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Статті в журналах з теми "Event-Chain"

Автор: Grafiati

Опубліковано: 30 листопада 2024

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1

Heusler, Klaus Felix, Wolfgang Stölzle, and Harald Bachmann. "Supply Chain Event Management." WiSt - Wirtschaftswissenschaftliches Studium 35, no. 1 (2006): 19–24. http://dx.doi.org/10.15358/0340-1650-2006-1-19.

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2

Nissen, Volker. "Supply Chain Event Management." Wirtschaftsinformatik 44, no. 5 (October 2002): 477–80. http://dx.doi.org/10.1007/bf03250869.

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3

Sangeetha, S., R. S. Thakur, and Michael Arock. "Event detection using trigger chain." International Journal of Knowledge Engineering and Data Mining 2, no. 1 (2012): 76. http://dx.doi.org/10.1504/ijkedm.2012.044708.

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4

Barclay, Lorna M., Rodrigo A. Collazo, Jim Q. Smith, Peter A. Thwaites, and Ann E. Nicholson. "The dynamic chain event graph." Electronic Journal of Statistics 9, no. 2 (2015): 2130–69. http://dx.doi.org/10.1214/15-ejs1068.

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5

Stölzle, Wolfgang. "Kommentar zum Supply Chain Event Management." Die Unternehmung 65, no. 2 (2011): 130–32. http://dx.doi.org/10.5771/0042-059x-2011-2-130.

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6

Otto, Andreas. "Supply Chain Event Management: Three Perspectives." International Journal of Logistics Management 14, no. 2 (July 1, 2003): 1–13. http://dx.doi.org/10.1108/09574090310806567.

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Анотація:

Supply Chain Event Management (SCEM) addresses a fundamental business problem: inter‐organizational processes rarely execute as scheduled, since they happen in an environment prone to failure and disturbance. SCEM attempts to identify, as early as possible, the resulting deviations between the plan and its execution across the multitude of processes and actors in the supply chain to trigger corrective actions according to predefined rules. Despite SCEM's well documented attractiveness for practitioners, it has received little attention as a field of academic research. This paper provides an introduction into SCEM from three complementary perspectives: SCEM as a management concept; as a software solution; and as a software component. Each is analyzed in detail and potential fields of research on SCEM are presented.

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7

Barclay, Lorna M., Jane L. Hutton, and Jim Q. Smith. "Chain Event Graphs for Informed Missingness." Bayesian Analysis 9, no. 1 (March 2014): 53–76. http://dx.doi.org/10.1214/13-ba843.

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8

Smith, Jim Q., and Paul E. Anderson. "Conditional independence and chain event graphs." Artificial Intelligence 172, no. 1 (January 2008): 42–68. http://dx.doi.org/10.1016/j.artint.2007.05.004.

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9

Thwaites, Peter, Jim Q. Smith, and Eva Riccomagno. "Causal analysis with Chain Event Graphs." Artificial Intelligence 174, no. 12-13 (August 2010): 889–909. http://dx.doi.org/10.1016/j.artint.2010.05.004.

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10

Thwaites, Peter. "Causal identifiability via Chain Event Graphs." Artificial Intelligence 195 (February 2013): 291–315. http://dx.doi.org/10.1016/j.artint.2012.09.003.

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11

Lv, Shangwen, Wanhui Qian, Longtao Huang, Jizhong Han, and Songlin Hu. "SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6802–9. http://dx.doi.org/10.1609/aaai.v33i01.33016802.

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Scripts represent knowledge of event sequences that can help text understanding. Script event prediction requires to measure the relation between an existing chain and the subsequent event. The dominant approaches either focus on the effects of individual events, or the influence of the chain sequence. However, only considering individual events will lose much semantic relations within the event chain, and only considering the sequence of the chain will introduce much noise. With our observations, both the individual events and the event segments within the chain can facilitate the prediction of the subsequent event. This paper develops self attention mechanism to focus on diverse event segments within the chain and the event chain is represented as a set of event segments. We utilize the event-level attention to model the relations between subsequent events and individual events. Then, we propose the chain-level attention to model the relations between subsequent events and event segments within the chain. Finally, we integrate event-level and chain-level attentions to interact with the chain to predict what happens next. Comprehensive experiment results on the widely used New York Times corpus demonstrate that our model achieves better results than other state-of-the-art baselines by adopting the evaluation of Multi-Choice Narrative Cloze task.

