Multi-criteria Strategy Research Articles

The Local Wisdom Knowledge Applied in The management of Coastal Resources at Ilili Village, Western Seram District, Indonesia

The local knowledge system puts the cultural capacity, knowledge system, religious value, tradition, and social capital as important in managing natural resources. The local community of Ilili Village has used the local wisdom, locally called sasi and siritaun in coastal resources management. The coastal natural resources managed through this local wisdom are sea cucumber, top shell, green turban, and fish. The objective of this study was to identify factors that affect the status of local wisdom implemented in the effectiveness of fish resource sustainability. The study was conducted from April to June 2022, and a purposive sampling method was used for respondent selection. FGD and interviews were used to collect the data. The Rapid appraisal of fisheries (Rapfish) was used to assess the local wisdom status, and the DPSIR conceptual model framework was used to propose the strategy for sustainable management of sasi and siritaun (local wisdom). The Analytic Hierarchy Process (AHP) was used to propose a priority strategy from a multicriteria strategy objective. The study shows that the local wisdom was generally fair sustained with ecology sustained while institutional was less sustained. There were seven strategies proposed for sustainable management at the first level criteria and three additional strategies at the second level. The combined strategy proposed following its ranking were the implementation of local wisdom, the establishment of village regulation, and the increase in the local community income.

A Spatial Decision Support System for Prioritizing Repair Interventions on Power Networks

The business continuity of services provided by Critical Infrastructures is vital in order to ensure the security, the economy and the public’s health of a Nation. Delays and bad recovery strategies after disasters or failures can lead to impairing impacts in terms of injury to people, environmental pollution and loss of time, money and resources. In such a context, the adoption of a spatial Decision Support System (DSS) might play a crucial role in order to help operators to adopt the best recovery strategy in the shortest possible time frame. Current approaches do not consider the problem of assigning an intervention location to a maintenance crew and do not account for the effective time needed for emergency intervention. In this paper we develop a novel spatial multi-criteria DSS methodology for prioritizing repair interventions on power networks. The multi criteria strategy is solved by the adoption of Incomplete Analytic Hierarchy Process (AHP) which computes holistic assignment costs as the result of the combination of multiple and possibly conflicting metrics of cost.Then, we use the holistic costs as the basis for a task assignment phase that is based on the Hungarian algorithm. The proposed strategy has been implemented as a module in the Decision Support System, namely Critical Infrastructure Protection Risk Analysis and Forecast (CIPCast), whose outputs are represented on a web-based Geographic Information System (GIS) platform. The effectiveness of the proposed multi-criteria strategy has been validated via a real case study on the Rome City electrical distribution network.

A Multiobjective and Multicriteria Approach for Optimal Placement of Protective Devices and Switches in Distribution Networks

The optimal allocation of protective devices and switches in distribution systems concerns the analysis of conflicting attributes, such as capital investment, SAIDI, ENS, ECOST, MAIFI, among others. Regarding this multiattribute context, this paper proposes a multicriteria strategy to support decision-making. From this strategy, an assertive decision can be made under different scenarios when defining an appropriate solution to a real-world problem. A multiobjective optimization approach is applied to approximate an adequate set of potential solutions for allocating protective devices and switches. The capital investment and the SAIDI index are assumed as objective functions of the problem, and the well-known NSGA-II method is employed as the optimizer. Experiments are performed using the RBTS - Bus 4; the obtained trade-off solutions are evaluated in the previously stated attributes, and assisted decisions are made to illustrate the effect of considering different scenarios of preferences from the decision unit. The results highlight both the advantages of a multiobjective approach for allocating protective devices and the usefulness of a multicriteria strategy to aid assertive decision-making under different scenarios.

Comprehensive approach to the protection and controlled release of extremely oxygen sensitive probiotics using edible polysaccharide-based coatings

