Decision Support System In Order Research Articles

Sistem Pendukung Keputusan Pemilihan Staf Karyawan Bpn Terbaik Di Wilayah Kota Medan Dengan Mengunakan Metode Moora Dengan Pembobotan Roc

Decision Support System (DSS) is a decision support system that can help in handling or solving problems in a company or in the daily environment, in determining a problem we need data that we will take in a company or in the environment. In writing this is an example of a problem that is currently happening with the case study of the Selection of the Best BPN Employees in the City of Medan, therefore the author uses a method that will be able to find the best employee data, therefore the author creates a decision support system that will be able to determine the best BPN employee assessment process in the city of Medan. Where this system is a system that has a very important role in decision making. The system is DSS, where DSS is one of the systems that can assist in solving each problem by requiring data and methods contained in the DSS in order to obtain more relevant information and support in making each decision. In completing this research, the writer has to find the weight value of the criterion data where the writer uses the ROC method to produce the weight value and after that the writer also searches for the ranking value using the MOORA method. And where to produce ranking values in the MOORA method, the author must work according to predetermined steps in order to obtain precise and accurate reference values. The ROC and MOORA methods really help writers in generating values accurately.This decision support system was made using the MOORA method with ROC weighting where this method can be used or can be used as a reference in determining a problem because it aims to find out how many employees are Top Talent (Best Employee), after testing it is given a weight value on criteria and can be modified which aims to find out how many criteria will be added in each criterion which has its own weight value and after that a ranking is made in which the ranking can already determine the best BPN employee in the city of Medan, namely (T3) Anggi Alfarissi HSB with a score of 1,157. So because of this it was confirmed or very easy for the team to select in decision-making and were selected as the best BPN employees in the city of Medan.

Seleksi Penerimaan Bantuan Internet Gratis dengan Menggunakan Metode AHP

The internet is an information technology that is able to provide benefits for life and is a major requirement as a means of communication, entertainment or business. Covid 19 spreads throughout the City or Regency to villages in all regions in Indonesia which has a broad impact on the world of Education, especially Elementary and Middle School Education. Seeing the change in learning patterns from face-to-face to online learning, there are obstacles that are felt by students, especially in learning that requires the teaching and learning process between teachers and students to be carried out remotely (online). To support online learning, adequate internet facilities are needed. Provision of internet packages can be provided free of charge with due observance of predetermined requirements. To support the provision of free internet packages, a decision support system is needed to determine the selection of receiving free internet assistance so that it is right on target by using the Analytical Hierarchy Process (AHP) solving procedure. This method is used to make rankings for selecting free internet recipients, where the highest score is generated based on the best criteria. The results of calculations with this method. Where to make a decision support system in order to prevent errors in determining the criteria that deserve free internet assistance.

Evaluation of Habitat Suitability Index Model of Indian Gharial in National Chambal Sanctuary, M.P. India

Almost all animals use their habitats in a non-random manner. But the cost and benefits of using specific habitat types remain unknown for many types of organisms. Habitat suitability models aim to assess the relationship between species and their habitat and accurately predict the species response to habitat changes. We developed a riverine habitat model for Gharial (Gavialis gangeticus), a critically endangered species, in the National Chambal Sanctuary in India after reviewing and synthesizing existing information. Based on the model, an index of habitat suitability is produced, ranging from 0 (unsuitable habitat) to 1 (optimal habitat). The results of the present study indicate that the Gharial prefer deep pools in water bodies. The deep waters serve as feeding and breeding grounds for Gharial. They prefer water with a velocity ranging from 20–70 cm.sec-1 . Sandy banks are preferred as basking sites, mainly because they provide moisture during the warm months and because it seems that they find it easier to crawl on sandy surfaces than on rocky or clay surfaces. Insects, tadpoles, tiny fish, and frogs are preferred by young Gharial. A habitat suitability model for Gharial can be used as a decision support system in order to improve conservation efforts at different landscape scales.

