Academic literature on the topic 'Decision support systems – Research'

Decision support systems (DSS) are usually based on computerised models of biophysical and economic systems. Despite early expectations that such models would inform and improve management, adoption rates have been low, and implementation of DSS is now “critical” The reasons for this are unclear and the aim of this study is to learn to better design, develop and apply DSS in farming systems research (FSR). Previous studies have explored the merits of quantitative tools including DSS, and suggested changes leading to greater impact. In Australia, the changes advocated have been: Simple, flexible, low cost economic tools: Emphasis on farmer learning through soft systems approaches: Understanding the socio-cultural contexts of using and developing DSS: Farmer and researcher co-learning from simulation modelling and Increasing user participation in DSS design and implementation. Twenty-four simple criteria were distilled from these studies, and their usefulness in guiding the development and application of DSS were assessed in six FSR case studies. The case studies were also used to better understand farmer learning through models of decision making and learning. To make DSS useful complements to farmers’ existing decision-making repertoires, they should be based on: (i) a decision-oriented development process, (ii) identifying a motivated and committed audience, (iii) a thorough understanding of the decision-makers context, (iv) using learning as the yardstick of success, and (v) understanding the contrasts, contradictions and conflicts between researcher and farmer decision cultures<br>Doctor of Philosophy (PhD)

Summary presents self-service networks operational performance improvement and management system, which is adapted to manage the supply of ATMs’ cash flows. The system is created using multi-agent technologies and artificial neural networks. Adaptive neural network model has been created for the forecast of ATMs’ cash demand. Flexibility of the network is regulated in real time mode by limiting neural network weights, depending on process complexity, therefore such network better forecasts in real life situations. Evaluation and process improvement methodology has been created to optimize productivity of self-service systems, which includes: value-based self-service quality and performance criteria’s, and performance evaluation models. Using these models is possible to increase productivity of self-service systems. Using theoretical studies results a computer program enabling real-time monitoring and management of ATM cash flows was created. Analysis of high and low intensity of ATM network profitability showed that the created flexible neural network forecast method is more superior than classical methods of time series forecast (moving average, Holt, Winters, and ARMA), and is able to quite accurately forecast various time series of ATM cash demand. Based on studies found that using the created ANN method and optimization procedure, ATM cash management productivity may be approximately increased by 33 percent.<br>Santraukoje pristatoma savitarnos tinklų veiklos našumo didinimo ir valdymo intelektinė sistema, pritaikyta valdyti bankomatų grynųjų pinigų tiekimo srautus. Sistema sukurta naudojant multi-agentų technologijas ir neuroninius tinklus. Bankomatų grynųjų pinigų poreikiui prognozuoti sudarytas adaptyvus neuroninis tinklas. Šio tinklo lankstumas reguliuojamas realiu laiku apribojant neuroninių tinklų svorius, priklausomai nuo proceso sudėtingumo, todėl toks tinklas gali geriau prognozuoti realiose situacijose. Savitarnos sistemų našumui optimizuoti sukurta vertinimo ir procesų tobulinimo metodika apimanti: verte pagrįstus savitarnos kokybės ir našumo kriterijus bei našumo vertinimo ir procesų tobulinimo modelius. Naudojant šiuos modelius galima užtikrinti našesnį savitarnos sistemų darbą. Teorinių tyrimų pagrindu buvo sukurta kompiuterinė programa, leidžianti realiu laiku stebėti ir valdyti bankomatų grynųjų pinigų srautus. Šios programos panaudojimas leidžia sumažinti tinklo valdymo kaštus ir padidinti paslaugų prieinamumą. Atlikus didelio ir mažo intensyvumo bankomatų tinklų pelningumo analizę nustatyta, kad sukurtas lankstus neuroninių tinklų prognozavimo metodas yra pranašesnis už klasikinius laiko eilučių prognozavimo metodus (slankiųjų vidurkių, Holto, Vinterio, ARMA), ir gali pakankamai tiksliai prognozuoti įvairias bankomatų grynųjų pinigų poreikio laiko eilutes. Remiantis atliktais tyrimais nustatyta, kad pritaikius sukurtą ANN metodą ir optimizavimo procedūrą, bankomatų... [toliau žr. visą tekstą]