The human intestinal system is a complex of various anaerobes including extremely oxygen-sensitive (EOS) bacteria, some of which have been credited with significant health benefits. Among these, Faecalibacterium prausnitzii, which is one of the most abundant anaerobic bacterial strains in the human intestinal tract, has been proved to be a promising probiotic for the treatment of inflammatory bowel diseases. However, because of its extremely sensitive nature, there are many difficulties when passing through the harsh environment of the gastrointestinal tract. Hence, in this study, a comprehensive physicochemical characterization was performed for the use of polysaccharides from several origins (hydroxypropyl methyl cellulose, methyl cellulose, hydroxypropyl cellulose, chitosan, low-methoxylated pectin, kappa-carrageenan, sodium alginate and pullulan) as encapsulating agents to protect and deliver this bacterium. First, the apparent viscosity and surface tension of the polymer solutions were tested. Then, the mechanical properties, water vapor and oxygen barrier properties of these biopolymers as self-standing films were investigated. Lastly, in vitro release profiles of small molecules and bacterial cells from these biopolymer matrices in contact with a simulated gastrointestinal tract were evaluated. The results showed that chitosan, low-methoxylated pectin, kappa-carrageenan, sodium alginate and pullulan films exhibited good oxygen barrier properties to protect EOS probiotics. Among all the biopolymers tested, sodium alginate exhibited the best oxygen barrier properties and release profile. The release kinetics can be modulated by several factors including biopolymer type, plasticizer concentration and active molecules or bacteria to be encapsulated. On that basis and integrating the other parameters analyzed, a multicriteria strategy for probiotic encapsulation was proposed.

Method of fault feature selection and fusion based on poll mode and optimized weighted KPCA for bearings

The health monitoring system for equipment is essential in the smooth proceeding of industrial production. However, the fault features to be detected in monitoring systems are generally selected through projects and expertise, which are not capable for complex and ever-changing fault information and may result in incomplete correspondence to the fault types emerged. To dig deeper for the effective features hidden in the data instead of selecting by experience, a feature selection and fusion method based on poll mode and optimized Weighted Kernel Principal Component Analysis (WKPCA) method is then proposed. Specifically, inspired by poll-mode and multi-criteria strategy, a multi-measure hierarchical model is designed to sort the fault features with high sensitivity, acquiring the feature subset with corresponding weight coefficient. Considering the variation in fault information collected by different sensors, the diagnosis rate in Extreme Learning Machine (ELM) is taken as the index for evaluation of each single sensor, then the sensitivity weight matrix of features extracted by multiple sensors is constructed after linear normalization. To integrate the feature information, WKPCA is applied for the weighted fusion of features, and Quantum Genetic Algorithm (QGA) is used to search for the kernel width parameter when the best separability in the samples under the fusion is reached. Finally, such samples are introduced to drive the diagnostic model of the monitoring system in rolling bearing. The experimental results show that, compared with the traditional feature selection and fusion methods, this method is capable for sorting out highly sensitive features with more fault information self-adaptively, and can improve the separability in the subset of fault samples effectively.

Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach

Evaluating Industry 4.0 technology application in small and medium-sized enterprises (SMEs) is an issue that requires a multi-criteria strategy comprising quantitative and qualitative elements. The purpose of this study is to integrate performance estimation of Industry 4.0 technology application using the technology–organization–environment (TOE) framework. Relating TOE to Industry 4.0 technology application evaluation is more multifaceted than other methods and it requires comprehensive analysis. In this study, we applied a multiple-criteria decision-making (MCDM) approach to develop a model which integrates MCDM to perform an assessment that prioritizes the influence weights of Industry 4.0 technology application to SMEs’ factors. Firstly, we carried out a review of the literature and the TOE framework was selected to generate nine elements, along with three aspects used to measure Industry 4.0 technology application in SMEs. Secondly, the approach of the decision-making trial and evaluation laboratory (DEMATEL) was set up using an influence network relations digraph (INRD). The DEMATEL-based analytic network process (DANP) was used to indicate the influence weights linking the above aspects and elements. Lastly, the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique applied influence weights to assess the aspects/elements in the gaps identified and to investigate how to reduce the gaps so as to estimate the application of Industry 4.0 technology by SMEs. The results show that the technology aspect is the most influential factor.

Sustainable bio-succinic acid production: superstructure optimization, techno-economic, and lifecycle assessment

A multi-criteria strategy to identify sustainable bio-succinic acid production processes on a commercial scale.

