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Naslov:Dynamic price competition market for retailers in the context of consumer learning behavior and supplier competition: machine learning-enhanced agent-based modeling and simulation
Avtorji:ID Deng, G. F. (Avtor)
Datoteke:.pdf APEM18-4_434-446.pdf (1,15 MB)
MD5: 5FF5A213CBCBF6E3F095B427403BE2C0
 
URL https://apem-journal.org/Archives/2023/APEM18-4_434-446.pdf
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Opis:This study analyzes the impact of consumer learning behavior and supplier price competition on retailer price competition in a complex adaptive system. Using machine Learning-enhanced agent-based modeling and simulation, the study applies fuzzy logic and genetic algorithms to model price decisions, and reinforcement learning and swarm intelligence to model consumer behavior. Simulations reveal that different learning behaviors result in different retailer competition patterns, and that supplier price competition affects the strength of retailer price competition. Simulation results demonstrate that consumer learning behavior influences retailer competition, with self-learning consumers leading to higher-priced partnerships, and collective-learning consumers leading to a shift in price competition among retailers. In contrast, perfect rationality consumers result in low-price competition and the lowest average margin and profit. Additionally, the competitive price behavior of suppliers impacts retailers' price competition patterns, with supplier price competition reducing retailer price competition in the perfect rationality consumer market and enhancing it in the self-learning and collective-learning consumer markets, leading to lower average prices and profits for retailers. This study presents a simulated market for price competition among suppliers, retailers, and consumers that can be expanded by subsequent scholars to test related hypotheses.
Ključne besede:pricing competitive model, complex adaptive system (CAS), agent-based modeling and simulation (ABMS), machine learning (ML), genetic algorithms (GA), fuzzy logic (FL), reinforcement learning (RL), swarm intelligence (SW), consumer learning behavior
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:10.04.2023
Datum sprejetja članka:16.11.2023
Datum objave:28.12.2023
Založnik:Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering
Leto izida:2023
Št. strani:str. 434-446
Številčenje:Vol. 18, no. 4
PID:20.500.12556/DKUM-97128 Novo okno
UDK:004.8
COBISS.SI-ID:268960771 Novo okno
DOI:10.14743/apem2023.4.483 Novo okno
ISSN pri članku:1854-6250
Avtorske pravice:Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Datum objave v DKUM:19.02.2026
Število ogledov:127
Število prenosov:1
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del revije

Naslov:Advances in production engineering & management
Skrajšan naslov:Adv produc engineer manag
Založnik:Fakulteta za strojništvo, Inštitut za proizvodno strojništvo
ISSN:1854-6250
COBISS.SI-ID:229859072 Novo okno

Gradivo je financirano iz projekta

Financer:Drugi - Drug financer ali več financerjev
Program financ.:the MOE Teaching Practice Research Program, ROC
Številka projekta:PGE1120918

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:kompleksni prilagodljivi sistemi, agentni modeli, simulacija, strojno učenje, genetski algoritem, mehka logika, spodbujevano učenje, vedenje potrošnikov


Zbirka

To gradivo je del naslednjih zbirk del:
  1. Advances in production engineering & management

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