Irrigation scheduling using the soil water balance approach has been recommended to irrigators for many years. Reasonably good results are normally obtained by researchers using carefully quantified inputs. Irrigators in production agriculture may estimate inputs and then question the validity of the method when the irrigation recommendations conflict with present irrigation schedules. By associating each input with an interval representing possible bias based on the way the input was estimated, and solving the irrigation scheduling model using the intervals as inputs, the output was associated with an interval representing possible bias. This method was also used to evaluate possible bias associated with growing degree day based crop coefficient curves developed from Arizona crop consumptive use measurements. For comparison purposes, roughly estimated inputs based on irrigation system type, soil type, area weather data and available crop coefficient curves were used as default intervals. Improved input intervals consisted of observed irrigation system performance, soil property measurements, local weather data and theoretical improvements in crop coefficient curves. For surface irrigation, field observation of plant stress and soil water content showed the greatest potential to improve irrigation date predictions. For buried drip under a row crop, accuracy of the predicted daily irrigation rate was most improved by a better estimate of irrigation efficacy.

Past research in the area of Group Decision Support Systems (GDSS) has attempted to ascertain its future potential in the business world. This thesis extends the GDSS research to the area of distributed Group Decision Support Systems (DGDSS) . An experiment was performed where groups of three and four persons were tested in different group decision making settings, a traditional decision room and a DGDSS. An experimental prototype DGDSS program, developed at Claremont University, Claremont, CA, was evaluated during the experiment. The experiment pointed out several program advantages and disadvantages during the evaluation. The study determined that use of the DGDSS resulted in generation of larger numbers of criteria and alternatives and greater decision satisfaction. DGDSS supported groups experienced: less commitment to the final decision, less satisfaction with the decision process, and less satsifaction with communication.

The thesis reports a research programme designed to answer two research questions. These are concerned with improving the practice of Visual Interactive Modelling in the context of an Operational Research activity and the potential contribution of Visual Interactive Modelling towards overcoming certain long term concerns which the Operational Research community has regarding the service it provides. The literature of Visual Interactive Modelling is reviewed, as is that of wider aspects of Operational Research concerned with the paradigm used by its practitioners and researchers. Two series of experimental studies are undertaken to collect data to help answer the research questions. Action Research is used for a series of six studies of Visual Interactive Modelling cases. The second series is more laboratory based to gain a type of data which is not available from Action Research. Results are presented in three forms. A 'body of experience' is collated from the data collected during the studies. This will form a base for future researchers in the Visual Interactive Modelling field. Secondly, a series of guidelines is tentatively proposed which could be used by practitioners as a basis for good practice in Visual Interactive Modelling. Finally, in the light of the data collected and reviews of the literature, a new way of considering the contribution of Visual Interactive Modelling to the Operational Research process is proposed. Suggestions for further research are offered.

<p> Despite being a mandated requirement, the U.S. Government Accountability Office found a lack of guidance across the Department of Defense for conducting analysis of alternatives which contributed to significant cost, schedule, and performance problems for Defense acquisition programs. In 2008 ninety-six major weapon system programs were reviewed and findings showed cost growths of $296 billion, average program delays of twenty-two months, and the delivery of fewer systems with reduced capabilities. Without specific guidance and criteria for how analysis of alternatives should be conducted the Department of Defense will continue to struggle to make informed trade-offs and start executable programs. This praxis presents a decision support system that enables decision makers to analyze cost, schedule, and performance ratings for multi criteria decision analysis problems. The decision support system provides interactive visualization tools that allow decision makers to execute sensitivity and uncertainty analyses, analyze the decision problem from multiple stakeholder-specific viewpoints, and synthesize results in a meaningful way. Although the primary motivation of this praxis is to fill the gap identified by the U.S. Government Accountability Office, the decision support system presented in this praxis can be modified and applied across multiple domains. </p><p>