Prediction of litter performance in lactating sows using machine learning, for precision livestock farming

• The best predictions were obtained with farm-level training and ensemble methods. • Reliable predictions were also obtained with simple machine learning algorithms. • Effects of farm, litter size, and parity on litter performance were confirmed. • The presented approach enables the estimation of milk nutrient output in sows. Predicting litter performance in lactating sows is an essential step towards the development of decision support systems for precision feeding in lactating sows. Numerous factors affecting litter performance have been described in literature. However, predictive models working on-farm in real time are not available. The main objectives of this research was to (i) explore 4 different machine learning strategies, and (ii) identify the best supervised learning algorithm in order to obtain reliable predictions of litter performance. This study was carried out with data obtained from 6 experimental farms over the last 20 years. Algorithms were trained to predict the litter weight at weaning using a set of 4 numeric and 3 categorical features, and a method for predicting secondary litter performance and nutrient output in milk from the predicted litter weight at weaning was evaluated. To evaluate the reliability of predictions within each farm, the mean error per farm (ME f ) and the mean absolute percentage error per farm (MAPE f ) were computed. The best performance for the prediction of litter weight at weaning was obtained with an ensemble algorithm with farm-level training and testing (ME f = −0.14 kg; MAPE f = 9.01%), but performance with simple linear regression was very close (MAPE f = 9.30%). Learning across all farms only achieved comparable results with the neural networks algorithm, but at higher computational costs. The method for predicting secondary litter performance and nutrient output from the predictions of litter weight at weaning reveals that the ME f remains close to 0, and that the MAPE f only increases by a few percentage points. This study confirms the effect of numerous factors known in the literature to affect litter performance, such as litter size and parity of sows, but also revealed huge variations between farms. According to this study, reliable predictions could be obtained with interpretable supervised algorithms trained at farm level, with features that can be easily measured on-farm. This study thus shows that on-farm data are necessary to accurately train models and make reliable predictions at farm level. These predictions could be used by decision support systems in order to develop precision feeding approaches in lactating sows.

Methodology for improving the decision support system in order to reduce the probability of environmental accidents at gas production industry

The aim of this work is to establish the key correlations between the technological, economic and environmental characteristics of gas production industry. To solve this problem, methods of mathematical modeling, fuzzy logic, and management theory were used. A generalized criterion was constructed that serves to assess the cost characteristics of measures for the implementation of management decisions developed by the decision support system and aimed at reducing the probability of environmental accidents.

Improving the ship's power plant automatic control system by using a model-oriented decision support system in order to reduce accident rate under the transitional and dynamic modes of operation

This paper proposes a method to improve the performance of a ship's power plant by reducing accidents within it under transitional operating modes. The method is based on decreasing the number of service personnel errors by using a model-oriented decision support system. In order to implement the proposed method, the structure of the system of automatic control of the ship's power plant has been improved. Such an improvement of the control system implied the integration of a modeling unit and a decision support unit into its structure. The modeling unit makes it possible to predict values of the controlled parameters under a transition mode of operation before they actually appear in the system as a result of the operator's actions. A mathematical model of the automatic control system under transitional operating modes has been built for this unit. In order to implement the decision support unit, a method has been devised to formalize the task of managing the power plant under transitional operating modes. The method essentially involves modeling a transitional operating regime, followed by an evaluation of the results based on regulatory requirements and an empirical criterion for assessing the quality of enabling the diesel generators to work in parallel. In addition, a method has been developed for the decision support unit to reduce the accident rate and improve performance with the help of a mathematical apparatus of fuzzy inference, fuzzy logic, and fuzzy sets. Transitional operating regimes resulting from actual erroneous operator actions during ship flights were investigated. As a result of using the proposed system, the power plant performance increases

Alterations in coordinated EEG activity precede the development of seizures in comatose children

ObjectiveWe aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children. MethodsIn a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring. ResultsChildren who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results. ConclusionsThese computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures. SignificanceIf confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.

Framework design of decision support system: Improving decision making in fishery supply chain for coastal communities

The focus of researchers in the field of decision making which related to designing a decision support system to assist in the decision-making process, so far is very limited to the fisheries sector, especially at the small and micro business scale. On the other hand, the SME-scale fisheries sector is a major driver of regional and country performance because it has a very significant multiplier effect on the economic development of the region. Specifically, for coastal areas, the main problem faced is the consistency of the results obtained, one of the main causes is a distorted supply chain. In this study, we are trying to design a conceptual framework for decision support systems in order to assist the decision-making process of fisheries business operators in coastal areas in the Kei Islands, Indonesia. With the main objective of expediting the supply chain of fisheries in this region. The design of the DSS that was built was adjusted to the needs of every fishery businessman in this region. So, in addition to web-based DSS framework design, we also conducted surveys on fisheries supply chain processes from capture fishermen and aquaculture farmers to adjust the conditions of needs and empirical problems to design DSS concepts that can help overcome supply chain problems in the fisheries sector in the region.