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12

Rinab, Peter. "RFID im mobile supply chain event management." WIRTSCHAFTSINFORMATIK 49, no. 4 (August 2007): 312. http://dx.doi.org/10.1007/s11576-007-0123-5.

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13

HUANG, Z. Y. "DYNAMIC PROGRAMMING PATH OF MOBILE ROBOT BASED ON EVENT-DRIVEN PROCESS." Latin American Applied Research - An international journal 48, no. 4 (October 31, 2018): 317–22. http://dx.doi.org/10.52292/j.laar.2018.247.

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Event-driven process chain is an important method for path flow and system optimization in the process of robot dynamic planning path research. Path planning is an important part of robot research. The path planning problem and the path optimization planning problem of the robot in the dynamic environment are explored deeply in this paper based on the event-driven process chain perspective and the actual needs of the research project. Therefore, it uses the event-driven process chain method to optimize the path according to the original process and existing requirements based on the concept of event-driven process chain. A dynamic path planning model under event-driven process chain is constructed, and then the function, user and permission, code, input and output, and security of industrial robot system are optimized.

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14

Bodendorf, Freimut, and Roland Zimmermann. "Proactive Supply-Chain Event Management with Agent Technology." International Journal of Electronic Commerce 9, no. 4 (July 2005): 58–89. http://dx.doi.org/10.1080/10864415.2003.11044340.

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15

Freeman, G., and J. Q. Smith. "Bayesian MAP model selection of chain event graphs." Journal of Multivariate Analysis 102, no. 7 (August 2011): 1152–65. http://dx.doi.org/10.1016/j.jmva.2011.03.008.

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16

Vlahakis, George, Dimitris Apostolou, and Evi Kopanaki. "Enabling situation awareness with supply chain event management." Expert Systems with Applications 93 (March 2018): 86–103. http://dx.doi.org/10.1016/j.eswa.2017.10.013.

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17

Banno, H., D. Mugita, and M. Isobe. "Diffusional characteristics of a Newtonian event-chain Monte Carlo in hard disk systems." Journal of Physics: Conference Series 2207, no. 1 (March 1, 2022): 012011. http://dx.doi.org/10.1088/1742-6596/2207/1/012011.

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Abstract The equilibration and relaxation times of the physical properties of time correlation functions have historically been of fundamental importance in terms of molecular simulation. An efficient algorithm for the acceleration of equilibration, termed the ‘event-chain Monte Carlo’ has been investigated over the past decade. Recently, by adding the collision rule often used in event-driven molecular dynamics, a Newtonian event-chain Monte Carlo was developed. In this event-chain Monte Carlo and variants thereof, optimal performance strongly depends on chain length, physical properties, and system size; the microscopic mechanism in play remains elusive. To identify key factors affecting equilibration, we investigated the diffusional characteristics per event (collision) for each algorithm, focusing systematically on the diffusion coefficients and pair dispersions in two-dimensional hard disk systems.

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18

Karlsson, Ulf, and Anders Ulfvarson. "Chain Breakers—A Survey of Fatal Ship Accidents With the Event-Chain Method." Marine Technology and SNAME News 45, no. 03 (July 1, 2008): 182–90. http://dx.doi.org/10.5957/mt1.2008.45.3.182.

