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Journal articles on the topic 'Problem-solving system'

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Published: 10 December 2022

Last updated: 28 January 2023

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1

Muliarevych, Oleksandr. "Cyber-Physical System for Solving Travelling Salesman Problem." Advances in Cyber-Physical Systems 2, no. 1 (March 28, 2017): 22–28. http://dx.doi.org/10.23939/acps2017.01.022.

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2

Rodriguez-Ulloa, Ricardo A. "The problem-solving system: Another problem-content system." Systems Practice 1, no. 3 (September 1988): 243–57. http://dx.doi.org/10.1007/bf01062923.

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3

WANG, PEI. "PROBLEM SOLVING WITH INSUFFICIENT RESOURCES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 05 (October 2004): 673–700. http://dx.doi.org/10.1142/s0218488504003144.

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A new approach, "controlled concurrency," is introduced for inference control in an adaptive reasoning system working with insufficient knowledge and resources. With this method, a problem-solving process is constructed from atomic steps in run time, according to the system's past experience and the current context. The system carries out many such processes in parallel by distributing its resources among them, and dynamically adjusting the distribution according to feedback. A data structure, "bag," is designed to support this dynamic time-space allocation, and is a kind of probabilistic priority queue. This approach provides a flexible, efficient, and adaptive control mechanism for real-time systems working with uncertain knowledge. To analyze problem solving in such a system, the traditional computability theory and computational complexity theory become inappropriate, because the system no longer follows problem-specific algorithms in problem solving.

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4

SHIMOSAKA, Hisashi, Tomoyuki HIROYASU, and Mitsunori MIKI. "Optimization Problem Solving System using GridRPC." Transactions of the Japan Society of Mechanical Engineers Series C 72, no. 716 (2006): 1207–14. http://dx.doi.org/10.1299/kikaic.72.1207.

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5

SHIMOSAKA, Hisashi, Tomoyuki HIROYASU, and Mitsunori MIKI. "Optimization Problem Solving System using GridRPC." Proceedings of Design & Systems Conference 2003.13 (2003): 92–95. http://dx.doi.org/10.1299/jsmedsd.2003.13.92.

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6

Winzer, Petra. "Generic System Description and Problem Solving in Systems Engineering." IEEE Systems Journal 11, no. 4 (December 2017): 2052–61. http://dx.doi.org/10.1109/jsyst.2015.2428811.

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7

Keys, Paul. "System dynamics as a systems-based problem-solving methodology." Systems Practice 3, no. 5 (October 1990): 479–93. http://dx.doi.org/10.1007/bf01064156.

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8

Still, Jospeh. "Condominium noise issues: Problem solving with flooring systems and solving flooring system problems." Journal of the Acoustical Society of America 119, no. 5 (May 2006): 3220. http://dx.doi.org/10.1121/1.4785919.

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9

Hudlicka, Eva, and Victor Lesser. "Modeling and Diagnosing Problem-Solving System Behavior." IEEE Transactions on Systems, Man, and Cybernetics 17, no. 3 (1987): 407–19. http://dx.doi.org/10.1109/tsmc.1987.4309057.

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10

Applegate, B., T. Fernandez, and D. Sarker. "Analogical problem solving in an expert system." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 5 (1992): 1138–44. http://dx.doi.org/10.1109/21.179851.

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11

Tchounikine, Pierre. "Modelling Problem-solving for an Educational System." Intelligent Tutoring Media 7, no. 3-4 (January 1997): 83–96. http://dx.doi.org/10.1080/14626269709408378.

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12

SHIMOSAKA, Hisashi, Tomoyuki HIROYASU, and Mitsunori MIKI. "Optimization Problem Solving System based on OGSA." Proceedings of OPTIS 2004.6 (2004): 203–8. http://dx.doi.org/10.1299/jsmeoptis.2004.6.203.

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13

Papa, Frank J. "An emergency medicine clinical problem-solving system." Annals of Emergency Medicine 14, no. 7 (July 1985): 660–63. http://dx.doi.org/10.1016/s0196-0644(85)80883-0.

