Simple Decision Support System Research Articles

Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling

This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem. The uncertainty is modeled directly as part of recurrent artificial neural networks, but using two fundamentally different approaches: one based on Deep Quantile Regression and the other on Bayesian neural network. Both approaches use a recurrent neural network to predict multiple time steps into the future, but handle the time-dependent uncertainty estimation differently. We present a novel sampling technique in order to aggregate quantile estimates for link level travel time to yield the multi-link travel time distribution needed for a vehicle to travel from its current position to a specific downstream stop point or transfer site.To motivate the relevance of uncertainty-aware models in the domain, we focus on the connection protection application as a case study: An expert system to determine whether a bus driver should hold and wait for a connecting service, thus ensuring the connection, or break the connection and reduce its own delay. Our results show that the proposed quantile sampling method performs overall best for the 80%, 90% and 95% prediction intervals, both for a 15 min time horizon into the future (t+1), but also for the 30 and 45 min time horizon (t+2 and t+3), with a constant, but very small underestimation of the uncertainty interval (1–4 pp.). However, we also show, that the Bayesian model still can outperform the DQR for specific cases. Lastly, we demonstrate how a simple decision support system can take advantage of our uncertainty-aware travel time models to prioritize the difference in travel time uncertainty for bus holding at strategic points, thus reducing the introduced delay for the connection protection application.

Identifying Key Risk Factors in Product Development Projects

In a rapidly changing world, most operational processes of enterprises are conducted in the form of development projects. The development of new products is an important organizational strategy to meet consumer needs. During this process, enterprises often encounter many bottlenecks and risks that can cause delays in, and even the failure of, development projects. In this study, we developed a research framework based on relevant literature and expert interviews and then used the decision making trial and evaluation laboratory (DEMATEL) and the analytic network process (ANP) to determine the relationships among and the importance of risks in the development of new products. The results of a case study show that the six key risks of product development projects include project completion time, mastery of key technical capabilities, controlling the progress of the project, uniqueness and complexity of the project, ability to control the market, and functional integrity of the product. According to the results of importance performance analysis, six key factors were classified in the concentrate quadrant. The optoelectronic manufacturing industry should focus on reducing risks to the project. A cause-and-effect diagram shows that if an enterprise wants to improve performance in terms of these key factors, it should first improve the project completion time or the mastery of key technical capabilities. Therefore, it is appropriate to start by improving the project completion time. In this study, we developed a practical and simple decision support system that allows managers of research and development to examine the risk of projects and assess the relevant risks. A case study was also conducted to test the accuracy of the proposed risk-management method.

Decision Support System (DSS) dengan Berorientasi -Solver

A Decision Support System (DSS) or decision support system is part of a computer-based information system (including knowledge-based/knowledge management systems) that is used to support decision-making within an organization or company. can also be said as a computer system that processes data into information to make decisions on specific semi-structured problems. In this paper, we try to solve a simple DSS with Microsoft Excel by using Solver.

A decision support system for predicting settling velocity of spherical and non-spherical particles in Newtonian fluids

An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML.

Urban Water Consumption and its Influencing Factor Identification using Water Decision Support System

Due to population growth and economic development, the water shortage in many urban cities has become a serious concern for sustainability and management. In-house surveys and in-person monitoring of water consumption is a tedious task. To overcome this difficulty Geographic Information System helps in finding the optimal solution. The study on existing literature outlines the major underlying issues and challenges in analyzing water consumption patterns. The objective of this work is to identify urban water consumption patterns and ways to provide a surplus supply of water using geographic Information. The results reveal that the urban areas with a high economy consume more water than the low-income group. As a feasible solution, a simple water decision support system is proposed with the help of GIS to overcome the upcoming challenges.

Application of risk-based fuzzy decision support systems in new product development: An R-VIKOR approach

Innovative manufacturing firms strive to sustain and enhance their competitive advantages by running a range of new product development (NPD) projects in a consistent manner. The capital and time required to execute the NPD projects have substantially increased over the past years. This magnified the risk-aversion behavior of R&D managers and has increased their sensitivity towards the underlying risk of NPD projects. In particular, the R&D departments have recently started to proactively assess the accuracy of ambiguous information that is extensively used in preliminary market study and customer requirements analysis. Thanks to its high performance in dynamic environments, the R-numbers method can be employed to capture and analyze the risk of fuzzy numbers in a variety of decision making models. To tackle the complexity of such analysis, this paper proposes a novel risk-based fuzzy VIKOR (R-VIKOR) methodology. Using the interpretive structural modeling, the risk factors are first classified to identify and rank the existing critical risk factors of NPD projects. The ultimate goal of this study is to develop a practical yet simple decision support system tool that enables the R&D managers to effectively examine the riskiness of fuzzy information and assess the relevant risk factors. A real-world case study is presented to test and examine the accuracy and effectiveness of the proposed risk management method.

