Multi-criteria Decision-making Model Research Articles

Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation

Assessment of Land Suitability Potential Using Ensemble Approaches of Advanced Multi-Criteria Decision Models and Machine Learning for Wheat Cultivation

Innovative strategic multicriteria decision-making selection model of infrastructures projects delivery systems using multiobjective optimization, case of Egypt

Purpose To help decision-makers choose appropriate infrastructure project delivery systems (IPDS) and keep up with the construction industry’s rapid growth, this study aims to develop a goal optimization technique.This looks into team integration, large production and optimum sustainability. The suggested approach for meeting several infrastructure project objectives is flexible and expandable. This research overcomes the significant discrepancy between the construction industry’s progress and the rate at which project delivery methods evolve. Design/methodology/approach This study examined pertinent literature to choose an appropriate project delivery method and gave information on several elements that affect that decision. After optimization using a genetic algorithm (GA), a Pareto front of solutions has been found. The three construction goals of sustainability, mass production and team integration are all met by the chosen best solution. The four most popular possibilities for studying the suggested approach are five primary categories, each of which has 22 variables, and the weight of each variable was established using Simo’s procedure. This is optimized, demonstrating the accuracy of the optimization model. Findings Sustainability, mass production and team integration are the major goals of selecting the finest IPDS. The Pareto-optimal solutions discovered through analysis demonstrated that the created GA is reliable and generates solid outcomes. In fact, it enables decisions that were based on a single criterion to be overturned. The process has therefore demonstrated its efficacy in identifying the ideal answer. First integrated project delivery (IPD), second design-build (DB), third design-bid-build (DBB) and last construction manager at risk (CMR) are the best options. The weight of the aims function has found these rankings to be satisfactory. Practical implications The findings demonstrate that the suggested strategy can lead to optimization, providing the government with a wide range of options for attaining certain project objectives. The ability of this study to evaluate the combined effects of three objectives in choosing the best IPDS, the production of optimal selection solutions (IPDS), which can help with better decision-making when many objectives are present, and the flexibility and extendibility of the suggested approach for achieving priorities in infrastructure projects are what make it unique. This approach was able to select IPDS to meet developments in the construction project. Originality/value To confirm the validity of the GA, the factor of error was calculated, which is equal to 1.7599e-08.

Flood susceptibility assessment of Jhelum River Basin: A comparative study of TOPSIS, VIKOR and EDAS methods

Floods are frequent natural hazards that cause widespread destruction, particularly in low-elevated areas. This study focuses on identifying flood susceptible zones in the Kashmir Valley, known for historical flooding attributed to the overflow of the Jhelum River. Various Multi-Criteria Decision Making (MCDM) techniques, including Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijai Compromission Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS), were employed in this research. A total of 17 multidimensional factors were considered, and multicollinearity tests revealed no correlation among these factors. The results of the MCDM models indicate that areas along the Jhelum River are classified under very high flood susceptible zone. Specifically, Srinagar city is consistently classified under very high flood susceptible zone by all three models. Approximately 4.27 %, 9.67 %, and 5.39 % of the total area were identified as very high susceptible areas by TOPSIS, VIKOR, and EDAS, respectively. The models exhibited robust performance, as evidenced by the Area Under the Curve of the Receiver Operating Characteristics curve (AUC-ROC). Notably, VIKOR demonstrated excellent performance among the three models in generating flood susceptible maps. The favorable outcomes of these models underscore their potential application in similar regions facing comparable challenges. This study carries significant implications for policymakers, administrators, and local authorities involved in flood management within the Kashmir Valley. The insights provided can inform proactive measures and strategies to mitigate the impact of floods and enhance the overall resilience of the region.

Geospatial based groundwater resources susceptibility and contamination risk mapping for Yeşilköy aquifer, Cyprus

In rural areas where the goals of sustainable agriculture are intertwined with the availability and supply of groundwater resources, it becomes imperative to perceive and search the status of groundwater resource’s susceptibility to contamination. A well-known overlay & index method, the DRASTIC model that assesses the state of susceptibility to contamination using vulnerability index map has been applied to the Yeşilköy aquifer. The seven different hydro-geologic parameters; aquifer media, vadose zone, and soil media, surface topography, recharge capacity, water table depth, and the hydraulic conductivity maps were overlaid to find the vulnerability of the aquifer. Results depicted that the aquifer is categorized into two vulnerability zones; low and moderate. The available susceptibility of the aquifer due to ongoing anthropogenic activities like agriculture was also studied by implementing the Weighted Arithmetic Water Quality Index (WQI) method. The aquifer’s overall water quality was “poor water quality”. Hence, there was no significant relationship between WQI and the vulnerability of the aquifer. A modification of the DRASTIC to enhance the relationship with WQI is proposed by utilizing the Analytic Hierarchy Process (AHP), as one of the successful multi-criteria decision-making models. The modification of the DRASTIC model with AHP revealed that the performance of the DRASTIC model was improved and the correlation between WQI and the DRASTIC vulnerability index was significantly enhanced. The results also show that the effective contributing parameters for groundwater vulnerability in the Yeşilköy aquifer were the aquifer media and the vadose zone. On the other hand, the water table’s depth and topography were almost independent of the aquifer’s vulnerability.

