outsourced databases, where little work has been done. Skyline query is an advanced kind of database query which is important for multi-criteria decision-making systems and applications. We propose SecSkyline, a new system framework building on lightweight cryptography for fast privacy-preserving skyline queries. SecSkyline ambitiously provides strong protection for not only the content confidentiality of the outsourced database, the query, and the result, but also for data patterns that may incur indirect data leakages, such as dominance relationships among data points and search access patterns. Extensive experiments demonstrate that SecSkyline is substantially superior to the state-of-the-art in query latency, with up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$813\times$</tex-math></inline-formula> improvement.

A Scenario-Based Spatial Multi-Criteria Decision-Making System for Urban Environment Quality Assessment: Case Study of Tehran

Spatial evaluation of urban environment quality (UEQ) is a key prerequisite in urban planning and development. The main goal of this study is to present a scenario-based spatial multi-criteria decision-making system for evaluating UEQ. Therefore, stakeholder involvement was conducted and eight environmental criteria and six spatial-functional criteria were identified for five districts of Tehran. The weight of the effective criteria was calculated using the analytic hierarchy process (AHP) model. Then, the ordered weighted averaging (OWA) model was used to prepare UEQ maps in different scenarios, including very pessimistic, pessimistic, intermediate, optimistic, and very optimistic. Finally, the spatial distribution of the district population in different classes of UEQ was evaluated. Among the spatial-functional and environmental criteria, the sub-criteria of population density and air pollution, respectively, had the greatest impact on UEQ. In very pessimistic, intermediate, optimistic, and very optimistic scenarios, approximately 76.7, 51.8, 36.4, 23.7, and 9.8 km2 of the studied area had unsuitable UEQ conditions, respectively. In the very pessimistic scenario, about 37,000 and 1,500,000 people lived in areas with suitable and unsuitable UEQ conditions, respectively. In the very optimistic scenario, the population increased to over 917,000 in areas with suitable UEQ and decreased to 336,000 in those with unsuitable UEQ conditions in terms of both environmental and spatial-functional criteria. The research results showed that a high percentage of the population in the study area live under unsuitable UEQ conditions, which indicates the need for attention to improving the current UEQ conditions. The proposed approach is timely to gain a better understanding of the adverse impact of climate change on human well-being in marginal societies and how climate-resilient urban planning can play a significant role.

Emergency logistics centers site selection by multi-criteria decision-making and GIS

Public health events and natural disaster emergencies often cause logistical disruptions and shortages of supplies for residents in the affected areas. The construction of emergency logistics centers (ELCs) can enable relief to reach the affected areas faster, so the ELCs site selection is critical. This study proposes a novel approach based on Multi-Criteria Decision-Making (MCDM) and Geographic Information System (GIS) to solve the ELCs site selection problem. Firstly, nine criteria affecting ELCs site selection are identified, the criteria spatial data are collected and the criteria normalized layers are created using GIS. Then, the weights of the criteria are determined by combining the entropy weight method with the Criteria Importance Though Intercrieria Correlation method (CRITIC), and the ELCs site selection suitability map is obtained using superposition analysis. Finally, the ranking of ELCs alternatives is obtained using the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. A case study is conducted in Xi'an which is the largest city in Northwest China, and the results indicate that (1) the population density criterion (C9) has a weight of 0.581, which accounts for the largest proportion of weight. Alternative A9 (near the New City Plaza) ranks highest among the 13 alternatives. (2) Sensitivity analysis shows that the ranking is very sensitive to changes in the criterion weights, which verifies the rationality of the combined assignment method. (3) The comparative analysis with the TOPSIS method shows that the rankings obtained by the method proposed in this paper are stable, which verifies the feasibility of the siting model.

Planning urban underground space from urban emergency evacuation: A digital layout planning method

In recent years, the vulnerability of cities has heightened the threat of disasters, thereby endangering the safety of urban population. To mitigate these risks, the establishment of well-planned shelter system is of utmost importance for effective emergency response. Urban underground space (UUS) stands out as a promising solution for providing shelter during times of disasters, owing to its distinct characteristics of confinement and stability. However, the majority of UUS do not function directly for emergency evacuation, leading to neglect at the planning phase. It is valuable to study an applicable planning technique to prospectively consider emergency shelter services of UUS. In this paper, based on digital tools, a method for the rapid quantitative extraction and analysis of a dynamic population distribution is proposed to quantify demand for emergency evacuation. A multi-step greedy algorithm and a social force model (SFM) with specialized parameters are constructed to realize the simulation and analysis on a digital platform with integrated urban models. A multi-criteria decision-making system for evaluating UUS layout is established using the technique for order of preference by similarity to ideal solution (TOPSIS). Finally, a case study in Nanjing built-up area is presented to demonstrate the performance of the proposed method. The proposed method can expand the research perspective and data-driven toolkit for underground space planning.

Evaluation of enhanced oil recovery methods for mature continental heavy oil fields in China based on geology, technology and sustainability criteria

Enhanced oil recovery (EOR) methods in mature oil fields play an irreplaceable role in supporting the long-term and stable production of crude oil. This study establishes a unified and comparable evaluation index system based on geology, technology and sustainability, re-evaluates the existing EOR projects focused on heavy oil extraction by utilizing hybrid AHP-CRITIC-FCE models, and selects the most efficient, economic, and low-carbon footprint EOR technologies to facilitate the development of mature Chinese heavy oil fields. Three main criteria and nineteen subcriteria are considered in the multi-criteria decision-making system. The weights given to criteria are determined by combining the subjective AHP and objective CRITIC methods. The fuzzy comprehensive evaluation method is adopted to prioritize the performance of the EOR process. Empirical analysis is conducted on seven typical heavy oil technologies. The study finds that geological factors account for the largest proportion, and the top five indicators in terms of weight are buried depth, recovery factor, viscosity, operating cost per tonne of oil and reservoir thickness. Three technologies are recommended for potential future green footprint heavy oil EOR methods. This study combines both expert judgment and field data appraisal to help decision makers better plan the future strategic layout of EOR technology.

