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Статті в журналах з теми "Operations research-Models"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Smith, David K., and Katta G. Murty. "Operations Research: Deterministic Optimization Models." Journal of the Operational Research Society 46, no. 9 (September 1995): 1154. http://dx.doi.org/10.2307/2584503.

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2

Smith, David K. "Operations Research: Deterministic Optimization Models." Journal of the Operational Research Society 46, no. 9 (September 1995): 1154–55. http://dx.doi.org/10.1057/jors.1995.160.

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3

Goldsman, David. "Operations Research Models and Methods." IIE Transactions 35, no. 9 (September 2003): 920. http://dx.doi.org/10.1080/07408170304414.

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4

Jándy, Géza. "PROBLEMS OF LOCATION IN OPERATIONS RESEARCH MODELS." Papers in Regional Science 22, no. 1 (January 14, 2005): 195–202. http://dx.doi.org/10.1111/j.1435-5597.1969.tb01461.x.

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5

Karabuk, Suleyman, and F. Hank Grant. "A Common Medium for Programming Operations-Research Models." IEEE Software 24, no. 5 (September 2007): 39–47. http://dx.doi.org/10.1109/ms.2007.125.

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6

Kreines, E. M., N. M. Novikova, and I. I. Pospelova. "Multicriteria Competitive Games as Models in Operations Research." Computational Mathematics and Mathematical Physics 60, no. 9 (September 2020): 1570–87. http://dx.doi.org/10.1134/s0965542520090122.

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7

Stark, Robert F., Emilie M. Roth, and Michael P. Farry. "Incrementally Formalizing Graphical Models for Collaborative Operations Research." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, no. 1 (September 2013): 230–34. http://dx.doi.org/10.1177/1541931213571051.

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8

Guajardo, Mario, and Mikael Rönnqvist. "Operations research models for coalition structure in collaborative logistics." European Journal of Operational Research 240, no. 1 (January 2015): 147–59. http://dx.doi.org/10.1016/j.ejor.2014.06.015.

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9

Bertolazzi, Paola, Jacek Blazewicz, and Metin Turkay. "Operations Research Models for Computational Biology, Bioinformatics and Medicine." Journal of Mathematical Modelling and Algorithms 9, no. 3 (June 30, 2010): 209–11. http://dx.doi.org/10.1007/s10852-010-9135-z.

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10

Bleed, Peter. "Operations Research and Archaeology." American Antiquity 56, no. 1 (January 1991): 19–35. http://dx.doi.org/10.2307/280969.

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Анотація:

Operations research is the systematic study of resource utilization through the manipulation of conceptual models. Used most often by engineers and managers, these models also have potential application in archaeology. Event tree analysis, which models multipart systems with a number of potential consequences resulting from a series of events, makes it possible to analyze artifact-production processes or the operation of complex economic activities such as hunting. Fault tree analysis models the interactions of factors that can cause a technological system to fail. It offers a framework for the interpretation of archaeological remains such as flintlock firearm parts at Indian and Euroamerican sites of the Great Plains.

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11

Savsar, Mehmet. "Analysis and Scheduling of Maintenance Operations for a Chain of Gas Stations." Journal of Industrial Engineering 2013 (March 10, 2013): 1–7. http://dx.doi.org/10.1155/2013/278546.

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Maintenance is one of the central issues in operational activities, which involve any type of equipment. In this paper we have considered analysis, modeling, and scheduling of preventive maintenance operations for fuel dispensers in a chain of gas stations. A gas station company with more than 570 dispensers in more than 40 stations is considered and the maintenance problem is studied in detail. Operations research tools, including maintenance models and linear programming, were used to establish optimum schedules for preventive maintenance operations. Detailed cost analyses were carried out to determine feasibility of the proposed preventive maintenance schedules. Models and procedures presented in this paper could guide operation engineers and maintenance managers in solving similar problems for operational improvements.

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12

Monjezi, Masoud, Farhad Farzaneh, and Ahmad Asadi. "Evaluation of Blasting Patterns Using Operational Research Models / Ocena Planów Prac Strzałowych W Oparciu O Metody Badań Operacyjnych." Archives of Mining Sciences 58, no. 3 (September 1, 2013): 881–92. http://dx.doi.org/10.2478/amsc-2013-0061.