Thesis (MPA)--University of Stellenbosch, 2004.<br>ENGLISH ABSTRACT: The traditional skills required in government-wide local knowledge, sound political judgment and concern for the welfare of people-are still essential in the global information society. But, to be more effective, these skills now have to be supported by the new decision-making techniques of operations research and decision support systems. The capacity of the human mind to handle complex issues is limited. This situation of complexity and incapacity makes the application of operations research techniques and electronic DSS essential for good governance outcomes. Operations research is a multidisciplinary discipline that requires a team approach to decision making. It is based on systems analysis approach because of its preoccupation with interconnections among parts rather than within the parts themselves. This systems approach allows the optimization of an organization's overall goals, not just those of isolated departments. Optimization is one of the functions of operations research techniques. Linear programming models are most effective at the operational level of decision making with a single objective where scarce or limited resources must be allocated or used in an optimal manner. At the policy level where there are many uncertainties and conflicting objectives, multiobjective programming is more suitable. On the other hand, dynamic programming is flexible and is particularly applied whenever a sequence of decisions must be made and the goal is to find the combination of decisions that optimizes the overall effectiveness of the entire set of decisions. However, when a problem is too complex to be treated by numerical optimization techniques, simulation is used. That is when the problem either cannot be formulated for optimization, because the formulation is too large, there are too many interactions among the variables, or the problem is stochastic (probabilistic) in nature. Despite the analytical power of operations research, many real-world problems are not amenable to direct analytical solution by known mathematical techniques. Hence, in the absence of exact methods to solutions, we usually resort to heuristics, i.e. finding a good but not necessarily the best solution. Other problems encountered by public sector agencies include service stations (waiting lines), inventory levels, forecasting, and project scheduling, which all need decision support systems. To reduce the adverse impact of waiting to acceptable levels one has to minimize costs associated with providing service and those associated with waiting time. For smooth operations, inventory of goods must be kept to an acceptable level to minimize setup or ordering, inventory holding, and shortage (public complaints, and loss of good will and sales) costs. Forecasting is crucial as most managerial decisions are based on projected information and policy analysis is almost always about future outcomes. Many government policies and programs are implemented through projects. Project managers must know how long a specific project will take to finish, what the critical tasks are, and what the probability of completing the project within a given time span is. Successful applications of operations research and decision support systems in the public sector have been recorded including in the areas of the military, transportation, crime and justice, police units, energy, natural resources, facility location, and land use planning. However, operations research applications are not without impediments. Technical and institutional barriers are some of the problems encountered in the effort to apply operations research in the public sector. Similarly, reasons for the slow growth of decision support systems include lack of user demand, lack of system designer motivation, lack of system designer expertise, reluctance to change, and increased risk of failure In the Eritrean public sector, the low level of awareness of operations research and decision support systems is reflected in the inadequacy of addressing multicriteria decision processes, the lack and lor inappropriate selection of decision support systems, improper project management techniques, suboptimal facility locations and service stations, the low level of multidisciplinary approach, and the absence of national standards for pollution control. In general, constraints such as the lack of capacity, awareness, know-how, and software, are rampant. The study concludes that policy-making processes should incorporate opportunities to exercise choices and explore rational options. These rational options are the results of appropriate interface of human, operations research and decision support systems.