Sistem Pendukung Keputusan dalam Menentukan Kualitas Lateks pada Tanaman Karet Menggunakan Matlab dengan Metode Fuzzy Mamdani (Studi Kasus PT. Gergas Utama Kecamatan Wampu Kabupaten Langkat)

Rubber plant is one of plants that produces latex which can be used as materials production of vehicle tools, health equipment, mat and other kinds of equipment. In PT Gergas Utama, Wampu Sub-district, District of Langkat, a good and proper quality of ruber plants are still determined by applying conventional method of Meter Lock usage. The criteria to produce International standard Latex are the age of rubber plants, winding width, and climate or weather. In the research, the Mamdani Fuzzy method was applied as the Decision Support System in order to determine the best quality of latex which can optimize the time and can overcome failures in determining latex quality. Fuzzy variables used in the research are the age of rubber plants, winding width, and climate or weather. The Fuzzy set used in each variable are is young (0-6), medium (3-12), old (9-15), small ( 0-0.8 ), medium (0.4-1.6 ), big (1.2 -2 ), rain ( 0-20 ), transition ( 15-30 ), dry (25-35). Fuzzy Mamdani method consists of Fuzzification, Rule Formation, Inference Engine, Defuzzification and Matlab Programming

Improving Food Supply Chain Management by a Sustainable Approach to Supplier Evaluation

Increasing food supply chain sustainability means having to deal with many conflicting aspects and involves producers, several departments in distribution companies, and consumers. The objectives of this research are to develop models to solve real-world supplier evaluation problems and validate them with real data on fresh fruits in a supermarket chain. Literature review and results from a survey with managers from purchasing, logistics, and quality departments of a food distribution company are used to establish criteria, to first model the assessment of products and, second, to model supplier evaluation. A multicriteria hybrid approach is proposed, using multi-attribute utility theory (MAUT) to assess the quality of products and Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) to complete their evaluation with strategic criteria to be included in the second phase. The results allow companies to rank suppliers by product and classify them according to the main criteria categories, such as product strategy, food safety, economic, logistic, commercial, green image and corporate social responsibility. A sorting approach is also applied to obtain ordered groups of suppliers. Finally, the models proposed can form the core of a decision support system in order to create and monitor the supplier base in food distribution companies, as well as to inform sustainable decision making.

A data-driven methodology for the automated configuration of online algorithms

With the goal of devising algorithms for decision support in operational tasks, we introduce a new methodology for the automated configuration of algorithms for combinatorial online optimization problems. The procedure draws upon available instance data and is capable of recognizing data patterns which prove beneficial to the overall outcome. Since online optimization requires repetitive decision making without complete future information, no online algorithm can be optimal for every instance and it is reasonable to restrict attention to rule-based algorithms. We consider such algorithms in the form of procedures which derive their decisions using a threshold value. Threshold values are computed by evaluating a mathematical term (threshold value expression) composed of the available instance data elements. The goal then consists of determining the structure of the threshold value expression leading to the best algorithm performance. To this end, we employ a simulated annealing scheme returning the most favorable term composition given the available instance data. The resulting methodology can be implemented as part of data-driven decision support systems in order to facilitate knowledge-based decision making. Decision rules are generated in an automated fashion once historical input data is provided. The methodology is successfully instantiated in a series of computational experiments for three classes of combinatorial online optimization problems (scheduling, packing, lot sizing). Results show that automatically configured online algorithms are even capable of substantially outperforming well-known online algorithms in respective problem settings. We attribute this effect to the methodology's capability of integrating instance data into the process of algorithm configuration.

Development of an algorithm to train artificial neural networks for intelligent decision support systems

The algorithm to train artificial neural networks for intelligent decision support systems has been constructed. A distinctive feature of the proposed algorithm is that it conducts training not only for synaptic weights of an artificial neural network, but also for the type and parameters of membership function. In case of inability to ensure the assigned quality of functioning of artificial neural networks due to training of parameters of artificial neural network, the architecture of artificial neural networks is trained. The choice of the architecture, type and parameters of membership function occurs taking into consideration the computation resources of the facility and taking into consideration the type and the amount of information entering the input of an artificial neural network. In addition, when using the proposed algorithm, there is no accumulation of an error of artificial neural networks training as a result of processing the information entering the input of artificial neural networks.Development of the proposed algorithm was predetermined by the need to train artificial neural networks for intelligent decision support systems in order to process more information given the unambiguity of decisions being made. The research results revealed that the specified training algorithm provides on average 16–23 % higher the efficiency of training artificial neural networks training that is on average by 16–23 % higher and does not accumulate errors in the course of training. The specified algorithm will make it possible to conduct training of artificial neural networks; to determine effective measures to enhance the efficiency of functioning of artificial neural networks. The developed algorithm will also enable the improvement of the efficiency of functioning of artificial neural networks due to training the parameters and the architecture of artificial neural networks. The proposed algorithm reduces the use of computational resources of decision support systems. The application of the developed algorithm makes it possible to work out the measures aimed at improving the effectiveness of training artificial neural networks and to increase the efficiency of information processing