Demand forecasting methods for spare parts logistics for aviation: a real-world implementation of the Bootstrap method

One of the critical issues that an airline faces in its day-to-day operations is a correct prognosis of the necessary quantity of spare parts that are continuously fed into unexpected maintenance operations. Indeed, there is a critical need for accurate forecasting methods to predict the demand of these spare parts in order to minimize the so-called Aircraft-On-Ground situations. This paper describes the real-world implementation of the Bootstrap method and the assessment of its performance with actual data from aviation logistics. The analysis reveals that the Bootstrap method, while not the most accurate in every case, should be preferred over other popular methods in spare parts forecasting for aviation, because is more agile and can address adequately all categories of demand. A simple decision support system is then presented to assist airline materials managers in using the bootstrap method. The system is expandable and can potentially incorporate other forecasting method as well.

Decision Support Systems Suitability in Agarwood Tree Planting Using Simple Additive Weighting Method (Saw) In Merauke District

Gaharu is a blackish colored wood produced by a number of tree species from the genusAquilaria,especiallyAquilaria Malaccensis. This type is used in the fragrance industry (perfume and clover) because it has a fragrant aroma. Gaharu is a commodity of non-timber forest products (HHBK) which is quite reliable, especially when viewed from its very special price when compared to other NTFPs. A simple decision support system (DSS) can be defined as a computer-based system that is used to make it easier to make decisions. The decision-making ability used by the community is still conventional in determining suitable land for gaharu planting, without a model to assist the assessment process. Based on the results of research conducted using the Simple Additive Weighting (SAW) method with three samples of land and the type of gaharu to be planted isAquilaria Malaccensis, which refers to several indicators including soil type, soil pH, altitude, rainfall, and temperature. land C with a value of 0.42.

A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

An Intelligent Stock Trading Decision Support System Using the Genetic Algorithm

The authors present a simple data-driven decision support system for stock market trading using multiple technical indicators, decision trees, and genetic algorithms (GAs). It assembles technical indicators set into a decision tree based on stock trading rules and generates buy, hold, and sell classes that represent trading decisions. The main contribution of this study is the use of GAs based on a two-step classification method. This allows for selecting the relevant inputs and adapting them to the market dynamic. The GAs are used at the data input selection step and the weight selection step. Classifiers of different technical indicators are trained in the first step and combined into the trading rules in the second step. Random sampling and data input selection techniques were used to ensure the required variety of technical indicators in the first step. An evaluation shows that the proposed algorithm improved forecasting accuracy from 73.6% to 81.78%.

Decision Support System for Selecting Sustainable Alternatives to Conventional Jet Fuel: Impact of Emissions, Production Costs and Carbon Pricing

The United States Environmental Protection Agency (EPA) in June 2015, took a step toward regulating carbon emissions from airlines, following an assessment that airlines contribute to climate change. On July 25, 2016, the final endangerment finding (Note 1) under section 231(a) (2) (A) of the Clean Air Act for aviation emissions was issued by the EPA. The European Union had issued a similar finding previously and had proposed implementing an emission trading scheme in which the airlines would be required to participate in a cap and trade scheme for emissions from jet fuel. Traditional jet fuel is derived from petroleum, whose price is volatile and depends on geopolitical stability. Fuel burn is a significant cost for airlines and affects their profitability and value. Fuel burn is also a significant source of greenhouse gas emissions. An investigation of alternatives to jet fuel and switching from conventional jet fuel based on varying emission profiles, production costs and varying carbon prices is therefore timely. We use a simple decision support system to examine the link between the life-cycle greenhouse gas emissions of a range of fuels, economic costs of production and varying carbon prices. This analysis should be of interest to regulators, traders, risk managers and executives in the airline industry as well as practitioners of sustainability management.

Development and Field Testing of a Spatial Decision Support System to Control Populations of the European Cherry Fruit Fly, Rhagoletis cerasi, in Commercial Orchards

The European cherry fruit fly, Rhagoletis cerasi (Diptera: Tephritidae), is a key pest for the cherry production industry in Europe and west Asia that has recently invaded North America. Insecticide applications are frequently employed to control this devastating pest, often without considering its population trends. We developed a novel decision support system (DSS), and field tested it in commercial sweet cherry orchards in central Greece. The DSS includes two algorithms that predict the timing of adult activity in the wild and support pest management decisions, based on R. cerasi population trends and pesticide properties, respectively. Preparatory monitoring of the testing area during 2014, using adult traps, revealed high population densities of R. cerasi in non-managed sweet cherry orchards and low densities in commercial ones. Implementation of the DSS during 2015 resulted in low R. cerasi adult population densities and zero fruit infestation rates in commercial cherry orchards. Similar population and infestation rates were recorded in conventionally treated plots that received on average two insecticide applications compared to the one-half that the DSS treated plots received. Simultaneously, high population densities and fruit infestation rates were recorded in non-managed cherry orchards. Apparently, the implementation of the simple DSS we developed reduces the cost of R. cerasi management and minimizes the chemical footprint on both the harvested fruit and the environment.