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Abstract Blasting is one of the most important operations, which has a great technical and economical effect on the mining projects. Criteria such as fragmentation (operation ultimate objective) and ground vibration, flyrock, airblast, etc. (operation side effects) should be considered in the assessment of blasting operation. A suitable pattern should be able to provide both reasonable (required) fragmentation and blasting side effects. In order to evaluate blasting performance, operational research models such as multi attribute decision making technique (MADM) can be applied. Technique for order preference by similarity to an ideal solution (TOPSIS), a branch of MADM, is a strong method for pattern ranking. The other quantitative method which is applied in the evaluation of systems’ efficiency is data envelopment analysis (DEA) model. In this paper, an attempt has been made to develop a new hybrid MADM model for selecting the most appropriate blasting pattern in Chadormalu iron mine, Iran. In this regard, DEA method was utilized to select the efficient blast patterns thereafter TOPSIS was used to recognize the most suitable pattern amongst the selected patterns by DEA method. It was concluded that the patterns J, G and B are the most appropriate patterns for blasting operations in the Chadormalu iron mine.

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13

Fukuyama, Hirofumi, and William L. Weber. "OUTPUT SLACKS-ADJUSTED COST EFFICIENCY AND VALUE-BASED TECHNICAL EFFICIENCY IN DEA MODELS(Operations Research for Performance Evaluation)." Journal of the Operations Research Society of Japan 52, no. 2 (2009): 86–104. http://dx.doi.org/10.15807/jorsj.52.86.

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14

Hampu, Alexandru. "Considerations Regarding the Valence and Diversity of Operations Research Models." International conference KNOWLEDGE-BASED ORGANIZATION 26, no. 3 (June 1, 2020): 108–13. http://dx.doi.org/10.2478/kbo-2020-0123.

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Анотація:

AbstractOperations research(OR), also called decision optimization science, is the one that provides mathematical models meant to lead to finding the optimal variant in an economic, military, social, political, etc. type of activity. These are methods with a major potential, highlighted since the emergence of the models used in World War II actions and later in various other fields, especially in western countries. This paper aims to highlight the benefits of using OR techniques over time and especially to highlight its applicability. In this paper we summarize the presentation of the evolution and contributions of Operational Research in various fields until the ‘60s, a future paper will deal with the time frame 1960-2020.

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15

Wilson, John M. "Classification of Models in Operational Research." Journal of the Operational Research Society 36, no. 3 (March 1985): 253. http://dx.doi.org/10.2307/2582257.

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16

Wilson, John M. "Classification of Models in Operational Research." Journal of the Operational Research Society 36, no. 3 (March 1985): 253–56. http://dx.doi.org/10.1057/jors.1985.44.

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17

Dumičić, Ksenija, Lidija Zadnik Stirn, and Janez Žerovnik. "Editorial for the special issue: “Novel solutions or novel approaches in Operational Research”." Business Systems Research Journal 7, no. 2 (September 1, 2016): 1–4. http://dx.doi.org/10.1515/bsrj-2016-0016.

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Abstract The special issue of Business Systems Research (SI of BSR) is co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI – SOR) and contains developments and new techniques in operations research (OR) models and their practical uses in various fields of business and economics. Seven papers that investigate developments and new techniques in operations research (OR) models and their practical uses in various fields of business and economics have been selected for this SI of BSR.

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18

Ben-Ayed, Omar. "Three different operations research models for the same (s,S) policy." Journal of Applied Mathematics and Decision Sciences 5, no. 1 (January 1, 2001): 47–59. http://dx.doi.org/10.1155/s1173912601000049.

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Анотація:

Operations Research techniques are usually presented as distinct models. Difficult as it may often be, achieving linkage between these models could reveal their interdependency and make them easier for the user to understand. In this article three different models, namely Markov Chain, Dynamic Programming, and Markov Sequential Decision Processes, are used to solve an inventory problem based on the periodic review system. We show how the three models converge to the same (s,S) policy and we provide a numerical example to illustrate such a convergence.

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19

Gonçalves, João N. C., M. Sameiro Carvalho, and Paulo Cortez. "Operations research models and methods for safety stock determination: A review." Operations Research Perspectives 7 (2020): 100164. http://dx.doi.org/10.1016/j.orp.2020.100164.

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20

Shapiro, Joel A., Warren B. Powell, and David Bernstein. "A Flexible Java Representation for Uncertainty in Online Operations-Research Models." INFORMS Journal on Computing 13, no. 1 (February 2001): 29–55. http://dx.doi.org/10.1287/ijoc.13.1.29.9749.

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21

Wright, P. Daniel, Matthew J. Liberatore, and Robert L. Nydick. "A Survey of Operations Research Models and Applications in Homeland Security." Interfaces 36, no. 6 (December 2006): 514–29. http://dx.doi.org/10.1287/inte.1060.0253.