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The ship research project DESSO (Design for Survival Onboard) aims at improving the safety of RO/RO (roll-on/roll-off) ferries. The project was organized in the autumn of 2003 in order to use the research and development resources existing in Sweden, combined with international expertise, for a radical RO/RO concept with a focus on safety. One part in the early stages of this project was to identify chain breakers from previous accidents, that is, something that would have prevented, stopped, or reduced the conse-quences of the incident event, in order to obtain a realistic basis of "working on the right problem." A systematic and in-depth analysis of a few selected significant and well-documented disasters was made using the event-chain method. The identified chain breakers were further categorized and analyzed to find ideas and concepts that would prevent these events from occurring. These efforts were further developed into a method where the graphics of event chains with chain breakers are used in workshops with experts in two stages. In general, it was found that "Management" in most cases was the initial cause of the events. Often, "Management" was also the reason for the continuing or worsening of the events. As such, education/training and stricter routines were found to be the most effective methods for reducing accidents and are at the same time an inexpensive measure to take. This, together with online stability calculations and decision-support systems, would have prevented most of the accidents analyzed in this work.

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19

Cigolini, Roberto, Margherita Pero, Tommaso Rossi, and Andrea Sianesi. "Linking supply chain configuration to supply chain perfrmance: A discrete event simulation model." Simulation Modelling Practice and Theory 40 (January 2014): 1–11. http://dx.doi.org/10.1016/j.simpat.2013.08.002.

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20

Yoo, Joanne Jung-Eun, and Meehee Cho. "Supply chain flexibility fit and green practices: evidence from the event industry." International Journal of Contemporary Hospitality Management 33, no. 7 (May 5, 2021): 2410–27. http://dx.doi.org/10.1108/ijchm-09-2020-0999.

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Purpose As the event industry is supported by a wide range of suppliers that provide different products and services, all these partners in the supply chain can play a crucial role in green practices. The industry needs to have a sufficient degree of supply chain flexibility to cater to the demands in today’s dynamic environment and the flexibility in the supply chain will help event businesses to be sustainable. This study aims to explore the role of the supply chain flexibility fit between event planner’s requirements and supplier offerings on the implementation of green practices. The study also attempted to provide insights into the adoption of green event practices by identifying the moderating roles of green organization image and public pressure. Design/methodology/approach Data analysis was conducted based on 207 useable responses from event planners. Polynomial regression and response surface analysis were performed to verify the hypothesized relationships. Additionally, hierarchical regression analysis was used to test the moderating effects of green organization image and public pressure on the proposed relationships. Findings Results revealed that product flexibility fit was positively related to green practices; however, such effect was not found in the volume flexibility fit model. Findings also showed that public pressure significantly improved the positive effect of product flexibility fit on green practices. However, the green organization image was found to have no significant moderating effect. Practical implications The adoption of green practices requires well-designed collaboration among supply chain partners. This study offers empirical evidence regarding the importance of achieving product flexibility fit between event planners’ requirements and supplier offerings when conducting green practices. The findings provide useful implications that can be applied for successful green event management. Originality/value Despite the significance and relevance of the topic, barely any study has been conducted to assess supply chain flexibility and its relationship with green event practices. The study adopted resource orchestration theory to examine the role of supply chain flexibility on green event practices by focusing on the planner-supplier flexibility fit. A number of implications regarding supply chain management and future research are identified.

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21

Wang, Q. "A discrete event modelling approach for supply chain simulation." International Journal of Simulation Modelling 7, no. 3 (September 15, 2008): 124–34. http://dx.doi.org/10.2507/ijsimm07(3)2.100.

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22

Avlijaš, Goran, and Miljan Radunović. "Application of Event Chain Methodology in Schedule Risk Analysis." European Project Management Journal 9, no. 2 (2019): 26–34. http://dx.doi.org/10.18485/epmj.2019.9.2.3.

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23

Jain, Swati, Suraj Prakash Narayan, Nalini Meena, Rupesh Kumar Dewang, Utkarsh Bhartiya, Varun Kumar, and Arvind Mewada. "Event Detection through Lexical Chain Based Semantic Similarity Algorithm." IOP Conference Series: Materials Science and Engineering 1166, no. 1 (July 1, 2021): 012016. http://dx.doi.org/10.1088/1757-899x/1166/1/012016.

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24

Wahyuni, H. C., I. Vanany, and U. Ciptomulyono. "Identifying risk event in Indonesian fresh meat supply chain." IOP Conference Series: Materials Science and Engineering 337 (April 2018): 012031. http://dx.doi.org/10.1088/1757-899x/337/1/012031.