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14

Lepley, Cyndi J. "Problem-Solving Tools for Analyzing System Problems." JONA: The Journal of Nursing Administration 28, no. 12 (December 1998): 44–50. http://dx.doi.org/10.1097/00005110-199812000-00014.

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15

Schwartz, W. "Using the computer-assisted medical problem-solving (CAMPS) system to identify studentsʼ problem-solving difficulties." Academic Medicine 67, no. 9 (September 1992): 568–71. http://dx.doi.org/10.1097/00001888-199209000-00004.

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16

Meyer, Thomas H., and Ahmed F. Elaksher. "Solving the Multilateration Problem without Iteration." Geomatics 1, no. 3 (June 29, 2021): 324–34. http://dx.doi.org/10.3390/geomatics1030018.

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The process of positioning, using only distances from control stations, is called trilateration (or multilateration if the problem is over-determined). The observation equation is Pythagoras’s formula, in terms of the summed squares of coordinate differences and, thus, is nonlinear. There is one observation equation for each control station, at a minimum, which produces a system of simultaneous equations to solve. Over-determined nonlinear systems of simultaneous equations are typically solved using iterative least squares after forming the system as a truncated Taylor’s series, omitting the nonlinear terms. This paper provides a linearization of the observation equation that is not a truncated infinite series—it is exact—and, thus, is solved exactly, with full rigor, without iteration and, thus, without the need of first providing approximate coordinates to seed the iteration. However, there is a cost of requiring an additional observation beyond that required by the non-linear approach. The examples and terminology come from terrestrial land surveying, but the method is fully general: it works for, say, radio beacon positioning, as well. The approach can use slope distances directly, which avoids the possible errors introduced by atmospheric refraction into the zenith-angle observations needed to provide horizontal distances. The formulas are derived for two- and three-dimensional cases and illustrated with an example using total-station and global navigation satellite system (GNSS) data.

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17

Irfani, Dian Prama, Dermawan Wibisono, and Mursyid Hasan Basri. "Integrating performance measurement, system dynamics, and problem-solving methods." International Journal of Productivity and Performance Management 69, no. 5 (October 22, 2019): 939–61. http://dx.doi.org/10.1108/ijppm-12-2018-0456.

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Purpose Transport logistics systems in companies with additional public service roles are complex and could benefit from new approaches to performance management. Existing approaches tend to be fragmented; thus, the purpose of this paper is to integrate balanced performance measures, a dynamics model, and the problem-solving method into a new model. Design/methodology/approach An integrated framework is developed by reviewing literature and synthesising attributes of performance measurement systems, system dynamics and problem-solving methods. The framework is then applied to a multiple-role company’s sea transportation system. The study uses statistical methods to identify performance indicators, management interviews with document study to develop a dynamics model, and simulation methods to formulate an improvement plan. Findings The performance measurement design stage allowed for the identification of balanced, aligned performance indicators, while the system dynamics model illuminated the impact of the system components’ interrelationships on performance output. The problem-solving method allowed for analysis of system performance, identification of constraints and formulation of a performance improvement plan. Practical implications This framework can help transport logistics system stakeholders in multiple-role companies avoid silo thinking, misaligned performance objectives, local optima and short-term solutions. Originality/value This study contributes to the existing body of research by introducing a novel framework integrating performance measurement, system dynamics and the problem-solving method. It also addresses a theoretical gap by showing how interconnecting components of sea transportation systems affect transport logistics performance.

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18

Lin, Chia-Yi, and Seokhee Cho. "Predicting Creative Problem-Solving in Math From a Dynamic System Model of Creative Problem Solving Ability." Creativity Research Journal 23, no. 3 (July 2011): 255–61. http://dx.doi.org/10.1080/10400419.2011.595986.

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19

Hoang, Dinh Tuyen, Ngoc Thanh Nguyen, Botambu Collins, and Dosam Hwang. "Decision Support System for Solving Reviewer Assignment Problem." Cybernetics and Systems 52, no. 5 (January 20, 2021): 379–97. http://dx.doi.org/10.1080/01969722.2020.1871227.

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20

Monahan, Brian D., and Sandra E. Belkin. "Expert-System Software and Knowledge-Intensive Problem Solving." Thought 61, no. 4 (1986): 497–507. http://dx.doi.org/10.5840/thought198661426.