Optimization of urban transport vehicle tasks for large, mixed fleet of vehicles and real-world constraints

Planning the operation of urban public transport vehicles is the first stage of operational planning and consists in combining timetable tips, which are input data, into blocks that constitute the daily tasks of vehicles. For a large mixed fleet of vehicles of various types, especially those with battery power that requires recharging, operating from many depots, with numerous requirements and rolling stock constraints, the problem is a major engineering challenge, even for an experienced team of planners. IT solutions based on realistic, mathematical decision-making models and fast optimization algorithms can be of great assistance. For the problem formulated this way, a mathematical decision model with a multi-criteria objective function was built, taking into account technical, economic, and ecological criteria, natural, and binary decision variables. The model takes into account the real requirements and constraints, a mixed fleet of different types vehicles, including electric buses, multiple depots, technical trips (dead heads), and battery charging. The considered problem is an NP-hard combinatorial optimization problem. The use of classical, exact algorithms to solve this problem is not possible for schedules with many thousands of line trips and fleets of hundreds or thousands of vehicles. This research proposes an original, dedicated heuristic, enabling to obtain an acceptable, but still suboptimal solution, in a very short time. The tests of the proposed heuristic algorithm were carried out on real databases of public transport systems of the two selected medium and large Polish cities. In particular, multiple depots, a mixed fleet of different types of vehicles, and real-world constraints were taken into account. The results of computer experiments carried out using the developed heuristic were compared with the results obtained manually by a team of experienced and expert planners. For the developed multi-criteria decision-making model results comparable to and better than those prepared manually by experts were obtained in a very short time using the proposed heuristics. It is the basis for further development works on expanding the model and improving the optimization algorithm.

Predictive evaluation of city sustainability based on benchmarking method - A case study of 34 cities in northeastern China

Sustainability is a recurring theme in city development, aiming to foster an environmentally friendly, livable, and prosperous future. Sustainable development, as a long-term vision for cities, requires an interdisciplinary method and a systems-orientated perspective on evaluating and solving city problems. The multi-criteria decision making (MCDM) method is highly applicable given the excellent multidimensional analytical and model building capabilities. As a widely used decision-making tool, the classical paradigm of MCDM is to make summary measurements of the system based on historical data to facilitate the best conclusions. Nevertheless, from the perspective of long-term sustainability evaluation, the currently available MCDM models are gradually failing to meet the predicting needs highlighted by policy makers in urban planning. Therefore, this paper proposes an innovative predictive evaluation method of city sustainability by integrating benchmarking and stochastic simulation under the framework of MCDM, which enables cities to develop visionary scenarios to accelerate the achievement of sustainability goals. This method was used to predict the future sustainability of 34 cities in northeastern China, which is a good demonstration of its utility and validity in sustainable cities and related fields.

Research on Enhancing the Yield and Quality of Oat Forage: Optimization of Nitrogen and Organic Fertilizer Management Strategies

In the context of the increasingly serious issues of resource waste, soil degradation, and environmental pollution caused by excessive nitrogen fertilizer application worldwide, this study conducted a two-year field experiment in Qinghai Province to explore suitable nitrogen fertilizer management strategies for the region. Ten fertilization levels were set, incorporating varying ratios of conventional nitrogen fertilizer and organic fertilizer, as well as the proportion of base fertilizer and topdressing. The focus was on monitoring the forage yield, quality, and related physiological indicators of oats during the flowering and milk stages. The use of correlation analysis and the multi-criteria decision-making model TOPSIS was applied for comprehensive data evaluation to determine the optimal fertilization strategy. After systematic data collection and analysis, the results showed that when 75% conventional nitrogen fertilizer was combined with 4500 kg·hm−2 of organic fertilizer (F4), the oat yield during the milking stage reached its peak at 14,722.48 kg·hm−2. Additionally, the yield effect was optimal (13,677.34 kg·hm−2) when using 30% base fertilizer and 70% jointing fertilizer (D2). Regarding nutritional quality, the fertilization strategy combining 75% conventional nitrogen fertilizer with 4500 kg·hm−2 of organic fertilizer, along with 30% base fertilizer and 70% jointing fertilizer (F4D2), significantly reduced the content of acid detergent fiber (ADF), neutral detergent fiber (NDF), and coarse fiber (CF) in oats, while increasing the content of EE (crude fat) and CP (crude protein). This significantly improved the nutritional value of oats. Correlation analysis further revealed the positive effect of fertilization amount and fertilization period on oat yield, as well as a negative correlation with fiber content. Finally, through comprehensive evaluation using the multi-criteria decision-making model TOPSIS, we verified the superiority of the fertilization strategy.