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25

Doronina, Victoria A., Pablo de Felipe, Cheng Wu, Pamila Sharma, Matthew S. Sachs, Martin D. Ryan, and Jeremy D. Brown. "Dissection of a co-translational nascent chain separation event." Biochemical Society Transactions 36, no. 4 (July 22, 2008): 712–16. http://dx.doi.org/10.1042/bst0360712.

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Some RNA and protein sequences are capable of directing changes to the course of translation from that expected from the mRNA sequence, and this process is termed translational ‘recoding’. ‘CHYSEL’ peptides are ∼19-amino-acid sequences found in many viral genomes. When translated at internal portions of polypeptides, they yield co-translational separation of the nascent chain at their C-termini. We dissected the reaction promoted by CHYSEL sequences using yeast genetics and in vitro translation systems. Our results indicate that the reaction occurs within the peptidyltransferase centre of the ribosome where the nascent chain is hydrolytically released from tRNA despite the presence of further sense codons.

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26

Barclay, L. M., J. L. Hutton, and J. Q. Smith. "Refining a Bayesian Network using a Chain Event Graph." International Journal of Approximate Reasoning 54, no. 9 (November 2013): 1300–1309. http://dx.doi.org/10.1016/j.ijar.2013.05.006.

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27

Palma, Fanny, Jania A. Saucedo, J. A. Marmolejo-Saucedo, and Roman Rodriguez-Aguilar. "The supply chain event management application: a case study." IFAC-PapersOnLine 52, no. 13 (2019): 2698–703. http://dx.doi.org/10.1016/j.ifacol.2019.11.615.

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28

Darrall, Jan. "1992: a non-event? Perceptions of the food chain." Food Policy 17, no. 1 (February 1992): 81–83. http://dx.doi.org/10.1016/0306-9192(92)90020-x.

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29

Michel, Manon, Johannes Mayer, and Werner Krauth. "Event-chain Monte Carlo for classical continuous spin models." EPL (Europhysics Letters) 112, no. 2 (October 1, 2015): 20003. http://dx.doi.org/10.1209/0295-5075/112/20003.

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30

Sangjin Lee, Weiyi Liu, and Inseok Hwang. "Markov Chain Approximation Algorithm for Event-Based State Estimation." IEEE Transactions on Control Systems Technology 23, no. 3 (May 2015): 1123–30. http://dx.doi.org/10.1109/tcst.2014.2349971.

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31

Echefaj, K., A. Charkaoui, and A. Cherrafi. "Analyzing Capabilities for Resilient Supply Chain in Unexpected Event." IFAC-PapersOnLine 55, no. 10 (2022): 3190–95. http://dx.doi.org/10.1016/j.ifacol.2022.10.220.

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32

Liu, Rong, Akhil Kumar, and Wil van der Aalst. "A formal modeling approach for supply chain event management." Decision Support Systems 43, no. 3 (April 2007): 761–78. http://dx.doi.org/10.1016/j.dss.2006.12.009.

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33

Zhao, Lijuan, Yi Li, Shihua Luo, Chao Zhang, Xiaohua Duan, Xinhui He, Yanqing He, Yan Gao, Yong Guan, and Hui Tian. "Choroid Theory Guides the Theory of Cardiovascular Event Chain." Highlights in Science, Engineering and Technology 30 (February 15, 2023): 196–200. http://dx.doi.org/10.54097/hset.v30i.4973.

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Анотація:

The theory, cardiovascular disease is one of the most common clinical chronic diseases. Modern medicine has confirmed that cardiovascular diseases are caused by the long-term accumulation and deposition of various pathological products caused by a variety of causes, and most of them are non-specific. Cardiovascular events such as acute myocardial infarction and stroke mainly occur in the occurrence process of cardiovascular diseases and cerebrovascular accidents, so the pathogenesis of cardiovascular and cerebrovascular diseases is very complex and difficult to predict. Therefore, this paper carries out the corresponding discussion on it.