A Novel Multi-Criteria Discounting Combination Approach for Multi-Sensor Fusion

Belief function theory manages uncertain information and offers useful combination rules for multi-sensor fusion. However, when sensor readings are in conflict or even unreliable, the quality of the fusion result is significantly affected. Recently, many discounting approaches have been proposed to combine unreliable sensor readings. The discounting factors involved in these methods are often determined based on a single criterion which is not sufficient in general to obtain a precise assessment of the reliability degrees of the sources to combine. In this work, that is why we propose a novel discounting combination approach, in which the reliability factors are obtained by using the multi-criteria strategy. Our discounting combination method includes two main steps. The first step to assess the sensor’s reliability is based on belief function-based technique for order preference by similarity to ideal solution (BF-TOPSIS). The second step is to discount and global combine all involved sensor readings according to their degree of reliability with proportional conflict redistribution-6 (PCR6) rule. Several simulations and comprehensive comparisons with classical approaches are given to show the efficiency of our proposed method.

A multi-criteria decision framework to support measurement-system design for bridge load testing

Due to conservative design models and safe construction practices, infrastructure usually has unknown amounts of reserve capacity that exceed code requirements. Quantification of this reserve capacity has the potential to lead to better asset-management decisions by avoiding unnecessary replacement and by lowering maintenance expenses. However, such quantification is challenging due to systematic uncertainties that are present in typical structural models. Field measurements, collected during load tests, combined with good structural-identification methodologies may improve the accuracy of model predictions. In most structural-identification tasks, engineers usually select and place sensors based on experience and high signal-to-noise estimations. Since the success of structural identification depends on the measurement system, research into measurement system design has been carried out over several decades. Despite the multi-criteria nature of the problem, most researchers have focused only on the information gained by the measurement system. This study presents a framework to evaluate and rank possible measurement-system designs based on a tiered multi-criteria strategy. Performance criteria for the design of measurement systems include monitoring costs, information gain, ability to detect outliers and impact of loss of information in case of sensor failure. Through including conflicting criteria, such as cost of monitoring and information gain, the optimal measuring system becomes a Pareto-like choice that ultimately depends on asset-manager preference hierarchies. Several potential preference scenarios are generated and results are compared using a full-scale test study, the Exeter Bascule Bridge. The framework successfully supports an informed design of measurement systems by providing an extensive set of alternatives, including the best solution defined probabilistically and for specific conditions when other near-optimal solutions might be preferred.

Multi-Criteria Strategy for Job Scheduling and Resource Load Balancing in Cloud Computing Environment

Cloud computing is growing rapidly over the years and it faces challenges especially in resource management. Resource management in cloud computing is necessary due to its distributed nature with different user demands. Quality of Service (QoS), load balancing and throughput are identified as some of the benefits of proper resource management. This research focuses on job scheduling and resource load balancing in cloud environment. We proposed an efficient algorithm based on multi-criteria strategy. The algorithm consists of two main phases. In the first phase the shortest job completion time is measured based on the completion time of three techniques i.e. min-min, max-min and suffrage. Meanwhile in the second phase genetic algorithm is implemented for resource load balancing. Cloud Sim simulator is used to measure the performance and efficiency of the proposed algorithm. The proposed algorithm enhances jobs scheduling and resource load balancing by ensuring an efficient utilization of the available resources.

Entropy theory based multi-criteria resampling of rain gauge networks for hydrological modelling – A case study of humid area in southern China

Rain gauge networks are used to provide estimates of area average, spatial variability and point rainfalls at catchment scale and provide the most important input for hydrological models. Therefore, it is desired to design the optimal rain gauge networks with a minimal number of rain gauges to provide reliable data with both areal mean values and spatial–temporal variability. Based on a dense rain gauge network of 185 rain gauges in Xiangjiang River Basin, southern China, this study used an entropy theory based multi-criteria method which simultaneously considers the information derived from rainfall series, minimize the bias of areal mean rainfall as well as minimize the information overlapped by different gauges to resample the rain gauge networks with different gauge densities. The optimal networks were examined using two hydrological models: The lumped Xinanjiang Model and the distributed SWAT Model. The results indicate that the performances of the lumped model using different optimal networks are stable while the performances of the distributed model keep on improving as the number of rain gauges increases. The results reveal that the entropy theory based multi-criteria strategy provides an optimal design of rain gauge network which is of vital importance in regional hydrological study and water resources management.

유전 알고리즘을 이용한 자동화 컨테이너 터미널에서의 장치장 크레인의 작업 할당 전략

In an automated container terminal, automated stacking cranes(ASCs) take charge of handling of containers in a block of the stacking yard. This paper proposes a multi-criteria strategy to solve the problem of job dispatching of twin ASCs which are identical to each another in size and specification. To consider terminal situation from different angles, the proposed method evaluates candidate jobs through various factors and it dispatches the best score job to a crane by doing a weighted sum of the evaluated values. In this paper, we derive the criteria for job dispatching strategy, and we propose a genetic algorithm to optimize weights for aggregating evaluated results. Experimental results are shown that it is suitable for real time terminal with lower computational cost and the strategy using various criteria improves the efficiency of the container terminal.

Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain

Despite the availability of weather radar data at high spatial (1 km2) and temporal (5–15 min) resolution, ground‐based rain gauges continue to be necessary for accurate estimation of storm rainfall input to catchments during flash flood events, especially in mountainous catchments. Given economical considerations, a long‐standing problem in catchment hydrology is to establish optimal placement of a small number of rain gauges to acquire data on both rainfall depth and spatiotemporal variability of intensity during extreme storm events. Using weather radar observations and a dense network of 40 tipping bucket rain gauges, this study examines whether it is possible to determine a reliable “best” set of rain gauge locations for the Sabino Canyon catchment near Tucson, Arizona, USA, given its complex topography and dominant storm track pattern. High‐quality rainfall data are used to evaluate all possible configurations of a “practical” network having from one to four rain gauges. A multicriteria design strategy is used to guide rain gauge placement, by simultaneously minimizing the residual percent bias and maximizing the coefficient of correlation between the estimated and true mean areal rainfall and minimizing the normalized spatial mean squared error between the estimated and true spatiotemporal rainfall distribution. The performance of the optimized rain gauge network was then compared against randomly designed network ensembles by evaluating the quality of streamflows predicted using the Kinematic Runoff and Erosion (KINEROS2) event‐based rainfall‐runoff model. Our results indicate that the multicriteria strategy provided a robust design by which a sparse but accurate network of rain gauges could be implemented for semiarid basins such as the one studied.

Que signifient les variations du dollar pour un grand acteur multinational ?

The Effects of the Dollar's Variations for a Multinational Company Having described the different channels (profits, balance sheets and market value) through which the multinational actors are affected by the variations of the dollar, the authors recall that the structural exposure of the insurance companies to the currency risk, even if limited by specific characteristics, is far from being negligible. Then, they detail the hedging strategy of the Axa group, whose net worth is for an important part in foreign currencies, mainly dollars, and whose half of the profits are realized in foreign currencies (25% dollars). Since 2001, the exposure of Axa to the currency risk is hedged by a voluntary and multicriteria strategy, closely monitored by governance mechanisms. Classification JEL : F23, F31, G12, G22

Optimisation of a selective method for the determination of organophosphorous triesters in outdoor particulate samples by pressurised liquid extraction and large-volume injection gas chromatography–positive chemical ionisation–tandem mass spectrometry

A selective analytical method for the determination of nine organophosphate triesters and triphenylphosphine oxide (TPPO) in outdoor particulate matter is presented. It involves a fully automated pressurised liquid extraction (PLE) step, integrating an alumina clean-up process, and subsequent determination by large-volume injection gas chromatography-positive chemical ionisation-tandem mass spectrometry (LVI-GC-PCI-MS/MS). The extraction variables (solvent, amount of adsorbent, temperature, time and number of cycles) were optimised using a multicriteria strategy which implements a desirability function that maximises both extraction and clean-up efficiencies while searching for the best-compromise PLE conditions. The final method affords quantification limits of between 0.01 and 0.3 microg g(-1) and recoveries of >80%, with the exceptions of the most polar analytes, TCEP and TPPO (~65%) for both urban dust and PM10 samples. Moreover, the method permitted the levels of these compounds in dust deposited outdoors (between LOD and 0.5 microg g(-1) for TEHP) and PM10 samples (between LOD and 2.4 microg m(-3) for TiBP) to be measured and reported for the first time.