Last Word in Last-Mile Logistics: A Novel Hybrid Multi-Criteria Decision-Making Model for Ranking Industry 4.0 Technologies

The complexity, increasing flow number and volumes, and challenges of last-mile logistics (LML) motivate or compel companies, authorities, and the entire community to think about ways to increase efficiency, reliability, and profits, reduce costs, reduce negative environmental impacts, etc. These objectives can be met by applying Industry 4.0 (I4.0) technologies, but the key question is which one. To solve this task, this paper used an innovative method that combines the fuzzy analytic network process (fuzzy ANP) and the fuzzy axial-distance-based aggregated measurement (fuzzy ADAM) method. The first was used for determining criteria weights and the second for selecting the best variant. The best solution is e/m-marketplaces, followed by cloud-computing-supported management and control systems and blockchain. These results indicate that widely adopted and implemented technologies are suitable for last-mile logistics. Newer technologies already producing significant results have serious potential for further development in this area. The main novelties and contributions of this paper are the definition of a new methodology based on multi-criteria decision-making (MCDM) methods, as well as its application for ranking I4.0 technologies for LML.

"Thin" Structure of Relations in MCDM Models. Equivalence of the MABAC, TOPSIS(L1) and RS Methods to the Weighted Sum Method

This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings.

A hybrid multi-criteria decision-making model for waste facilities location considering system resilience

The location of waste treatment facilities is a crucial strategic decision in urban management. Most existing studies focus on mathematical modeling and algorithm solving for multi-objective models aimed at minimizing cost or pollution. They lack consideration for the resilience of decision solutions in the face of uncertain and unpredictable disruptive incidents. This paper aims to bridge the research gap by proposing a top-down hybrid multi-criteria decision-making model that incorporates cost, pollution, social discontent, and system resilience simultaneously. The proposed model integrates various methods such as integer programming modeling, simulation modeling, and catastrophe analysis. It addresses the challenge that disruptive incidents cannot be precisely expressed. Additionally, it quantitatively analyzes the factors that impact system resilience. Finally, the proposed model is validated by applying it to a waste management network based on the real case of Shenzhen city. The results demonstrate notable differences in the resilience indexes among the solutions in the Pareto-optimal set, which confirms the significance of system resilience from another aspect. Further catastrophe analysis reveals several valuable insights for urban waste management. This paper offers a new perspective to cope with various risks related to waste management and provides a method reference and decision support for the construction of urban resilience.

GIS-based multi-criteria decision models for barite exploration in Nigeria’s Benue Trough

Spatial predictive mapping using geographic information system (GIS) is considered an invaluable tool for reconnaissance-scale exploration of mineral resources. In this study, geospatial data on geophysics, remote sensing, and structural and lithological attributes were systematically integrated to prospect barite potential zones within the Mid-Nigerian Benue Trough (MBT). Correlation attribute evaluation was used to establish the relationship between mineral deposit occurrences and geospatial data, while data integration was implemented using the Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Additive Ratio Assessment (ARAS) multi-criteria models. Here we show that the correlation attribute evaluation suggests that barite occurrences displayed a strong correlation with spatial data on lineament density, ferric iron alteration, and potassium to thorium (K/Th) ratio, whereas a weak correlation was observed with spatial data on the first vertical derivative (FVD), proximity to the host rock, and ferrous iron alteration. Here we report that the quantitative estimation of predictive models indicated that very high predictive zones for barite occurrences accounted for 19% of all the models. The accuracy assessment using Receiver Operating Characteristic (ROC)/Area Under the Curve (AUC) showed prediction levels above 78% for all models. The effectiveness of the spatial application of multi-criteria decision models makes them a reliable tool for barite exploration within the Mid-Nigerian Benue Trough (MBT) and other geologically similar environments.