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22

Ben-Ayed, Omar. "Three Different Operations Research Models for the Same (s; S) Policy." Journal of Applied Mathematics and Decision Sciences 5, no. 1 (March 1, 2001): 47–59. http://dx.doi.org/10.1207/s15327612jamds0501_4.

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23

Dekker, Rommert. "On the use of operations research models for maintenance decision making." Microelectronics Reliability 35, no. 9-10 (September 1995): 1321–31. http://dx.doi.org/10.1016/0026-2714(95)99380-2.

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24

Pintelon, Liliane. "Handbooks in operations research and management science volume 2: Stochastic models." European Journal of Operational Research 50, no. 1 (January 1991): 97. http://dx.doi.org/10.1016/0377-2217(91)90047-y.

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25

Pfeifer, d. "Annals of operations research, vol. 32: Stochastic models and their applications." European Journal of Operational Research 62, no. 2 (October 1992): 253–55. http://dx.doi.org/10.1016/0377-2217(92)90254-7.

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26

Zhen, Lu. "Preface: Special Issue on Operations Research Models and Algorithms in Transportation." Journal of the Operations Research Society of China 8, no. 3 (August 27, 2020): 353–55. http://dx.doi.org/10.1007/s40305-020-00321-w.

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27

Guzmán Paredes, Carlos Manuel, Agustín Gallegos Rodríguez, Efrén Hernández-Álvarez, and Héctor Jesús Contreras Quiñones. "Investigación de operaciones para desarrollar procesos de optimización en manejo forestal: determinación en campo de modelos multivariados." e-CUCBA 9, no. 17 (December 29, 2021): 115–23. http://dx.doi.org/10.32870/ecucba.vi17.216.

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Анотація:

The most widely used technique in operations research is linear programming, and one of the most widely used algorithms in thisfield is simplex method. The management of forest ecosystems is an ideal field to implement optimization models, since clear goalsmust normally be met, and the available resources are usually limited. The aspects involved are complex, several routes of actioncan be taken, and it is important in order to make the best decision. The simplex method, once objective function and restrictions areestablished, allows to consider all the possibilities, and make the decision that provides the greatest advantages. Undoubtedly, awrong decision can cause damage to forest ecosystem and economic losses, so a tool that helps in this work will always be a greatally. Once the problem has been formulated in mathematical terms, software or programming languages specialized are usuallyrequired, which can be a problem if access to computer equipment and internet connectivity is not the most appropriate. Mobiledevices such as cell phones, tablets, and programmable calculators have become small computers at an affordable price, withincreasingly powerful processors and a good amount of memory. This, coupled with availability of applications of simplex method,or even the ability to program it directly on these devices, makes it possible to have in the palm of hand tools that previouslyrequired very sophisticated computers. This work reviews the options currently available to run simplex method, both analyticallyand graphically, on portable devices. This topic is of interest to both professional and student of forestry sector.

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28

Bieger, Joshua, Jadalaine Ferrer, Dillon Riedlinger, William Xu, and Jeffrey Demarest. "Simulating Army Rail Yard Operations at the Port of Bremerhaven." Industrial and Systems Engineering Review 6, no. 2 (March 7, 2019): 95–100. http://dx.doi.org/10.37266/iser.2018v6i2.pp95-100.

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To maintain the United States military’s capability to deploy rapidly across the globe, logistical planning tools, simulations, and models enhance leaders’ decision making abilities. This research develops a discrete event model designed to simulate military operations within a railyard in order to support the Engineer Research and Development Center’s (ERDC) Planning Logistics Analysis Network System (PLANS). The research team chose the Port of Bremerhaven, Germany as a case study due to its relevance to current military operations, granting us access to timely data and stakeholders with recent operational experience. The discrete event simulation (DES) utilizes stochastic processes and multiple layouts in order to analyze the amount of time it takes to move varying amounts of cargo and vehicles and identify potential bottlenecks in the operation.

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29

Dhir, K. S. "Enhancing management's understanding of operational research models." Journal of the Operational Research Society 52, no. 8 (August 2001): 873–87. http://dx.doi.org/10.1057/palgrave.jors.2601148.

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30

Li, Feng, Alberto Nucciarelli, Sinead Roden, and Gary Graham. "How smart cities transform operations models: a new research agenda for operations management in the digital economy." Production Planning & Control 27, no. 6 (April 18, 2016): 514–28. http://dx.doi.org/10.1080/09537287.2016.1147096.

