Decision Support System For Planning Research Articles

Analytic network process (ANP)-based spatial decision support system (SDSS) for sustainable tourism planning in Cameron Highlands, Malaysia

Sustainable approach to tourism development is rarely addressed among authorities and participants. This research presents an approach based on an integrated use of analytic network process (ANP) and geographic information system (GIS) for sustainable tourism planning in Cameron Highlands, Malaysia. ANP was utilized to evaluate the relative priorities for the sustainable tourism development of the highlands based on chosen criteria. ANP results were then transferred into GIS environment and attached to their respective criterion maps. Subsequently, GIS-based sustainable tourism map was generated, which defines the various types of tourism activities that are compatible with the highlands according to the sensitivity of the highland’s environment. The final output was validated using Cameron Highlands Local Plan.

EN-ROUTE AIRSPACE CAPACITY AND TRAFFIC FLOW ENHANCEMENT USING GENETIC ALGORITHMS

Air transportation industry has gone under a rapid and continuous growth for the last four decades. International Civil Aviation Organization (ICAO) and airlines anticipate that annual growth rate of air traffic will be 4.5-5.1% until 2030. Airspace capacity management becomes more critical for the safety, efficiency and sustainable growth of the industry. En-route areas cover the largest portion of the controlled airspace and are subdivided into small segments referred to as ‘en-route sectors’ usually monitored and controlled by a single air traffic controller in the relevant Area Control Center (ACC). When air traffic demand reaches or exceeds the available en-route sector capacity, serious aircraft delays occur at airports or in airspace and lead to system-wide congestions and interruptions in air traffic services, airline flight schedules and airport operations, and therefore result in increased operational costs, passenger dissatisfaction and air traffic controller workload. This study presents a multiple entry point assignment model based on a genetic algorithm to minimize delays and increase throughput of a generic high-altitude en-route sector. The proposed approach intends to use more flexible direct route configuration without changing the sector geometry and provide a framework for a decision support system for air traffic flow and capacity planning and management. The available single entry point sector configuration and its multiple entry point assignment alternatives were compared for various traffic scenarios. Multiple entry point configurations provided up to 10% increase in throughput and significant reductions in average delay per aircraft compared to the single point entry configuration.

A multi-user decision support system for online city bus tour planning

Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry. The city bus tour (CBT) service is one of the most successful businesses in the tourism industry. However, there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT. In this research, we report on the ongoing development of a mobile application (app) and a website for tourists, hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time. Firstly, the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem. All places of interest (PoI) and pickup/dropout points are identified with arcs of the network (instead of nodes), each of which can be visited at least once (instead of exactly once). Secondly, the resulting pure integer programming (IP) problem is solved using a leading optimization software known as General Algebraic Modeling System (GAMS). The GAMS code developed for this project returns: (1) the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network; (2) the augmenting paths corresponding to the pickup stage, the PoI visiting stage and the drop-off stage. Finally, we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne (Australia). All the computations relative to the initial tests show that the ability of the app to answer users’ inquiries in a fraction of a minute.

Potential of Geographic Information Systems for Refugee Crisis: Syrian Refugee Relocation in Urban Habitats

Spatial decision support systems have become ubiquitous tools in planning and understanding regional dynamics. With the present challenges faced throughout the world, Canada is becoming an increasingly important benchmark for migrants of different nations to start safe and productive lives. With the latest group currently entering the country are refugees being brought in primarily from Syria, in larger numbers than Canada has seen in decades. Because of this, the government has had to rapidly implement plans to allocate this influx to available places around the country. Because of the particular status of these immigrant's particular attention must be given to address complex social and economic interactions that guarantee opportunity and growth for refugees as well as functional socio-economic habitats within metropolitan regions. This paper adopts a spatially-explicit approach using a key set of socio-economic variables to understand micro-spatial location optima for refugees to begin their lives in Toronto. By intertwining key variables such as accessibility to employment, English language classes, people of similar cultures or situations, proximity to food/clothing/healthcare, a combinatory metric is designed to assess the most adequate liveability within the Toronto Census Metropolitan Area. The micro-spatial models propose an integrative vision of weighted measures to assess the spatial perspective brought by Geographic Information Systems. It is concluded that suburban regions around major cities hold a significant potential for refugee habitats, suggesting the integration of regional intelligence paradigms in the spatial planning and regional decision support systems of governmental and policy interaction.

Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study

AbstractPurposeThe aim of this study is to develop predictive models to predict organ at risk (OAR) complication level, classification of OAR dose-volume and combination of this function with our in-house developed treatment decision support system.Materials and methodsWe analysed the support vector machine and decision tree algorithm for predicting OAR complication level and toxicity in order to integrate this function into our in-house radiation treatment planning decision support system. A total of 12 TomoTherapyTM treatment plans for prostate cancer were established, and a hundred modelled plans were generated to analyse the toxicity prediction for bladder and rectum.ResultsThe toxicity prediction algorithm analysis showed 91·0% accuracy in the training process. A scatter plot for bladder and rectum was obtained by 100 modelled plans and classification result derived. OAR complication level was analysed and risk factor for 25% bladder and 50% rectum was detected by decision tree. Therefore, it was shown that complication prediction of patients using big data-based clinical information is possible.ConclusionWe verified the accuracy of the tested algorithm using prostate cancer cases. Side effects can be minimised by applying this predictive modelling algorithm with the planning decision support system for patient-specific radiotherapy planning.

A spatial decision support system for special health facility location planning in developing regions

Access to healthcare is a determinant of the wellbeing of the people. Planning the location and distribution of health facilities to ensure efficiency and equity in the face of limited resources can be challenging, especially where the type of care requires expensive equipments and specialists. This study attempts to provide a spatial decision support system (SDSS) to select specific locations for provision of mental health care out of existing health centres. The SDSS in this study uses a geographic information system (GIS) and location-allocation models. The data used in the SDSS include the coordinates of the location of the mental health facilities, the coordinates and population of the settlements and data on the roads and footpaths in the study area. The empirical results from the application of the SDSS framework shows that the average distance travelled in the existing configuration of mental health care centres (MHC) could be reduced from 15.3 km to 14 km by adopting a model plan. It is also shown that 74.85% of the settlements are more than ten kilometers from the nearest MHC. In order to minimize travel distance and maximize coverage of the population the minimize facilities, model option of the SDSS shows that 21 facilities are required to ensure that the average travel distance is 9.4km and that 98.6% of the settlements are not more than 20 km from the nearest MHC. Decision makers can use the SDSS discussed in this study to achieve efficiency or equity in the provision of health services. Thus it is hoped that health planners would adopt these techniques and tools to make their location decisions more efficient, reduce inequities and be rational in the use of resources. Key Words: Mental Health Facilities, Spatial Decision Support System, Location Allocation Models, Location Efficiency, Location Equity

Sustaining the Joint Production of Timber and Lactarius Mushroom: A Case Study of a Forest Management Planning Unit in Northwestern Turkey

Forest management planning focusing on sustainable supply of forest-based services such as wood and Non-Wood Forest Products (NWFPs) is important for the sustainability of forest ecosystems over time. This study explores the development of a mushroom integrated decision support system (ETÇAPOptimization) for multiple use forest management planning and for the analysis of long-term effects of different forest management scenarios on the joint production of timber and mushroom. The Decision Support System (DSS) integrates both mushroom and timber production derived from the same forest ecosystem using empirical models for mushroom occurrence and yield as well as for tree growth. The DSS takes further into account the spatial distribution and productivity models of Lactarius deliciosus and Lactarius salmonicolor generated for the Kızılcasu Planning unit in Northwest Turkey. Six different forest management scenarios were considered, each with a different set of objectives, e.g., maximization of both the amount and the income from timber or mushroom production. Some scenarios include further timber even flow constraints (10% fluctuation). The Net Present Value (NPV) and the amount of timber and of mushroom production were used as performance indicators to discuss and elaborate on forest dynamics under different management scenarios. The results indicated that forest management planning strategies to address the maximization of NPV from mushroom production scenarios are characterized by substantial decreases in total income from the forest due mainly to the conservation of forest areas to favor mushroom production. On the other hand, the integration of regulatory constraints into forest management plans lead to a substantial decrease of both the economic profit and the amount of forest ecosystem services, e.g., timber and mushroom. The results showed that the NPV from mushroom production can be two to three times higher than the NPV from timber production based on carefully designed management objectives and constraints.