Data mining applied for national road maintenance decision support system

National roads are one of the main networks of a country’s transportation system. To maintain the performance level of national roads requires a well-structured pavement management system (PMS). The decision support system (DSS) is inseparable in the modern PMS, which required the development of a new approach for the DSS in support of national road network maintenance. The proposed model integrates data mining (DM) and geographical information system (GIS) to construct a simple DSS. DM is used to developed road maintenance optimization models, and then integrated with DSS with the help of GIS as an interface application. Historical data on the national road network in West Java, Indonesia is used as a case study. Examples show that the proposed model can determine a decision support solution efficiently. In addition, a userfriendly computer interface is developed so that PMS stakeholders can plan pavement maintenance simply and effectively.

Influence of cognitive human factor on nuclear reactor safety: a simple decision support system for operators in emergency conditions

In this paper a prototype for the TRIGA Research Reactor Decision Support System is proposed. Its framework is realised on the basis of three models: the Universal Management Paradigm, the Information-Preference-Knowledge Model from the TOGA meta-theory, and the Finite State Machine Model. Such an application uses data from the instrumentation and control system and according to the human factor state recognises the type of operative situation and suggests to the operator a decision action as an output. The tool can be used for helping the operator in accident conditions. Here, for illustrative purposes only, an accidental variation of the control rod position has been simulated and results from the proposed tool are analysed.

A Simple and Farmer-Friendly Decision Support System for Enhancing Water Use Efficiency in Agriculture:Tool Development, Testing and Validation

In the semi-arid tropics (SAT) farmers practice calendar- based irrigation scheduling, which generally results in over irrigation and poor water use efficiency. The lack of a simple decision tool to decide timing and quantity of water to be applied is a bottleneck. An Excel-based decision support system termed Water Impact Calculator (WIC) is developed using data collected at the ICRISAT, which were validated at three pilot sites on farmers' fields in Rajasthan, Gujarat and Telangana. Field studies were conducted under two land-form treatments (broad bed and furrow (BBF) and flat fields); and irrigation water was applied following two different methods (drip and flood). The data collected at micro-watershed at the ICRISAT and three other sites showed that WIC could be used under wide range of soil and rainfall conditions. WIC simulated soil moisture was comparable with the observed moisture data, which forms the basis of irrigation scheduling. The WIC-based water balance at these experimental sites showed that number and amount of irrigation could be reduced by 30-40% using WIC-based irrigation scheduling without compromising the crop yield. The WIC could be a potential tool for water resources planning and efficient management at the field and watershed scale in the SAT.

Combining Land Subsidence and Geology for Improved Coastal Climate Adaptation

Land subsidence is mapped over two Danish coastal study locations from precision leveling and modified Sentinel-1 satellite data. Geological models are produced after digitization of geotechnical archives. The inferred subsidence patterns are related to the geology and advantages of bringing this information together in flood risk assessments and in climate adaptation are discussed. The lack of data for subsidence monitoring and a fragmentation of geotechnical information are considered a hindrance to optimal adaptation measures in Denmark. A national subsidence mapping to gain an overview of the challenges faced is advocated. A simple decision support system can implement subsidence and geotechnical information in coastal climate adaptation to the benefit of municipalities and other relevant stakeholders.

A Simple Synchro – Modal Decision Support Tool for the Piraeus Container Terminal