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31

Matsnev, Maksim V., Natalia G. Ponomareva, Oleg N. Tveritnev, Petr V. Tikhomirov, Dmitry M. Levushkin, and Andrei N. Briukhovetskii. "Calculating the Groundwork Volume for the Construction of Logging Roads." Lesnoy Zhurnal (Forestry Journal), no. 4 (July 5, 2022): 156–72. http://dx.doi.org/10.37482/0536-1036-2022-4-156-172.

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Анотація:

Organizational and engineering models provide a formalized description of the construction process, including a list of operations, their sequence, description of the relationship between the operations, and the features of the work technology. Network models have the most complete mathematical description. However, a number of authors in their works point to serious shortcomings inherent in the network modeling of construction flow. The network model allows displaying accurately enough only the relationship of specialized traffic flows when the object is divided into sections equal in size to interchangeable grips. Such schedules are quite effective in planning and operational management of road works in a short planning period (decade or month). The adequacy of the real construction representation is violated when the network model is enlarged for long-term planning of the flow. The currently developed so-called generalized network models achieve process combination and continuity by introducing links between the operations, characterized by temporal parameters assuming any values, including negative values. However, the complexity of the mathematical description of these models still prevents their use in solving optimization problems. The research aims at developing a mathematical model and an algorithm for calculating the operational control of the groundwork volume during the construction of logging roads. A model of a complex object flow is proposed, where the flow operation time has a discrete nature. This condition is realized in the model by dividing the planned operation period of a complex flow into equal time intervals – scheduling intervals. The below described model of complex road construction flow can be presented on the basis of the scheduling task classification scheme and the analysis of existing economic and mathematical models and methods. The scheduling model developed for the purpose of determining the optimal groundwork for the road construction elements is a deterministic (at the 1st stage), discrete in time, engineering model with variable speeds of operations.

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32

Michalowski, Wojtek, and Hannele Wallenius. "Special section on behavioral considerations in developing and applying operations research models." Annals of Operations Research 211, no. 1 (November 14, 2013): 491–92. http://dx.doi.org/10.1007/s10479-013-1501-6.

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33

WILLIAMS, H. P. "How Important are Models to Operational Research?" IMA Journal of Management Mathematics 2, no. 2 (1989): 189–95. http://dx.doi.org/10.1093/imaman/2.2.189.

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34

Alabdulkarim, Abdullah A., Peter Ball, and Ashutosh Tiwari. "Assessing asset monitoring levels for maintenance operations." Journal of Manufacturing Technology Management 26, no. 5 (June 1, 2015): 632–59. http://dx.doi.org/10.1108/jmtm-01-2013-0003.

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Анотація:

Purpose – Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues. Design/methodology/approach – This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring. Findings – This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective. Practical implications – The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs. Originality/value – A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

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35

Gani, Fathurrahmah Abd, Dasa Ismaimuza, and Sudarman Sudarman. "PROFIL PEMAHAMAN KONSEP SISWA DITINJAU DARI TINGKAT KEMAMPUAN MATEMATIKA PADA MATERI FUNGSI KOMPOSISI." Aksioma 9, no. 2 (September 25, 2020): 98–111. http://dx.doi.org/10.22487/aksioma.v9i2.520.

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Abstract: The aim of this research was to describe the profile of understanding the concept of class X MIA students based on the level of mathematical ability. The research was conducted at MA Alkhairaat Palu using a qualitative descriptive approach. The results of the study show that the understanding of the concept of ST in classifying the function of composition is that there is a function and operation of composition. Identify the characteristics of operations or concepts students use associative, distributive, composition operations and algebraic. Applying the concept students explain the properties and operations. Giving examples and not the composition function of the students explains the example, that there is an operation of composition and not there is no operation of the composition. Presenting the problem students presents in the form of mathematical models. Understanding the SS concept in classifying composition functions, namely a combination of functions associated with composition operations. Identify the characteristics of operations or concepts, namely the nature of distributive, operating composition and calculating algebra. Applying the concept students explain the properties and operations. Give an example and not an example of a composition function is an example is that there is a composition operation and not that there is no composition operation. Presenting problems in the form of mathematical models. Understanding the concept of SR in classifying the function of composition, namely there is a composition operation. Give an example and not an example of a composition function, is an example there is a composition operation and not an example, that is, there is no composition operation.