Grey multi-criteria group consensus decision-making based on cobweb model

PurposeConsidering the bounded confidence of decision-makers (DMs), a new grey multi-criteria group consensus decision-making (GMCGCDM) model is established by using interval grey number (IGN), cobweb model, social network analysis (SNA) and consensus reaching process (CPR).Design/methodology/approachFirstly, the model analyzes the social relationship of DM under social networks and proposes a calculation method for DMs’ weights based on SNA. Secondly, the model defines a cobweb model to consider the preferences of decision-making alternatives in the decision-making process. The consensus degree is calculated by the area surrounded by the connections between each index value of DMs and the group. Then, the model coordinates the different opinions of various DMs to reduce the degree of bias of each DM and designs a consensus feedback mechanism based on bounded confidence to guide DMs to reach consensus.FindingsThe advantage of the proposed method is to highlight the practical application, taking the selection of low-carbon suppliers in the context of dual carbon as an example. Comparison analysis is performed to reveal the interpretability and applicability of the method.Originality/valueThe main contribution of this paper is to propose a new GMCGCDM model, which can not only expand the calculation method of DM’s weight and consensus degree but also reduce the time and cost of decision-making.

Evaluation factors of adopting smart home IoT: The hybrid fuzzy MCDM approach for robot vacuum

With the vigorous development of information technology, the applications of the Internet of Things (IoT) have become increasingly common in recent years. Robot vacuum has become a popular and representative product in smart homes. This study proposed a hybrid fuzzy multi-criteria decision-making (MCDM) model that applied fuzzy analytic network process (FANP) and decision-making trial and evaluation laboratory (DEMATEL) to analyze the critical factors evaluated by users when adopting a robot vacuum. It was found that the top two dimensions in order are “epistemic value” and “functional value”; and the top five factors in order are “novelty”, “exploratory”, “family information infrastructure”, “family consensus”, and “reliability”. Significant influential and affected factors were identified. Gender differences in decision-making factors are also discussed.

Understanding the Interrelationships Between Organizational Performance Measurement System Implementation Variables

Performance measurement (PM) systems are increasingly being adopted to manage and improve organizations’ effectiveness. However, a review of the research shows that there are a significant number of unsuccessful PM systems with indications of challenges arising during the implementation phase. A study involving subject-area experts was conducted and the analytic network process (ANP), for evaluating the multi-criteria decision model, was used to quantify the effects of the dynamic interrelationships among the model variables as well as the systemic effect of the factors on PM system implementation success (IS). The results show that Leadership Support and the Implementation Approach are the most important factors for successful implementation of PM systems. Meanwhile, factors related to training, participation, and an effective information technology system help users of PM understand the measures and identify their perceived benefits, thereby contributing to performance goals and objectives. This study provides a comprehensive and rigorous analysis by employing an expert study to evaluate and quantify the effects of model variables and their interrelationships on PM system implementation success using the ANP methodology. Managers and researchers can use the prioritization of the factors and insights regarding key interrelationships to develop more specific strategies and better manage resources of the implementation effort.

Achieving Sustainable Goals Using an Effective Budget-Allocation Multicriteria Mives Model: Case Study of a Spanish Water Utility Company

Within the global sustainability agenda, companies must align their operations with specific Sustainable Goals (SGs) and develop corresponding budgeting strategies. This paper presents a robust multicriteria decision-making model tailored for a Spanish utility company specializing in water services. The model ensures that financial allocations reflect the company's vision and its interpretation of sustainable development within its service areas. This approach aims to promote objectivity, traceability, and sustainability in decision-making, as a critical component in achieving SGs. The article emphasizes the importance of aligning water companies' budget allocations with SGs, highlighting key indicators for consideration. It provides a practical illustration of how the company's financial allocations were synchronized with SGs using a multicriteria decision-making model. A case study is included, involving a budget allocation of 40 million euros compared across four scenarios with the historical average of budgets from the Past Three Investment Plans (P3IP). The results reveal significant variations in budget allocation across different infrastructure categories, demonstrating the system's robustness, such as a 70% variation in production infrastructures, 27% in transportation, 25% in distribution, and 40% in the remaining areas, depending on the scenario.

A new integrated rough multi-criteria decision-making model for enterprise resource planning software selection.