The concept of Synchro modality is effectively an evolution of a multimodal supply chain. It integrates different transport modes and gives shippers and logistics service providers the freedom to deploy different modes of transportation in the same chain and in a flexible way so as to gain the desired outcome according to their priorities in a certain trip. Time, costs and emissions are certainly the three most relevant parameters when talking about a multimodal transportation chain. In most cases the logistics provider has set priorities to conform with, and obviously above mentioned constrains influence each other in an adverse way. With the development of ICT technologies and systems installed on board and on shore and with a simple decision support system fed with input from tracking and tracing systems or traffic monitoring systems, one can easily and flexibly plan his transportation job and maintain his set priority while in parallel keeping the remaining two parameters in control. Down times for example could be eliminated and efficiency gains could be achieved with decreased environmental footprint.The Port of Piraeus is the largest Greek seaport and one of the largest ports in the Mediterranean Sea basin. It exhibits an impressive container traffic growth rate over the last 4 years triggered by its partial privatization and a recently completed hinterland connection to the rail network, which associated the port with the South-Eastern European corridor e.g. the route Far Eastern ports–Piraeus–Prague.The current paper will present an easy to use simple tool to continuously assess even during the transportation event all the alternative modes for a given destination in terms of time cost and emissions. An analytical fully parameterized model will be the basis for this tool which will be run for the chain Shanghai–Piraeus–Prague. The overall scenario is as follows: A container ship is arriving from China to the Piraeus Container Terminal. One of its containers is destined to an inland Enterprise in Prague. The most common way for transportation to Prague is rail, but also truck could be an alternative solution and of course a combination of a Short Sea Shipping part to Thessaloniki and then truck or train to Prague. Emphasis in the calculations will be given to emissions for all the modes and relations will be shown with time and cost.The tool developed is based on the case study above, being however open architecture software it can be expanded and applied to other ports and routes. The final outcome will be an easy and user friendly tool with the possibility to alter different input parameters and receive quickly a useful decision support system for the shipper or the logistics providers. Finally, there are two loops foreseen for the runs of the program. The required input parameters at each stage are either directly fed to the program if available (e.g vessels ETA and position through GPS, VTS, ETC) or calculated if this is not the case.

Developing a Geographic Information System-Based Assessment Model for Sustainable Metropolitan Development: The Case of the Semarang Metropolitan Region, Indonesia

Spatial dynamics of metropolitan development are becoming important issues in sustainable development. In developing countries like Indonesia, the growth of metropolitan regions is often followed by problems of unsustainability. Therefore, a framework of assessment for metropolitan development can be useful as an early means of identifying any untoward aspects. This study aims to develop an assessment system that is easily applicable (user-friendly) in assessing metropolitan development in Indonesia. It is a Geographic Information System (GIS)-based spatial model formulated as a simple Decision Support System (DSS), within which information can be queried down to district (kecamatan) level. As a test case, the model was applied in the Semarang Metropolitan Region of Central Java. The results show that it is capable of documenting and assessing the level of sustainability of the region. However, the lack of data at the kecamatan level is the main barrier to the application and development of the technique. Consequently, an in-depth study involving intensive dissemination of the concept, initiative and promulgation of initial results is subsequently proposed so that awareness of the local decision makers of the Indonesian government can be improved.

Predicting length of stay from an electronic patient record system: a primary total knee replacement example

BackgroundTo investigate whether factors can be identified that significantly affect hospital length of stay from those available in an electronic patient record system, using primary total knee replacements as an example. To investigate whether a model can be produced to predict the length of stay based on these factors to help resource planning and patient expectations on their length of stay.MethodsData were extracted from the electronic patient record system for discharges from primary total knee operations from January 2007 to December 2011 (n = 2,130) at one UK hospital and analysed for their effect on length of stay using Mann-Whitney and Kruskal-Wallis tests for discrete data and Spearman’s correlation coefficient for continuous data. Models for predicting length of stay for primary total knee replacements were tested using the Poisson regression and the negative binomial modelling techniques.ResultsFactors found to have a significant effect on length of stay were age, gender, consultant, discharge destination, deprivation and ethnicity. Applying a negative binomial model to these variables was successful. The model predicted the length of stay of those patients who stayed 4–6 days (~50% of admissions) with 75% accuracy within 2 days (model data). Overall, the model predicted the total days stayed over 5 years to be only 88 days more than actual, a 6.9% uplift (test data).ConclusionsValuable information can be found about length of stay from the analysis of variables easily extracted from an electronic patient record system. Models can be successfully created to help improve resource planning and from which a simple decision support system can be produced to help patient expectation on their length of stay.

FUZZY EVALUATION OF SERVICE QUALITY IN THE BANKING SECTOR: A DECISION SUPPORT SYSTEM

This study aims at proposing a modified 7-category fuzzy SERVQUAL to measure service quality for the banking sector. We have added new categories to the conventional SERVQUAL to account for the impacts of the bank’s Corporate Social Responsibility (CSR) initiatives, and e-banking. A questionnaire was distributed among the banks’ customers to measure the criteria’s weights, perceived performance and expected performance. We use the criteria weights, perceived performance and expected performance to test the proposed index for three of the largest Iran’s banks, namely: Bank Melli Iran, Bank Mellat and Bank Saderat Iran which henceforth will be referred to as banks A, B, C throughout the study. A simple Decision Support System (DSS) was devised to determine the most successful bank with regard to CS using Gap model. The result of this study showed that Bank B has the lowest weighted performanceexpectation gap (-0.051) among the three banks. It also has the highest average performance score in all dimensions and rank the best on 22 out of 30 criteria. Finally, sensitivity analyses are conducted to assess the impact of criteria weights and fuzzy numbers on the evaluation/ranking process. The resultant index helps managers in micro, meso and macro levels to detect the frailty zones and atone for the dissatisfying functions.