Special issue on Decision Support Systems for Medical Applications

Applications such as medical imaging depend strongly on the computer aided decision support systems for disease diagnosis and treatment planning. This applies an extra pressure on the computer based systems to provide highly accurate results within less convergence time. Soft computing techniques are widely preferred for such applications because of its numerous advantages. Though theoretically proven, these methodologies are rarely used in practice in real time scenario. This is mainly due to the various existing gaps between the theoretical and practical cases of soft computing approaches. This special issue stands as a bridge to unify the theoretical and practical concepts of soft computing techniques in the context of medical imaging applications. The focus of the special issue is on throwing new seeds of soft computing techniques for feasible application in the medical field.

A Radiation Therapy Treatment Planning Decision Support System (RTP-DSS) for Selecting Patient-Specific Optimal Treatment

A Radiation Therapy Treatment Planning Decision Support System (RTP-DSS) for Selecting Patient-Specific Optimal Treatment

Mathematical model for a microgrid consisting of wind turbine, PV panels, and energy storage unit

Micro grids are inclined to use renewable energy sources within the availability limits. Energy storage improved the management of hybrid energy resources used in parallel with the grid. This study models the hybrid usage of wind and solar energies as a support for the grid with the availability of batteries. A mixed integer mathematical model is proposed to schedule the use of different resources minimizing the cost following the power market. Capacity, energy balancing and demand constraints are taken into account for this purpose. This model can be used as a decision support system for micro grid load planning.

A decision support system for strategic maintenance planning in offshore wind farms

This paper presents a Decision Support System (DSS) for maintenance cost optimisation at an Offshore Wind Farm (OWF). The DSS is designed for use by multiple stakeholders in the OWF sector with the overall goal of informing maintenance strategy and hence reducing overall lifecycle maintenance costs at the OWF. Two optimisation models underpin the DSS. The first is a deterministic model that is intended for use by stakeholders with access to accurate failure rate data. The second is a stochastic model that is intended for use by stakeholders who have less certainty about failure rates. Solutions of both models are presented using a UK OWF that is in construction as an example. Conclusions as to the value of failure rate data are drawn by comparing the results of the two models. Sensitivity analysis is undertaken with respect to the turbine failure rate frequency and number of turbines at the site, with near linear trends observed for both factors. Finally, overall conclusions are drawn in the context of maintenance planning in the OWF sector.

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.

Usefulness and perceived usefulness of Decision Support Systems (DSSs) in participatory forest planning: the final users’ point of view