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21

Kato, Naotaka, and Susumu Kunifuji. "Consensus-making support system for creative problem solving." Knowledge-Based Systems 10, no. 1 (June 1997): 59–66. http://dx.doi.org/10.1016/s0950-7051(97)00014-2.

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22

Schmalhofer, F., and P. G. Polson. "A production system model for human problem solving." Psychological Research 48, no. 2 (August 1986): 113–22. http://dx.doi.org/10.1007/bf00309325.

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23

Hatzi, Ourania, Dimitris Vrakas, Nick Bassiliades, Dimosthenis Anagnostopoulos, and Ioannis Vlahavas. "A visual programming system for automated problem solving." Expert Systems with Applications 37, no. 6 (June 2010): 4611–25. http://dx.doi.org/10.1016/j.eswa.2009.12.047.

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24

Winne, Philip H., John C. Nesbit, and Fred Popowich. "nStudy: A System for Researching Information Problem Solving." Technology, Knowledge and Learning 22, no. 3 (July 22, 2017): 369–76. http://dx.doi.org/10.1007/s10758-017-9327-y.

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25

Sheremetov, Leonid, Ildar Batyrshin, Denis Filatov, Jorge Martinez, and Hector Rodriguez. "Fuzzy expert system for solving lost circulation problem." Applied Soft Computing 8, no. 1 (January 2008): 14–29. http://dx.doi.org/10.1016/j.asoc.2006.11.003.

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26

Wong, Jsun Y. "A note on solving a system reliability problem." Microelectronics Reliability 33, no. 7 (May 1993): 1045–51. http://dx.doi.org/10.1016/0026-2714(93)90301-e.

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27

Lippert, Renate C. "An expert system shell to teach problem solving." TechTrends 33, no. 2 (March 1988): 22–26. http://dx.doi.org/10.1007/bf02771224.

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28

K Rakesh, Shanu, Bharat Choudhary, and Rachna Sandhu. "Cooperative Problem Solving in Telecommunication Network." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 4, no. 3 (July 24, 2013): 388–92. http://dx.doi.org/10.24297/ijmit.v4i3.4548.

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Swarm intelligence, as demonstrated by natural biological swarms, has numerous powerful properties desirable in many engineering systems, such as telecommunication. Communication network management is becoming increasingly difficult due to the increasing size, rapidly changing topology, and complexity of communication networks. This paper describes how biologically-inspired agents can be used to solve control problems in telecommunications. These agents, inspired by the foraging behaviour of ants, exhibit the desirable characteristics of simplicity of action and interaction. The colle ction of agents, or swarm system, deals only with local knowledge and exhibits a form of distributed control with agent communication effected through the environment. In this paper we explore the application of ant-like agents to the problem of routing in telecommunication networks.

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29

Lewis, C. Michael, and Wesley Jamison. "Problem Solving in Naive Users." Proceedings of the Human Factors Society Annual Meeting 33, no. 5 (October 1989): 418–22. http://dx.doi.org/10.1177/154193128903300540.

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Unix Tutor is a menu interface to UNIX being developed at the University of Pittsburgh as a training aid for new users. This paper compares mental models currently supported by the interface and those used by novices by examining subject logs from experiments. The paper concludes that Unix Tutor provides good support for consistent aspects of the operating system but fails to support models novices need to deal with inconsistencies. Design enhancements are suggested for resolving this problem.

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30

McArthur, David, Cathleen Stasz, and John Y. Hotta. "Learning Problem-Solving Skills in Algebra." Journal of Educational Technology Systems 15, no. 3 (March 1987): 303–24. http://dx.doi.org/10.2190/pe6f-2upe-g03r-xeuk.

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In this article we describe aspects of our intelligent tutor for basic algebra. A main goal of the project is to develop a computer tutoring system whose skills and knowledge approximate those of a high-quality human tutor. We are particularly interested in exploring novel learning opportunities that can be made available to students for the first time by exploiting the reactive capabilities of such intelligent tutors. In this context, we focus here on the role of an algebra expert system embedded in the tutor. We discuss how it can be used to help students learn several nontraditional types of skill and knowledge in the context of algebra, including goal-directed reasoning skills, and debugging techniques.