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34

Vasthu, Durango. "Global Supply Chain Management Conference." International Journal of Risk and Contingency Management 1, no. 1 (January 2012): 64–65. http://dx.doi.org/10.4018/ijrcm.2012010105.

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The 2011 annual ‘big event’ in the global supply chain industry was held in Philadelphia PA USA during the week of October 2-5. This event is organized each year, and is the official global meeting, for the Council of Supply Chain Management Professionals (CSCMP, http://cscmp.org/events/annual-global/sessionsearch-after.asp). The conference featured risk-related topics ranging from inventory forecasting to reverse logistics planning in globally-impacted markets.

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35

Shi, Min, and Wei Yu. "Market Reactions to Supply Chain Management Excellence." Journal of Risk and Financial Management 11, no. 4 (October 12, 2018): 62. http://dx.doi.org/10.3390/jrfm11040062.

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A highly-respected public recognition of supply chain management (SCM) excellence is the Supply Chain Top 25 List, published annually by AMR Research. By employing event study method, this study extensively examined stock market reactions to annual announcements of the AMR Supply Chain Top 25 List, under various market scenarios. The results showed that SCM leading firms consistently outperform market portfolios around annual press-release dates. The mean abnormal returns observed in the event window (0, +1) were positive and statistically significant. In addition, the findings were robust across different estimation models and various market indexes adopted in the event study. At the same time, it is worth noting that the event effect on market performance was temporary and diminished within 5 trading days. This study makes contributions to the growing body of knowledge on the strategic values of firm reputation in general, and for SCM excellence in particular.

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36

Fu, Bin Bin, and Jie Zhu. "A Research on Complex Event Processing Technology Based on Smart Logistic System." Applied Mechanics and Materials 722 (December 2014): 430–35. http://dx.doi.org/10.4028/www.scientific.net/amm.722.430.

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With IOT technology developing and the cost reducing, Its application in supply chain is a matter of time. Smart logistic system is one of the IOT technology application in supply chain which solve difficult problems, such as acquisition underlying data, information transfer and so on. we need to achieve higher level application and solve more complex problems such as improving inventory management accuracy, reducing supply chain management cost, improving accuracy of supply and demand prediction, supply chain's rapidly react ability,these need to use complex event processing technology. It will introduce how to apply complex event processing technology to supply chain system based on IOT. By this way we can sort out valuable information by processing a large number of simple event.

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37

Lee, Chung-Hong, Hsin-Chang Yang, Yu-Chen Wei, and Wen-Kai Hsu. "Enabling Blockchain Based SCM Systems with a Real Time Event Monitoring Function for Preemptive Risk Management." Applied Sciences 11, no. 11 (May 24, 2021): 4811. http://dx.doi.org/10.3390/app11114811.

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The risk of supply chain disruption is usually related to daily disturbances in supply chain operations (e.g., demand fluctuations) and some emergency risks, such as earthquakes and epidemic outbreaks. During a crisis, companies need agility to quickly find new suppliers and open auxiliary sales channels to meet customer needs and remain competitive. However, identifying “event” is one of the most difficult challenges of current decision support systems. If the system encounters an emergency, it is usually unable to promptly notify users of the warning to avoid risks. A sensible solution is to incorporate the real-time event-monitoring system into SCM (i.e., supply chain management) in order to share emergency information in the early stage for preemptive management in the supply chain. On the other hand, in order to process confidential supply chain data with other members, the SCM infrastructure requires secure data sharing. The blockchain-based SCM system can improve the transparency of traceability to ensure that the supply chain system provides high-quality products and protects data privacy and security. The view is taken; therefore, in this work, we combined a method of real-time event detection using collected Twitter data and blockchain technology for event monitoring to improve the visibility of the supply chain system and take preemptive measures for risk avoidance. The experiments show some interesting results and potentials for future work in the field of the agile supply chain.