<br>AFRIKAANSE OPSOMMING: Die tradisionele vaardighede wat van 'n regering verwag word - wye kennis van plaaslike omstandighede, goeie politieke oordeel en besorgdheid oor die welvaart van mense - was nog altyd belangrik in die moderne wêreld. Hierdie vaardighede moet egter ondersteun word deur die nuwe besluitnemingstegnieke van operasionele navorsing en besluitnemings ondersteuningstelsels om effektief te wees. Die vermoë van die menslike brein om komplekse kwessies te hanteer, is beperk. Hierdie situasie van kompleksheid aan die een kant en onvermoë aan die ander kant maak die aanwending van operasionele navorsingstegnieke en elektroniese besluitneming nodig vir goeie regeringsuitkomste. Operasionele navorsing is 'n multidisiplinêre disipline wat 'n spanbenadering tot besluitneming benodig. Dit is baseer op die sisteemanalise benadering omdat dit gaan oor interkonneksies tussen onderdele en nie soseer oor die onderdele self nie. Hierdie sisteembenadering maak die optimisering van die sisteem se oorhoofse doelwitte moontlik, nie net die doelwitte van geïsoleerde departemente nie Optimisasie is een van die funksies van operasionele navorsing. Liniêre programmeringsmodelle is meer effektief op die operasionele vlak van besluitneming met 'n enkel doelwit waar skaars of beperkte bronne toegewys of gebruik moet word op 'n optimale wyse. Op die beleidsvlak waar baie onsekerhede en botsende doelwitte voorkom, is multi-doelwit programmering meer geskik. Aan die ander kant is dinamiese programmering meer toepaslik en buigsaam, veral as dit toegepas word waar 'n reeks besluite geneem moet word en die doel is om 'n kombinasie van besluite te vind wat die oorhoofse effektiwiteit van die hele stel besluite optimiseer. Sekere probleme is egter te kompleks om met numeriese optimisering op te los, omdat die probleem nie geprogrammeer kan word vir optimisering nie, omdat die formulasie te groot is, daar te veel interaksies tussen die veranderlikes is, of die probleem stogasties van aard is. Dan kan simulasies oorweeg word om oplossings te probeer vind. Ten spyte van die analitiese krag van operasionele navorsing, kan baie werklike probleme nie direk deur analitiese wiskundige tegnieke opgelos word nie - altans nie deur bekende wiskundige tegnieke nie. As 'n presiese oplossing nie moontlik is nie, kan kan 'n heuristiese oplossing ondersoek word, d.w.s. 'n goeie, maar nie noodwendig die beste oplossing nie. Ander probleme wat deur die openbare sektor ondervind word, sluit in diensstasies, inventarisvlakke, voorspellings, en projekskedulering. Hulle benodig almal besluitnemingsstelsels vir effektiewe oplossings. Om die wagtydperk te verminder tot 'n aanvaarbare vlak moet die koste verbonde aan die verskaffing van die diens en die koste verbonde aan wagtydperke minirniseer word. Om 'n operasie glad te laat verloop moet die inventaris van goedere op 'n aanvaarbare vlak gehou word om die koste van bestellings, die byhou van voorrade en tekorte (klagtes van die publiek, die verlies aan vertroue en verkope) te minirniseer. Voorspelling is van die uiterste belang vir hierdie doel, omdat bestuursbesluite baseer is op geskatte syfers en beleidsontleding betrekking het op toekomstige uitkomste. Baie regeringsbeleide en -programme word deur projekte geïmplementeer. Projekbestuurders moet weet hoe lank dit sal neem om 'n projek te voltooi, wat die belangrike take is en hoe waarskynlik dit is dat die projek betyds voltooi sal word. Operasionele navorsing en besluitnemingsondersteuning stelsels is al suksesvol aangewend in die volgende openbare sektore: militêre funksies, vervoer, misdaad en justisie, die polisie, energie, natuurlike hulpbronne, en die beplanning van grondgebruik. Tegniese en ander hindernisse word egter soms ondervind by die gebruik van operasionele navorsingstegnieke in die openbare sektor. Redes hoekom die gebruik van sulke stelsels so stadig toeneem, sluit in die gebrek aan aanvraag van verbruikers, die gebrek aan stelselontwerp motivering, die gebrek aan stelselontwerp vaardighede, onwilligheid om te verander en die groter risiko van mislukking. In die openbare sektor van Eritrea word die lae vlak van bewustheid van operasionele navorsing en besluitnemingsondersteuning stelsels gereflekteer in 'n onvermoë om dit te gebruik, die gebrek aan of verkeerde keuse van sulke hulpmiddels, verkeerde bestuurstegnieke, suboptimale plasing van dienspunte, die afwesigheid van multi-disiplinêre benaderings, en die afwesigheid van nasionale standaarde vir die beheer van besoedeling. Beperkings soos 'n gebrek aan kapasiteit, bewustheid, kennis en sagteware kom algemeen voor. In hierdie studie word daar tot die gevolgtrekking gekom dat beleidmakende prosesse die geleentheid behoort in te sluit om keuses te maak en om verskillende opsies te toets. Hierdie rasionele opsies is die gevolg van die regte interaksie tussen die mens, operasionele navorsing en besluitnemingsondersteuning stelsels.