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31

Kaklauskas, Liudvikas, Leonidas Sakalauskas, and Vitalijus Denisovas. "Stalling for solving slow server problem." RAIRO - Operations Research 53, no. 4 (July 29, 2019): 1097–107. http://dx.doi.org/10.1051/ro/2018056.

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The model of stalling in queueing system (QS) with two heterogeneous severs is considered, the probabilities of steady states by means of Tchebyshev polynomials of second order are derived. The obtained expressions are stable numerically, their complexity does not depend on the number of states, and they enable us to study QS characteristics analytically. Optimization of a stalling buffer is considered as well and it was shown that stalling helps us to solve the slow server problems under an appropriate choice of stalling buffer size, making a slow server usable under various values of system load. Asymptotic conditions of optimal query distribution in servers are established, when the ratio of capacities of fast and slow channels is increasing. Application of the model developed in computer networks is discussed as well.

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32

Cramer, Claes. "Problem-solving a lesson from relativity in physics education." Physics Education 57, no. 4 (April 12, 2022): 045010. http://dx.doi.org/10.1088/1361-6552/ac5641.

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Abstract The principle of relativity is of fundamental importance in practice when we are solving problems in physics since the axiom states that the result of any physical experiment is the same when performed with identical initial conditions relative to any inertial coordinate system. Hence, conceptual knowledge of coordinate systems is central in any physics problem-solving framework. This opens up the question whether upper secondary school students should be taught to set up coordinate systems and apply basic coordinate transformations systematically when solving problems or if it should be left for physics courses at a college or university level? Given that working with coordinate systems does not require any advanced algebra or analysis knowledge, a case can be made for introducing the concepts of coordinate systems and transformations together with a uniform problem-solving framework. The purpose here, given that coordinate systems are central in problem-solving, is to revisit the method of drawing free body diagrams in engineering mechanics but adapted for upper secondary school physics. This method relies on the existence of a coordinate system and can be described by an activity diagram—a connected sequence of actions and decisions. The activity diagram is a map for problem-solving. The central actions in the activity are to introduce a coordinate system, draw a free body diagram and decide if the system should be transformed besides actually working out any algebra and numerical calculations. Here the notion of ‘transformation’ should be read as a graphical transformation rather than an algebraic group operation that is not suitable, neither to teach, nor to use at upper secondary level. A basic example is provided to illustrate why the methodology simplifies the problem-solving process and the student’s understanding of the subject.

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33

Glebkin, V. V., A. Ye Kovtunenko, and Ye A. Krysova. "Counterfactual Problem Solving and Situated Cognition." Cultural-Historical Psychology 13, no. 2 (2017): 41–49. http://dx.doi.org/10.17759/chp.2017130205.

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The paper describes and interprets data of a study on counterfactual problem solving in representatives of modern industrial culture. The study was inspired by similar experiments carried out by A.R. Luria during his expedition to Central Asia. The hypothesis of our study was that representatives of modern industrial culture would solve counterfactual puzzles at a slower rate and with higher numbers of mistakes than similar non-counterfactual tasks. The experiments we conducted supported this hypothesis as well as provided us with some insights as to how to further develop it. For instance, we found no significant differences in time lag in solving counterfactual and ‘realistic’ tasks between the subjects with mathematical and the ones with liberal arts education. As an interpretation of the obtained data, we suggest a two-stage model of counterfactual problem solving: on the first stage, where situated cognition dominates, the realistic situation is transferred into the system of symbols unrelated to this very situation; on the second stage, operations are carried out within the framework of this new system of symbols.

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34

Delima, Nita. "A RELATIONSHIP BETWEEN PROBLEM SOLVING ABILITY AND STUDENTS’ MATHEMATICAL THINKING." Infinity Journal 6, no. 1 (January 29, 2017): 21. http://dx.doi.org/10.22460/infinity.v6i1.231.