Enterprise resource planning (ERP) is widely used to boost the total market power of businesses. The wrong selection is one of the key reasons why ERP installations fail. Due to the complexity of the business environment and the range of ERP systems, choosing an ERP system is a complex and time-consuming procedure. ERP alternatives may be assessed using several criteria, so the ERP selection process may be considered a multi-criteria decision-making (MCDM) problem. In this study, the rough best worst method (BWM) was used to determine criteria weights, while the newly developed rough integrated simple weighted sum product (WISP) was used to rank ERP alternatives. Results suggest that the SFT-4 coded software is regarded as the best option, followed by SFT-5, SFT-6, SFT-2, SFT-3, and SFT-1. Results of the newly developed rough WISP method are compared to those of existing rough techniques in the sensitivity analysis. The differences between them have been found to be negligible. The outcomes show how effectively developed rough BWM and WISP integrated method performs in terms of ERP selection with usability, accuracy, ease of use, and consistency. This study will help decision-makers in a context where ERP is implemented choose the best ERP software for different sectors.

Selection of Landing Site for Infantry River Crossing Using Aluminum Boat M70: Application of DIBR and Topsis Method

Abstract This research analyzes the problem of choosing a location for conducting infantry river crossing using the aluminum boat M70, applying the multicriteria decision-making method. It has been found that the use of these models significantly accelerates and simplifies the decision-making process, contributing to efficiency and speed in the process itself. At the same time, a comparison was made between the results obtained using multicriteria decision-making methods and the opinions of experts in the field of overcoming water obstacles. The research conclusions emphasize that multicriteria decision-making methods are highly reliable for decision-making, especially in the context of military operations. The focus of the research is on the significance of applying multicriteria methods, which opens up new perspectives for analyzing and improving the application of such models in real conditions. The applied multicriteria decision-making model consists of two phases. The first phase involves defining the weighting coefficients of criteria using the DIBR (Defining Interrelationships Between Ranked criteria) method. In the second phase, the best alternative is chosen using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. This research not only supports efficient and comprehensive planning in military operations but also further confirms the relevance and usability of multicriteria decision-making methods in wartime conditions.

Evaluating future strategies for sustainable growth of fiberglass composites industry in developing countries: A novel hybrid SWOT-Fuzzy extended PIPRECIA approach

The global fiberglass-composite market is expanding tremendously due to its extensive applications in the construction and automotive sector. The progress in low-medium income developing countries is slow. This study explores an exclusive hybrid model of SWOT (strengths, weaknesses, opportunities, and threats) analysis and Fuzzy extended PIPRECIA (pivot pairwise relative criteria importance assessment) to evaluate the strategies for sustainable development of fiberglass composites industry in Pakistan as a representative of low-medium developing countries. SWOT analysis is employed for examining the factors and sub-factors which have been extracted from a real-time industrial survey. While internal and external factors are also critically established to formulate a TOWS matrix comprising nine proposed strategies. Later the preferences as proposed by experts are evaluated by Fuzzy extended PIPRECIA i.e., a MCDM (multi-criteria decision making) model. Finally, SWOT factors, sub-factors and strategic choices are orderly ranked and presented. The results of the study reveal that development of a suitable environment to attract investors for the advancement and growth of the local fiber composites manufacturing industry (WO2 i.e., 0.175) is a most desirable and highly prioritized strategic choice. While maximizing environmental research to reduce environmental impact and better management of resources (WT2 i.e., 0.076) is the least favorable. The application of this exclusively developed MCDM model will provide an insight to the policy makers and assistive in strategic management and sustainable development of composite industry in developing countries. While this model can also be effective for other complex planning and decision-making processes.

An investment evaluation and incentive allocation model for public-private partnerships in renewable energy development projects

This study proposes an investment evaluation and incentive allocation model for public-private partnerships (PPPs) in renewable energy development projects (REDPs). A hybrid multicriteria decision-making (MCDM) and bi-level optimization model are proposed to evaluate investment opportunities and allocate government financial incentives (GFIs) to REDPs. The best-worst method (BWM) is used to weigh the evaluation criteria. VIKOR and grey relational analysis (GRA) rank and allocate GFIs to the private sector companies selected to participate in the REDPs. The government uses the PPP to persuade digital services companies in the private sector to invest in underdeveloped REDPs using financial incentives with minimal risk and maximum return. An iterative full-enumeration-based heuristic model is developed to handle the computational intractability in the bi-level model. The computational results show that political and financial support and land use are the most and the least important criteria, respectively. Moreover, we show that the government prefers to allocate a significant portion of its GFIs to waste heat recovery and hydropower in partnership with digital services companies. The results from the bi-level model help government agencies and policymakers offer equitable incentive programs in the energy sectors.

A methodology for multiple performances optimization of computer numerical controlled (CNC) machining process based on design of experiment, multi-criteria decision-making and multiple regression model

A methodology for multiple performances optimization of computer numerical controlled (CNC) machining process based on design of experiment, multi-criteria decision-making and multiple regression model