Multi-criteria assessment of the Representative Elementary Watershed approach on the Donga catchment (Benin) using a downward approach of model complexity

Abstract. This study is part of the AMMA - African Multidisciplinary Monsoon Analysis- project and aims at a better understanding and modelling of the Donga catchment (580 km2, Benin) behaviour in order to determine its spatially distributed water balance. For this purpose, we applied the REW concept proposed by Reggiani et al. (1998, 1999), which allows the description of the main local processes at the sub-watershed scale. Such distributed hydrological models, which represent hydrological processes at various scales, should be evaluated not only on the discharge at the outlet but also on each of the represented processes and in several points of the catchment. This multi-criteria approach is required in order to assess the global behaviour of hydrological models. We applied such multi-criteria strategy to the Donga catchment (586 km2), in Benin. The work was supported by an observation set up, undertaken since 1998 consisting in a network of 20 rain gauges, an automatic meteorological station, 6 discharge stations and 18 wells. The main goal of this study was to assess the model's ability to reproduce the discharge at the outlet, the water table dynamics in several points of the catchment and the vadose zone dynamics at the sub-catchment scale. We tested two spatial discretisations of increasing resolution. To test the internal structure of the model, we looked at its ability to represent also the discharge at intermediate stations. After adjustment of soil parameters, the model is shown to accurately represent discharge down to a drainage area of 100 km2, whereas poorer simulation is achieved on smaller catchments. We introduced the spatial variability of rainfall by distributing the daily rainfall over the REW and obtained a very low sensitivity of the model response to this variability. Simulation of groundwater levels was poor and our results, in conjunction with new data available at the local scale, suggest that the representation of the processes in the unsaturated zone should first be improved, in order to better simulate soil water dynamics and represent perched water tables which were not included in this first modelling study.

SU‐FF‐T‐355: Performance Evaluations of a Preemptive Approach in IMRT Planning

Purpose: Preemptive Goal Programming (PGP) facilitates the use of soft‐constraints in a hierarchical manner for solving complex inverse planning problems. It is a highly effective multicriteria strategy for IMRT planning. However, in order to ensure good numerical performance, a robust constrained optimization algorithm with a practical strategy for handling degeneracy is required. This study identifies cases when PGP introduces challenging problems that do not hold a conventional constraint qualification. Numerical performance for such problems is evaluated using nonlinear planning metrics (DVH, EUD, and NTCP). Method and Materials: PGP, as implemented in our in‐house planning system, solves a series of nonlinear least‐squares problems by a sequential quadratic programming (SQP) algorithm provided with precise Jacobians by an automatic differentiation algorithm. In the presence of constraint degeneracy, an elastic mode (feasibility bound relaxation) is used to ensure a positive step taken at all iterations. Dosimetric as well as numerical performance is evaluated for dose (or EUD) escalation cases with critical organ dose constraints. Reformulation of constraints is also studied as a potential way of reducing degeneracy in these problems. Results: Optimization levels involving strongly active constraints showed degeneracy near optimality, thus requiring use of the elastic mode for convergence. This mode exhibited robust convergence (typically <∼100 iterations) for all IMRT cases studied. Highly nonlinear models (NTCP or EUD with high +/−powers) were handled well by SQP. A strategy of constraint reformulation showed a monotonic decrease in merit function value and its dosimetric performances were comparable to those without reformulation. Conclusion: The optimization task of escalating target dose is limited by binding dose constraints of OARs, which can also create degeneracy in PGP. This study shows SQP with an elastic mode exhibits a stable convergence using the dose‐volume, EUD and NTCP metrics. Numerical performances can be further stabilized by reformulating degenerate constraints.

Sparse data in the evolutionary generation of fuzzy models

Fuzzy rule bases have proven to be an effective tool for modeling complex systems and approximating functions. Two approaches, global and local rule generation, have been identified for the evolutionary generation of fuzzy models. In the global approach, the standard method of employing evolutionary techniques in fuzzy rule base generation, the fitness evaluation of a rule base aggregates the performance of the model over the entire space into a single value. A local fitness assessment utilizes the limited scope of a fuzzy rule to evaluate the performance in regions of the input space. Regardless of the method employed, the ability to construct models is inhibited when training data are sparse. In this research, a multi-criteria fitness function is introduced to incorporate a bias towards smoothness into the evolutionary selection process. Several multi-criteria fitness functions, which differ in the extent of the assessment smoothness and the range of its application, are examined. A set of experiments has been performed to demonstrate the effectiveness of the multi-criteria strategies for the evolutionary generation of fuzzy models with sparse data.

Multicriteria optimization approach for hydrostatic bearing design

In this paper, computer‐aided design optimization approach for hydrostatic bearings is presented. The optimization problem is formulated in multicriteria strategy. The power loss and the temperature rise of oil are considered as performance measures for optimal hydrostatic bearing design. Optimization results indicate the effectiveness of the proposed combined approach which has advantages over the single criteria optimization. Sample results are presented to verify the proposed approach.