Abstract: In recent decades, the focus of forestry Decision Support Systems (DSSs) has expanded to consider the social dimension of forestry and to support participatory decision-making. A large number of models and tools have become available to solve forest management planning problems. The Usefulness of a DSS depends on the range of tools that it incorporates, and many researches have been developed to evaluate DSSs using Usefulness as parameter. The assessment of Usefulness concerns the effectiveness of a DSS. Furthermore, most assessments take into account the degree of Perceived Usefulness, which is considered an indicator of the impact a system has on job performance. The present study focuses on the analysis of final users’ point of view on the Usefulness and Perceived Usefulness of DSSs in participatory forest planning. The research investigates how forest users’ characteristics and context influence their views on the potentialities of DSSs to enhance both the various phases of the participatory planning process (Usefulness) and job performance (Perceived Usefulness). The study is based on quantitative data collected through two questionnaires e-mailed to a sample of 150 DSSs end users. The questionnaires focused on Usefulness and on Perceived Usefulness topics, respectively. Results indicate that special attention must be given to motivating respondents with a clear explanation of the survey objectives when e-mailing questionnaires. Moreover, results show that, in general, respondents consider DSSs useful at each step of the participatory process, despite differences emerge among steps. The research also shows that respondents’ Perceived Usefulness of DSSs was higher before actually engaging with DSSs. Furthermore, the results highlight differences in Perceived Usefulness to improve job performance, suggesting that the use of DSSs may actually improve job performance more than expected. Specifically, results indicate that improving the technical descriptions of methodologies incorporated in a DSS may contribute to increasing the Perceived Usefulness. The information provided within this research contributes to the advancement of knowledge regarding the Usefulness of DSSs as perceived by forest stakeholders, which in turn supports the improvement of DSS architectures and the development of participatory processes in forest management planning.

Nurse managers' decision-making in daily unit operation in peri-operative settings: a cross-sectional descriptive study.

To describe the tactical and the operational decisions made by nurse managers when managing the daily unit operation in peri-operative settings. Management is challenging as situations change rapidly and decisions are constantly made. Understanding decision-making in this complex environment helps to develop decision support systems to support nurse managers' operative and tactical decision-making. Descriptive cross-sectional design. Data were collected from 20 nurse managers with the think-aloud method during the busiest working hours and analysed using thematic content analysis. Nurse managers made over 700 decisions; either ad hoc (n=289), near future (n=268) or long-term (n=187) by nature. Decisions were often made simultaneously with many interruptions. Ad hoc decisions covered staff allocation, ensuring adequate staff, rescheduling surgical procedures, confirmation tangible resources and following-up the daily unit operation. Decisions in the near future were: planning of surgical procedures and tangible resources, and planning staff allocation. Long-term decisions were: human recourses, nursing development, supplies and equipment, and finances in the unit. Decision-making was vulnerable to interruptions, which sometimes complicated the managing tasks. The results can be used when planning decision support systems and when defining the nurse managers' tasks in peri-operative settings.

Improving essential fish habitat designation to support sustainable ecosystem-based fisheries management

A major limitation to fully integrated ecosystem based fishery management approaches is a lack of information on the spatial distribution of marine species and the environmental conditions shaping these distributions. This is particularly problematic for deep-water species that are hard to sample and are data poor. The past decade has seen the rapid development of a suite of advanced species distribution, or ecological niche, modelling approaches developed specifically to support efficient and targeted management. However, model performance can vary significantly and the appropriateness of which methods are best for a given application remains questionable. Species distribution models were developed for three commercially valuable Hawaiian deep-water eteline snappers: Etelis coruscans (Onaga), Etelis carbunculus (Ehu) and Pristipomoides filamentosus (Opakapaka). Distributional data for these species was relatively sparse. To identify the best method, model performance and distributional accuracy was assessed and compared using three approaches: Generalised Additive Models (GAM), Boosted Regression Trees (BRT) and Maximum Entropy (MaxEnt). Independent spatial validation data found MaxEnt consistently provided better model performance with ‘good’ model predictions (AUC =>0.8). Each species was influenced by a unique combination of environmental conditions, with depth, terrain (slope) and substrate (low lying unconsolidated sediments), being the three most important in shaping their distributions. Sustainable fisheries management, marine spatial planning and environmental decision support systems rely on an understanding species distribution patterns and habitat linkages. This study demonstrates that predictive species distribution modelling approaches can be used to accurately model and map sparse species distribution data across marine landscapes. The approach used herein was found to be an accurate tool to delineate species distributions and associated habitat linkages, account for species-specific differences and support sustainable ecosystem-based management.

Comparison of Predictive Models for the Early Diagnosis of Diabetes.