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36

Настин, Ю. Я. "Research and modeling of operational planning of cargo operations in the sea port." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 4(50) (December 17, 2020): 130–35. http://dx.doi.org/10.37220/mit.2020.50.4.083.

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Анотація:

Статья продолжает прежние исследования автора в области построения модели оперативного планирования грузовых операций в морском порту. Затрагивается широкий круг вопросов (стратификация, семиотика, искусственный интеллект). Основное внимание уделено моделированию на верхней «математической» страте - объёмно-календарному планированию на основе многоэтапного нелинейного и динамического программирований (НП), эвристик и экстраполирования. Оптимальный план должен поступать на нижнюю страту, где рассматриваются каргопланы, грузовые технологические схемы, а средства моделирования – системы искусственного интеллекта. В основе моделей верхней страты - диспач-демередж, сталийное время, норма одновременной обработки. Предложены три группы моделей. Во-первых, n-этапные сепарабельные задачи НП; показан алгоритм решения двухэтапной задачи НП динамическим программированием с понижением размерности и множителем Лагранжа; обсуждаются проблемы решения при n>2. Во-вторых, экстраполяционные модели; они включают в себя в качестве «ядер циклов» модели из 1-й группы; обсуждаются способы применения эвристик. В-третьих, несепарабельные задачи НП, которые учитывают процедуры вхождения судов в норму одновременной обработки. Обозначено направление исследований и проектирования модели планирования. The article continues the author's previous research in the field of building a model for operational planning of cargo operations in a seaport. It covers a wide range of issues (stratification, semiotics, artificial intelligence). The main attention is paid to modeling on the upper "mathematical" stratum-volume-calendar planning based on multi-stage nonlinear and dynamic programming (NP), heuristics and extrapolation. The optimal plan should be sent to the lower stratum, where cargoplans, cargo technological schemes are considered, and modeling tools – artificial intelligence systems. The upper stratum models are based on dispatch-demurrage, steel time, and the rate of simultaneous processing. Three groups of models are proposed. First, n-stage separable NP problems; an algorithm for solving a two-stage NP problem by dynamic programming with reduced dimension and a Lagrange multiplier is shown; solution problems for n>2 are discussed. Second, extrapolation of the model; they include models from group 1 as "cycle cores"; ways to apply heuristics are discussed. Third, non-separable NP tasks that take into account the procedures for vessels entering the simultaneous processing norm. The direction of research and design of the planning model is indicated.

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37

Filippini, Roberto. "Operations management research: some reflections on evolution, models and empirical studies in OM." International Journal of Operations & Production Management 17, no. 7 (July 1997): 655–70. http://dx.doi.org/10.1108/01443579710175583.

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38

Duman, Ekrem. "Probability models in operations research, by C. Richard Cassady and Joel A. Nachlas." International Journal of Production Research 47, no. 21 (August 13, 2009): 6177–78. http://dx.doi.org/10.1080/00207540902940366.

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39

Kraus, Mathias, Stefan Feuerriegel, and Asil Oztekin. "Deep learning in business analytics and operations research: Models, applications and managerial implications." European Journal of Operational Research 281, no. 3 (March 2020): 628–41. http://dx.doi.org/10.1016/j.ejor.2019.09.018.

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40

Settanni, Ettore, Tomás Seosamh Harrington, and Jagjit Singh Srai. "Pharmaceutical supply chain models: A synthesis from a systems view of operations research." Operations Research Perspectives 4 (2017): 74–95. http://dx.doi.org/10.1016/j.orp.2017.05.002.

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41

Bharathi Raja, S., and N. Baskar. "Optimization techniques for machining operations: a retrospective research based on various mathematical models." International Journal of Advanced Manufacturing Technology 48, no. 9-12 (December 8, 2009): 1075–90. http://dx.doi.org/10.1007/s00170-009-2351-x.

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42

Pati, Debajyoti. "Improving Precision and Confidence of Research Application Through Mediator and Moderator Models." HERD: Health Environments Research & Design Journal 13, no. 2 (March 20, 2020): 12–20. http://dx.doi.org/10.1177/1937586720911382.

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Анотація:

Purpose: The purpose of this article is to conceptually discuss moderator and mediator models in healthcare design studies. Background: Healthcare outcomes are many times the result of meaningful interactions between multiple factors from different domains such as physical design, operations, culture, psychology, physiology, and social, among others. Simple multivariate models may not (a) capture these intricate relationships, (b) provide predictive precision, and (c) optimize arguments behind design decisions. Methods: A review of text books and publications on research methods was conducted, along with a review of completed research studies to identify appropriate examples for articulation. Results: An analysis of a nonrandom sample of completed healthcare design studies demonstrates the potential benefits of developing and testing moderator and mediator models in healthcare design studies. Conclusions: Moderator and mediator models may help optimize multifaceted solutions that include simultaneous and parallel interventions in the physical, operational, cultural, psychological, physiological, social, and/or other environments hypothesized to play a role in the outcome of interest.