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This research have a purpose to know is there an influence of problem solving abilty to students mathematical thinking, and to know how strong problem solving ability affect students mathematical thinking. This research used descriptive quantitative method, which a population is all of students that taking discrete mathematics courses both in department of Information Systems and department of mathematics education. Based on the results of data analysis showed that there are an influence of problem solving ability to students mathematical thinking either at department of mathematics education or at department of information systems. In this study, it was found that the influence of problem solving ability to students mathematical thinking which take place at mathematics education department is stonger than at information system department. This is because, at mathematics education department, problem-solving activities more often performed in courses than at department of information system. Almost 75% of existing courses in department of mathematics education involve problem solving to the objective of courses, meanwhile, in the department of information systems, there are only 10% of these courses. As a result, mathematics education department student’s are better trained in problem solving than information system department students. So, to improve students’ mathematical thinking, its would be better, at fisrtly enhance the problem solving ability.

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35

Delima, Nita. "A RELATIONSHIP BETWEEN PROBLEM SOLVING ABILITY AND STUDENTS’ MATHEMATICAL THINKING." Infinity Journal 6, no. 1 (January 29, 2017): 21. http://dx.doi.org/10.22460/infinity.v6i1.p21-28.

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This research have a purpose to know is there an influence of problem solving abilty to students mathematical thinking, and to know how strong problem solving ability affect students mathematical thinking. This research used descriptive quantitative method, which a population is all of students that taking discrete mathematics courses both in department of Information Systems and department of mathematics education. Based on the results of data analysis showed that there are an influence of problem solving ability to students mathematical thinking either at department of mathematics education or at department of information systems. In this study, it was found that the influence of problem solving ability to students mathematical thinking which take place at mathematics education department is stonger than at information system department. This is because, at mathematics education department, problem-solving activities more often performed in courses than at department of information system. Almost 75% of existing courses in department of mathematics education involve problem solving to the objective of courses, meanwhile, in the department of information systems, there are only 10% of these courses. As a result, mathematics education department student’s are better trained in problem solving than information system department students. So, to improve students’ mathematical thinking, its would be better, at fisrtly enhance the problem solving ability.

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36

Gunawan, Supriatna, Eka Setyaningsih, and Rizki Fera Apriana. "Mathematics problem solving on linear system of two variables." Journal of Physics: Conference Series 1778, no. 1 (February 1, 2021): 012027. http://dx.doi.org/10.1088/1742-6596/1778/1/012027.

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37

S. Babaeizadeh, S. Babaeizadeh. "Solving Optimal Control Problem Using Max-Min Ant System." IOSR Journal of Mathematics 1, no. 3 (2012): 47–51. http://dx.doi.org/10.9790/5728-0134751.

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38

Matis, Peter. "DECISION SUPPORT SYSTEM FOR SOLVING THE STREET ROUTING PROBLEM." TRANSPORT 23, no. 3 (September 30, 2008): 230–35. http://dx.doi.org/10.3846/1648-4142.2008.23.230-235.

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Servicing a large number of customers in a city zone is often a considerable part of many logistics chains. The capacity of one delivery vehicle is limited, but, at the same time, it usually serves plenty of customers. This problem is often called a Street Routing Problem (SRP). Key differences between Vehicle Routing Problem (VRP) and SRP are presented here. The main problem of SRP is that when the number of customers is huge, the number of delivery path combinations becomes enormous. As the experimental results show in the case of SRP the error on the length of delivery routes based on an expert's judgment when compared to the optimal solution is in the range of 10–25%. As presented in the paper, only using decision support systems such as Geographical Information Systems (GIS) makes possible to effectively manage SRP. Besides classical measurements used in VRP, such as total length of routes or time required for delivery in each route, other measurements, mostly qualitative ones, are presented. All of these are named as visual attractiveness. This paper discusses possible relationships between quantitative and qualitative measurements that give a promise for finding better solutions of SRP. Several new types of heuristics for solving SRP are evaluated and afterward compared using the real data. One of the key properties of GIS to use routing software is its flexible interactive and user‐friendly environment. Routing software can find a good solution and explore the possibilities while an expert later can change the calculated routes to explore other possibilities based on the expert's judgment. This paper presents a practical use of new heuristics with the ArcView and solution of address mail for several cities in Slovakia served by Slovak Post ltd. Other Decision Support Systems that solve SRP are presented as TRANSCAD developed by Caliper Corporation or GeoRoute promoted by Canadian Post and GIRO.