ObjectivesThis study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts.MethodsWe used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC).ResultsThe memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively.ConclusionsThe results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

A Framework for Developing Management Intelligent Systems

This paper proposes a framework for developing management intelligent systems (MiS). The proposed framework identifies the main management functions, intelligent systems and decision support systems (DSS) for planning, organizing, leading and controlling, and their corresponding applications as the core components of MiS. It integrates the main management functions with intelligent systems and DSS in a context of decision making by managers in organizations. This paper also examines intelligent systems for management and management decision making. The approach proposed in this paper might facilitate research and development of MiS, management, intelligent systems, and information systems.

A new model for bridge management: Part B: decision support system for remediation planning

Bridges are fundamental elements in any road network and characterize a major capital asset of community resources. Because of their strategic location over obstacles, any failure may limit and restrict road traffic and cause inconvenience and economic loss. It is therefore important to manage bridge assets, to ensure that all the bridges are maintained in a safe condition, with the most cost-efficient use of resources. Most of the existing bridge management systems are to some extent subjective and based on cost optimization which does not address the indirect impact of the remedial actions. The subjective nature of decision-making in bridge remediation could be replaced by the application of a multi-criteria decision support system that supports decision-makers through balanced consideration of multiple criteria. The main goal of this paper is to present a requirements-driven decision support methodology for remediation of concrete bridges with the aim of maintaining bridge assets within acceptable limits of safety, serviceability and sustainability. The simplified analytical hierarchy process is used as a multi-criteria decision-making technique. A method for selection of the best remediation plan in terms of fund allocation for top-ranked bridges of the network is also proposed using the outputs of the previous procedures considering the budget as the main constraint.

Automated Discovery and Utilization of Tacit Knowledge in Facility Layout Planning and Optimization

This paper puts forth a novel methodology for facilities layout planning and optimization, where the fitness evaluation of layout alternatives is automatically performed by employing an artificial neural network trained to preferences of the domain experts. The inherently uncertain, unstructured, and often tacit nature of facilities layout design preferences, constraints, and fitness objectives demands the use of domain experts for the fitness evaluation of layout alternatives, which is a resource-intensive process. Indeed, the usual unavailability of domain experts in a timely or economical manner highlights the need for resorting to the use of some intelligent and effective automation technique in this important domain. In order to test the key novel ingredient of the proposed approach, a variety of artificial neural networks are trained on a large data set containing both qualitative and quantitative fitness values of layout alternatives, as well as subjective rankings by a seasoned domain expert utilizing the knowledge of the application domain. Simulation results strongly support the viability of the proposed idea. Such an automated approach to fitness evaluations of layout alternatives is expected to significantly increase the efficacy and efficiency of the overall facilities layout planning process. Moreover, such an approach would spur the much sought for research in decision support and expert systems in layout planning. As such, the paper also provides some very interesting and promising, albeit challenging, future research directions.

Delineation of River Basin Committees in Peru using WARPLAM DSS

In order to promote good water governance practices, harmonize multiple objectives, and reflect the linkages between environmental, socioeconomic and politico-administrative aspects, it is imperative to define appropriate regions for water resources planning and management. The Water Resources Planning and Management Decision Support System (WARPLAM DSS) is presented here as a tool to support the definition of suitable limits for water resources regions. Although river basins are generally considered the most suitable regions to achieve Integrated Water Resources Management (IWRM) goals, WARPLAM DSS facilitates the analysis of politico-administrative, historic-cultural, socioeconomic, and physical-environmental aspects. This paper summarizes an initiative to define integrated water resources regions for the establishment of River Basin Committees (RBCs) in Peru, based on a comprehensive analysis of the Peruvian territory. The study was developed through a partnership between ANA-Peru and ANA-Brazil, with the cooperation of the Inter-American Development Bank (IDB). The results simulated in this attempt anticipate a more harmonized division of Peru's territory into water resources regions. Such an approach aims to establish more robust RBCs and to increase the chances of attaining IWRM goals.