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Teow, Kiok Liang. "Practical Operations Research Applications for Healthcare Managers." Annals of the Academy of Medicine, Singapore 38, no. 6 (June 15, 2009): 564–66. http://dx.doi.org/10.47102/annals-acadmedsg.v38n6p564.

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Operations research (OR) focuses on the application of analytical methods to facilitate better decision-making. Despite its usefulness and proliferation of papers in the academic literature, there are still major issues around getting OR models widely accepted and used as part of mainstream decision-making by clinicians, health managers and policy-makers. This article aims to raise the awareness of healthcare managers with regard to practical OR applications. Key words: Decision-making, Healthcare, Operations Research

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Tao, Zhou, Chang XiaoYu, Lu HuiLing, Ye XinYu, Liu YunCan, and Zheng XiaoMin. "Pooling Operations in Deep Learning: From “Invariable” to “Variable”." BioMed Research International 2022 (June 20, 2022): 1–17. http://dx.doi.org/10.1155/2022/4067581.

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Deep learning has become a research hotspot in multimedia, especially in the field of image processing. Pooling operation is an important operation in deep learning. Pooling operation can reduce the feature dimension, the number of parameters, the complexity of computation, and the complexity of time. With the development of deep learning models, pooling operation has made great progress. The main contributions of this paper on pooling operation are as follows: firstly, the steps of the pooling operation are summarized as the pooling domain, pooling kernel, step size, activation value, and response value. Secondly, the expression form of pooling operation is standardized. From the perspective of “invariable” to “variable,” this paper analyzes the pooling domain and pooling kernel in the pooling operation. Pooling operation can be classified into four categories: invariable of pooling domain, variable of pooling domain, variable of pooling kernel, and the pooling of invariable “+” variable. Finally, the four types of pooling operation are summarized and discussed with their advantages and disadvantages. There is great significance to the research of pooling operations and the iterative updating of deep learning models.

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CHOI (Jason), Tsan‐Ming. "Special Issue on “Operational Research Models for Blockchain”." International Transactions in Operational Research 30, no. 2 (October 10, 2022): 1171–72. http://dx.doi.org/10.1111/itor.13215.

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CHOI (Jason), Tsan‐Ming. "Special Issue on “Operational Research Models for Blockchain”." International Transactions in Operational Research 30, no. 1 (August 3, 2022): 642–43. http://dx.doi.org/10.1111/itor.13194.

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CHOI (Jason), Tsan‐Ming. "Special Issue on “Operational Research Models for Blockchain”." International Transactions in Operational Research 29, no. 6 (June 8, 2022): 3800–3801. http://dx.doi.org/10.1111/itor.12832.

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CHOI (Jason), Tsan‐Ming. "Special Issue on “Operational Research Models for Blockchain”." International Transactions in Operational Research 29, no. 5 (April 7, 2022): 3231–32. http://dx.doi.org/10.1111/itor.12829.

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Zeng, Cheng, Dong Bo Yang, Li Liu, and Feng Tian Cai. "Modeling and Simulation Research on the Effect of Driving Skills on Vehicle Fuel Efficiency." Advanced Materials Research 512-515 (May 2012): 1167–73. http://dx.doi.org/10.4028/www.scientific.net/amr.512-515.1167.

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The research and application of eco-driving has the important practical significance for the energy conservation and emission reduction of automobile industry. In this paper, a simulation model has been set up based on GT-Suite and mathematical models of driving operations to simulate three typical driving operations. The results show that the acceleration speed plays important role in the acceleration operation. As the acceleration speed increases, fuel economy significantly deteriorates. In the transmission operation, the engine should not be operated in the high speed region. Otherwise, the fuel economy would deteriorate. In the cruise operation, the gear should be selected to matching the speed. High-gear low speed operation will cause safety problems, while low-gear high-speed operation will cause significantly deterioration of fuel economy.

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Antunes, Carlos Henggeler, and Álvaro Gomes. "Operational research models and methods in the energy sector." European Journal of Operational Research 197, no. 3 (September 2009): 997–98. http://dx.doi.org/10.1016/j.ejor.2008.03.013.

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