Applying available-to-promise (ATP) concept in mixed-model assembly line sequencing problems in a Make-To-Order (MTO) environment: problem extension, model formulation and Lagrangian relaxation algorithm

Mixed-model assembly line is known to be a special case of production lines where variety of product models similar to product characteristics are assembled. This article addresses available-to-promise (ATP) in mixed-model assembly line sequencing problems in a Make-To-Order environment in two stages. First, the customers are prioritized based on their corresponding profit values and a decision support system for order acceptance/rejection based on ATP is developed. By implementing this concept and developing a mathematical model, delivery quantity and date in a planning horizon are determined based on the inventories in the stock. The second stage is solving a mixed binary mathematical model to sequence accepted orders suitably according to demands due dates that guarantees the orders are not released too late or too early. The problem simultaneously considers following objectives: minimizing the total tardiness and earliness costs based on the determined priority of orders and minimizing the utility work and idle time of workers in the production line. An algorithm based on Lagrangian relaxation is developed for the problem, and tested in terms of solution quality and computational efficiency. To validate the performance of the proposed algorithm, various test problems in small size are solved using the CPLEX solver, and compared with the Lagrangian relaxation method. Finally, the proposed model is solved in large size problems to analyze the model performance. The drawback of the CPLEX is that it could not solve large problem instances in reasonable time. For the small sized problem, there is approximately 1% duality gap for the Lagrangian relaxation method. The maximum duality gap in the Lagrangian relaxation method for the large sized problem is always kept below 4% while the average computing time is very reasonable. Therefore, according to the results obtained from test problems, the developed Lagrangian relaxation method proved to be the suitable method for this problem.

Propulsion Monitoring System for Digitized Ship Management: Preliminary Results from a Case Study

The paradigm of Industry 4.0 a fundamental driver of innovation in marine industry, where the new digital era will see the development of smart cyber-ships equipped with advanced automation systems that will progressively evolve towards fully autonomous vessels. Although the journey towards such technological frontier has started, most companies operating in the maritime sector still appear un-prepared to face the future scenario. In the maritime sector, in fact, empirical models and oversimplified approaches are still largely employed for the management of fleet operations. There is thus the necessity of developing and providing operative models for digitized ship management, which, based on structured information gathering and processing, can provide maritime companies with effective decision support systems in order to strengthen their value chain. This paper focuses on the context of the monitoring of the propulsion system, which is one of the most important systems of a ship and a main source of operation and support costs. A decision support system is presented involving automated data gathering and analysis procedures, to assess the correct functioning of the system and for early-detection of incipient failures. The methodology has been validated through a real case study, and the related results are discussed.

Simple Additive Weighting algorithm to aid administrator decision making of the underprivileged scholarship

Underprivileged scholarships are assistance provided by government or institutions to students from poor families. Scholarships can be given to all students at the education level from elementary school to higher education with certain conditions and criteria. However, in reality many scholarships are given that are not on target. This is because of the number of students who register as scholarship recipients. Therefore, it is necessary to have a decision support system in order to aid the selection process. This study aims to find appropriate system to aid the underprivileged scholarship selection. This study proposed a system which was built using PHP for programming languages and using MySQL as its database using the Simple Additive Weighting method. Data is taken from SMK Sultan Agung 1 Jombang. Data is then analyzed and tested using the Blackbox testing method. Results of this research obtained a list of the best ranks of prospective scholarship recipients which are result of processing from system. The results obtained from system will be validated by means of comparisons with manual calculations as in Table 1. The validation results show same results, so that system can be said to be valid and can replace process that is done manually.

Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms

Synthetic aperture radar (SAR) images are a valuable source of information for the detection of marine oil spills. For their effective analysis, it is important to have segmentation algorithms that can delimit possible oil spill areas. This article addresses the application of clustering, logistic regression and convolutional neural network algorithms for the detection of oil spills in Envisat and Sentinel-1 satellite images. Large oil spills do not occur frequently so that the identification of a pixel as oil is relatively uncommon. Metrics based on Precision–Recall curves have been employed because they are useful for problems with an imbalance in the number of samples from the classes. Although logistic regression and clustering algorithms can be considered useful for oil spill segmentation, the combination of convolutional techniques and neural networks achieves the best results with low computing time. A convolutional neural network has been integrated into a decision support system in order to facilitate decision-making and data analysis of possible oil spill events.