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38

Malyshev, E., and T. Malysheva. "THE USE OF DIGITAL TECHNOLOGY IN SUPPLY CHAIN MANAGEMENT." Transbaikal state university journal 27, no. 3 (2021): 113–18. http://dx.doi.org/10.21209/2227-9245-2021-27-3-113-118.

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Анотація:

This article provides an overview of the event management concept in supply chains. The management of organizational change in supply chains can be based on five basic approaches. Supply Chain Event Management is the creation and management of supply chain events. The concept of SCEM involves tracing objects, which are defined as reference points, across multiple events. SCEM is an integration of the following aspects: monitoring; notification; modelling; control; measurement. The study shows that this system is beneficial for supply chain management

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39

Li, Jun Tao, Gang Lin, and Xiao Lin Cheng. "Research of IOT Context-Aware Processing Framework Based on Complex Event for Supply Chain Application." Applied Mechanics and Materials 263-266 (December 2012): 1677–87. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1677.

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Анотація:

In order to solve the logic fault of Internet of things technology applied in the supply chain system, this paper presents a supply chain oriented context-aware Framework which is based on Complex Event Processing. The frame is subdivided into five levels and proposes an active event flow situation model based on Complex Event Processing. Besides, it can provide a semantic senior situation for the application of the upper layer through processing the uncertainty contextual information under the semantic rules, as a support for the top decision.

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40

Al-Fedhly, Haider, and Duncan Folley. "Discrete Event Simulation Software for Agent-Based Supply Chain Demand." International Journal of Industry and Sustainable Development 1, no. 2 (July 1, 2020): 20–35. http://dx.doi.org/10.21608/ijisd.2020.101606.

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41

Kang, Jun-Gyu, and Seung-Kil Lim. "Business Process Analysis Based on Event-driven Process Chain Model." Journal of Society of Korea Industrial and Systems Engineering 36, no. 3 (September 30, 2013): 34–42. http://dx.doi.org/10.11627/jkise.2013.36.3.34.

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42

Jankowska, Anna Maria, Karl Kurbel, and Denny Schreber. "An architecture for agent-based mobile Supply Chain Event Management." International Journal of Mobile Communications 5, no. 3 (2007): 243. http://dx.doi.org/10.1504/ijmc.2007.012393.

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43

Yoo, Taejong, Hyunbo Cho, and Enver Yücesan. "Hybrid algorithm for discrete event simulation based supply chain optimization." Expert Systems with Applications 37, no. 3 (March 15, 2010): 2354–61. http://dx.doi.org/10.1016/j.eswa.2009.07.039.

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44

Cowell, Robert G., and James Q. Smith. "Causal discovery through MAP selection of stratified chain event graphs." Electronic Journal of Statistics 8, no. 1 (2014): 965–97. http://dx.doi.org/10.1214/14-ejs917.

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45

Isobe, Masaharu, and Werner Krauth. "Hard-sphere melting and crystallization with event-chain Monte Carlo." Journal of Chemical Physics 143, no. 8 (August 28, 2015): 084509. http://dx.doi.org/10.1063/1.4929529.

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46

Achee, Badrul, Saikat Kundu, and Muhammad Latif. "Exploiting Discrete Event Simulation to Improve a Chicken Supply Chain." BOHR International Journal of Advances in Management Research 1, no. 1 (2022): 65–75. http://dx.doi.org/10.54646/bijamr.008.

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Анотація:

This article focuses on improving supply chain management (SCM) at a poultry processing plant. The strategies applied in SCM are dependent on understanding customers’ demand, the ability to forecast accurately, and maintaining smart inventory management to achieve the full advantages of SCM. This study exploits the integration of SCM concepts with business simulation, along with exploring strategies to improve the whole operation of a chicken supply chain. Witness Horizon software is utilized to model the supply chain of Company X, and information gathered on the demand and sales of poultry (chicken) is applied and computed in the simulation. The study investigates several ordering strategies, which are based on the previous week’s demand (PWD), weighted (forward) 3-point average demand (WAFD), weighted (backward) 3-point average demand (WABD), and moving average demand (MAD). Each simulation result obtained from different ordering strategies indicates the effectiveness and potential of the proposed methodology. These strategies are successfully developed and simulated, in which the results obtained demonstrate a desirable level of inventory, and profitability can be achieved by implementing these strategies in real time. The study concludes with MAD as being a suitable forecasting method for the given scenario.