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39

Fink, Pamela K., John C. Lusth, and Joe W. Duran. "A General Expert System Design for Diagnostic Problem Solving." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7, no. 5 (September 1985): 553–60. http://dx.doi.org/10.1109/tpami.1985.4767702.

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40

Shaw, M. L. G. "An interactive knowledge-based system for group problem solving." IEEE Transactions on Systems, Man, and Cybernetics 18, no. 4 (1988): 610–17. http://dx.doi.org/10.1109/21.17379.

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41

Mamrak, S. A., M. S. Kaelbling, C. K. Nicholas, and M. Share. "Chameleon: a system for solving the data-translation problem." IEEE Transactions on Software Engineering 15, no. 9 (1989): 1090–108. http://dx.doi.org/10.1109/32.31367.

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42

Vasin, Yu G., and A. N. Perepelkin. "An interactive problem-solving system with levelwise data access." Pattern Recognition and Image Analysis 20, no. 4 (December 2010): 528–35. http://dx.doi.org/10.1134/s1054661810040139.

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43

Zhou, Xinlin, Mengyi Li, Leinian Li, Yiyun Zhang, Jiaxin Cui, Jie Liu, and Chuansheng Chen. "The semantic system is involved in mathematical problem solving." NeuroImage 166 (February 2018): 360–70. http://dx.doi.org/10.1016/j.neuroimage.2017.11.017.

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44

Hernando, Antonio, Luis De Ledesma, and Luis M. Laita. "A system simulating representation change phenomena while problem solving." Mathematics and Computers in Simulation 78, no. 1 (June 2008): 89–106. http://dx.doi.org/10.1016/j.matcom.2007.06.009.

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45

Mohd Tahar, Siti Hajar, Shamshul Bahar Yaakob, Ahmad Shukri Fazil Rahman, and Amran Ahmed. "Solving financial allocation problem in distribution system expansion planning." Bulletin of Electrical Engineering and Informatics 8, no. 1 (March 1, 2019): 320–27. http://dx.doi.org/10.11591/eei.v8i1.1445.

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This paper introduces a new technique to solve financial allocation in Distribution System Expansion Planning (DSEP) problem. The proposed technique will be formulated by using mean-variance analysis (MVA) approach in the form of mixed-integer programming (MIP) problem. It consist the hybridization of Hopfield Neural Network (HNN) and Boltzmann Machine (BM) in first and second phase respectively. During the execution at the first phase, this model will select the feasible units meanwhile the second phase will restructured until it finds the best solution from all the feasible solution. Due to this feature, the proposed model has a fast convergence and the accuracy of the obtained solution. This model can help planners in decision-making process since the solutions provide a better allocation of limited financial resources and offer the planners with the flexibility to apply different options to increase the profit.

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46

Shadpour, Atefeh, Andre J. A. Unger, Mark A. Knight, and Carl T. Haas. "Numerical DAE Approach for Solving a System Dynamics Problem." Journal of Computing in Civil Engineering 29, no. 3 (May 2015): 04014054. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000349.

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47

Ohsuga, Setsuo. "Intelligent problem-solving system based on model building method." Systems and Computers in Japan 18, no. 6 (1987): 71–88. http://dx.doi.org/10.1002/scj.4690180607.

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48

Pourgholi, Reza, S. Hashem Tabasi, and Hamed Zeidabadi. "Numerical techniques for solving system of nonlinear inverse problem." Engineering with Computers 34, no. 3 (November 25, 2017): 487–502. http://dx.doi.org/10.1007/s00366-017-0554-6.

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49

Chen, Xiao-hong, Yan-ju Zhou, and Dong-bin Hu. "A problem solving framework for group decision support system." Journal of Central South University of Technology 9, no. 4 (December 2002): 279–84. http://dx.doi.org/10.1007/s11771-002-0042-y.

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50

Soh, Chee-Kiong, A. K. Soh, and Kum-Yew Lai. "A simple knowledge system environment for engineering problem solving." Computing Systems in Engineering 6, no. 6 (December 1995): 485–96. http://dx.doi.org/10.1016/0956-0521(95)00051-8.

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