Actor-Based Approaches in Business Administration Research

This essay argues that the research questions of business administration as an academic discipline has to deal with can be classified into three types: first-order problems concern problems occurring in the daily operations of firms, for instance, how to correctly calculate the net present value of an investment project. Second-order problems concern how institutional settings in the firm must be designed in order to assure that solutions to first-order problems are actually applied by the actors in the firm. For instance, how to design a decision support system in order to avoid costly decision-biases known to occur in managerial decision-making. Third-order problems concern the question, why some firms install such advantageous institutional settings, while others do not. Economics typically assumes that solving first- and second-order problems constitute a competitive advantage for the firm which is able to do so, which implies that competition will force all firms to do so. However, entrepreneurial practice indicates this to be not the case: first, some institutions, for instance for surveillance, are costly to operate and the operating costs of such institutions may well exceed the costs arsing from the problem itself. Second, organizational changes are costly too, both in terms of installing a new organizational setup and in terms of overcoming the resistance of persons with vested interests in the existing institutional setting and general organizational inertia. While first-order problems constitute the traditional domain of business administration research and in particular teaching, the behavioral turn, the inclusion of psychological elements in the research agenda and “explanatory toolbox” of economics and business administration, is accompanied by a shift of the focus on second- and third-order problems. This raises the question, what methodological basis is appropriate for introducing explanations originating from an individual-level focused discipline like psychology to the research agenda of business administration research, which is concerned with collective entities, like firms and markets. This essay is about how actor-based methodological approaches from sociology can contribute to solving second- and third-order problems, using the cases of decision biases and the choice of institutional settings as examples.

Neuro-fuzzy inference systems approach to decision support system for economic order quantity

Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neuro-fuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision support system gives valid results. The proposed system is flexible and can be applied to various types of goods in SCM.

GIS Based Spatial Modeling to Mapping and Estimation Relative Risk of Different Diseases Using Inverse Distance Weighting (IDW) Interpolation Algorithm and Evidential Belief Function (EBF) (Case study: Minor Part of Kirkuk City, Iraq)

The health of the individual is one of the most important indicators of good living and quality of life for the community. Therefore, the contribution of developing of public health sector management and monitoring of diseases related to the cultural, economic, and social progress of any society. Moreover, the diseases occur from spatial factors where the distribution and concentration differ in diverse positions. Hence, GIS can be used as a decision support system in order to help the mangers, assess and monitoring of various types of diseases. Thus, this research aims to define a spatial distribution, prediction of risks and analysis of disease hazard areas in Kirkuk city, north east of Iraq using two models evidential belief function (EBF) and Inverse distance weighting (IDW). IDW determines the correlation between conditioning factors and disease occurrence. Consequently, EBF can be used to assess the effect of each class of conditioning factors on diseases occurrence. The result shows that Al-Wasity quarter reports the highest range of the patients who have the blood diseases (D89-D50) in 2017. Contrary, the northern parts of the city and some quarters in the center of the city (Tessen, Bagdad road, Al-Mansor) reflect the lowest range of the patients in blood diseases. Eye diseases (H59-H00) and its accessories have the same spatial distribution. The result also demonstrated that the GIS based spatial techniques is provided a prospect to simplify and measure the epidemic state of different diseases within specific areas(minor part of Kirkuk city), and lay a base to pursue future surveys into the environmental factors responsible for the augmented disease threat.

Towards a knowledge management framework for crossing knowledge boundaries in agricultural value chain

The purpose of the paper is to propose a framework for the development of a decision support system in order to evaluate the knowledge boundaries in agricultural value chain. Knowledge boundaries exist due to differences in the way we work, share our knowledge, expertise, different organisational culture, etc. In this paper, we identify the most common knowledge boundaries that are reported in the literature, and propose a general framework for a preparation of a decision support system to evaluate the existing knowledge boundaries. In particular, we are interested in identifying the knowledge boundaries in agricultural value chain, evaluating them and providing possible solutions of crossing them. It is a two-step method: firstly, a semi-automatic ontology is generated using the freely available tool OntoGen, which we use to define the most commonly reported concepts in crossing knowledge boundaries, and then, based on the obtained ontology, we propose a decision support system for evaluation of the level to which the boundaries exist.