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47

Bearzotti, Lorena A., Enrique Salomone, and Omar J. Chiotti. "An autonomous multi-agent approach to supply chain event management." International Journal of Production Economics 135, no. 1 (January 2012): 468–78. http://dx.doi.org/10.1016/j.ijpe.2011.08.023.

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48

Kundu, Saikat, Badrul Achee, and Muhammad Latif. "Exploiting Discrete Event Simulation to Improve a Chicken Supply Chain." BOHR International Journal of Advances in Management Research 1, no. 1 (2022): 63–74. http://dx.doi.org/10.54646/bijamr.2022.08.

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Анотація:

This article focuses on improving supply chain management (SCM) at a poultry processing plant. The strategies applied in SCM are dependent on understanding customers’ demand, the ability to forecast accurately, and maintaining smart inventory management to achieve the full advantages of SCM. This study exploits the integration of SCM concepts with business simulation, along with exploring strategies to improve the whole operation of a chicken supply chain. Witness Horizon software is utilized to model the supply chain of Company X, and information gathered on the demand and sales of poultry (chicken) is applied and computed in the simulation. The study investigates several ordering strategies, which are based on the previous week’s demand (PWD), weighted (forward) 3-point average demand (WAFD), weighted (backward) 3-point average demand (WABD), and moving average demand (MAD). Each simulation result obtained from different ordering strategies indicates the effectiveness and potential of the proposed methodology. These strategies are successfully developed and simulated, in which the results obtained demonstrate a desirable level of inventory, and profitability can be achieved by implementing these strategies in real time. The study concludes with MAD as being a suitable forecasting method for the given scenario.

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49

Fu, Zihao, Lidong Bing, and Wai Lam. "Open Domain Event Text Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7748–55. http://dx.doi.org/10.1609/aaai.v34i05.6278.

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Text generation tasks aim at generating human-readable text from different kinds of data. Normally, the generated text only contains the information included in the data and its application is thus restricted to some limited scenarios. In this paper, we extend the task to an open domain event text generation scenario with an entity chain as its skeleton. Specifically, given an entity chain containing several related event entities, the model should retrieve from a trustworthy repository (e.g. Wikipedia) the detailed information of these entities and generate a description text based on the retrieved sentences. We build a new dataset called WikiEvent1 that provides 34K pairs of entity chain and its corresponding description sentences. To solve the problem, we propose a wiki augmented generator framework that contains an encoder, a retriever, and a decoder. The encoder encodes the entity chain into a hidden space while the decoder decodes from the hidden space and generates description text. The retriever retrieves relevant text from a trustworthy repository which provides more information for generation. To alleviate the overfitting problem, we propose a novel random drop component that randomly deletes words from the retrieved sentences making our model more robust for handling long input sentences. We apply the proposed model on the WikiEvent dataset and compare it with a few baselines. The experimental results show that our carefully-designed architecture does help generate better event text, and extensive analysis further uncovers the characteristics of the proposed task.

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50

Fan, Min, and Shanwen Xu. "Research on Discourse Coherence based on the Analysis Model of Event Chain from the Perspective of Computational Linguistics." E3S Web of Conferences 189 (2020): 03025. http://dx.doi.org/10.1051/e3sconf/202018903025.

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With the rapid development of network technology, natural language processing has also entered a boom period. Probability and data-driven methods have been widely used in natural language processing. The need for people to extract and retrieve information from the Internet is also increasing, and more and more researchers are trying to use computers to process content related to discourse coherence. Based on the event chain of the text semantic structure representation, this paper proposes a text semantic structure representation model, on the basis of which, text coherent resources can be used for the task of text semantic analysis. Event chain is a necessary condition for discourse coherence, which can be transformed into a computable event chain analysis problem, and can be further formalized as discourse-oriented partial dependency analysis of sentences.

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