Bibliometric Analysis on Sustainability Assessment Methods in the Bioeconomy

Sustainability is meeting the needs of today without compromising the ability of future generations to meet their own needs. Sustainability assessment is a complex process because it needs to cover multidisciplinary aspects – environmental, economic, and social. The sustainability assessment is created by combining different indicators, and the resulting single holistic value can be used as an indicator for comparison. A successful economic transition towards a bioeconomy can contribute to the achievement of many sustainability goals. In this paper, a bibliometric analysis method is used to analyse Open Access articles from the Web of Science database using bibliometric VOSviewer software. The relationship between sustainability assessment methods and associated keywords is explored through a biometric analysis. A literature review is conducted on the methods and tools of assessing the sustainability of bioresources. The authors have summarised the use of sustainability assessment methods and tools, and their characteristics. The obtained results show that keywords' sustainability assessment methods, in publication have become particularly popular since 2015, as the number of publications increases by more than 100 publications every year. The biggest connection with keywords are keywords 'life-cycle assessment', 'performance', 'management', 'framework', 'model', and 'impact'. Life-cycle assessment, material flows analysis, multi-criteria decision analysis, system dynamics and modelling are popular for assessing sustainability. Each method has advantages and disadvantages; however, it is possible to combine several tools to improve the assessment.

An ANP-TOPSIS model for tourist destination choice problems under Temporal Neutrosophic environment

Decision-making is a systematic process of solving real-world problems that provide an optimal solution after checking the achievable set of alternatives. As decision-making theory plays an increasingly important role in real life, there is more and more research on decision-making. All possible options are sorted through a multi-criteria decision-making system, allowing users to choose the suitable solution. In the face of many alternatives, the lack of appropriate skills and limited time has led to eventful human judgments. In this framework, the paper introduces the definition of a Temporal Complex neutrosophic set (TCNS) and the operations and properties of TCNS. Then, the TCNS-ANP-TOPSIS model based on TCNS is presented, which extends the ANP model to determine the weights for group multi-criteria and TOPSIS to rank alternatives collected from different time intervals. Further, to prove the feasibility and potentiality of the proposed model, a case study of choosing a tourist destination in Vietnam is described under the TCNS context. A comparative analysis with existing algorithms demonstrates the proposed model’s effectiveness, consistency, and availability.

The nexus between research impact and sustainability assessment: From stakeholders’ perspective

Abstract A multi-criteria decision-making system based on stakeholder evaluation is performed to investigate the nexus between research impact and sustainability performance in the agro-food sector, in Spain. This study attempts to go a step further beyond the scientific assessment of research by examining its societal contribution. The empirical application is built upon the ELECTRE III methodology. Combining Evaluation theory and Stakeholder theory, the analysis facilitates the assessment of research impact with the inclusion of stakeholders’ knowledge. Four research programmes are selected from different agro-food industries representing the case studies addressed in this study. Each stakeholder performs an evaluation of the research programmes based on indicators and sub-indicators of sustainability performance. The findings reveal ranking matrices of research impact and demonstrate its implicit contribution to the Sustainable Development Goals. This study provides insights to policy-makers and practitioners and sheds light on how research evaluation accentuates the transition to sustainable agro-food sector.

A MinMax Item-based Method for Multi-Criteria Recommendation Systems

One of the common challenges in multi-criteria recommendation systems is dealing with data normalization. The challenge occurs since criteria can have diverse rating ranges and user rating behaviors are dissimilar. Previous studies on data normalization showed the supremacy of the Decoupling technique in the user-based multi-criteria recommendation system and the MinMax technique in the multi-criteria decision-making system as well as in data mining. A study also showed that the performance of Decoupling is improved in the item-based method than in the user-based. However, no study has been conducted to investigate the performance of MinMax compared to Decoupling in the item-based multi-criteria recommendation system. This study aims to combine the MinMax normalization technique and the item-based modeling approach in a multi-criteria recommendation system. The proposed method is named the MinMax Item-based method (MIB). We conducted a series of experiments using the Yelp Hotel multi-criteria rating dataset to perform a sensitivity analysis of MIB. The best settings are then used to benchmark MIB MIB towards DCMItem, i.e., a method that combines the Decoupling normalization technique and item-based multi-criteria modeling approach. The comparison results show the outperformance of MIB towards DCMItem by 2.30% in Precision and 2.00% in NDCG. Therefore, we can conclude that MixMax is able to improve the performance of the item-based multi-criteria recommendation system better than Decoupling.

Multi-Criteria Decision System for the Selection of A Freight Forwarder Using AHP

Supply chain management is critical for any organization. To improve the performance of a firm's supply chain, it is important to have a freight forwarder evaluation process in place. This paper develops an analytical hierarchy process (AHP) methodology to perform a multi-criteria evaluation of freight forwarders. It analyzes different criteria that would be employed in the evaluation and selection of forwarders. The problem of choosing the best freight forwarder is evaluated in terms of quality of service, specialized service, network, competitive price, information and technology, and sustainability. A total of fourteen sub-criteria are considered including quality of service, network, information and technology, and sustainability. The study revealed that specialized service is the most important criterion with a weight of 54.3% followed by quality of service, competitive price, information and technology, network, and sustainability. Proven experience is the most important sub-criterion with overall weight of 36.2% followed by knowing the regulations with overall weight of 18.1%. Freight forwarder B is the best option with the highest weight with respect to proven experience and knows the regulations with values of 3.62 and 1.629, respectively.