A reinforcement learning approach to competitive ordering and pricing problem
This study analyses simultaneous ordering and pricing decisions for retailers working in a multi‐retailer competitive environment for an infinite horizon. Retailers compete for the same market where the market demand is uncertain. The customer selects the winning agent (retailer) in each term on the...
Gespeichert in:
Veröffentlicht in: | Expert systems 2015-02, Vol.32 (1), p.39-48 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 48 |
---|---|
container_issue | 1 |
container_start_page | 39 |
container_title | Expert systems |
container_volume | 32 |
creator | Dogan, Ibrahim Güner, Ali R. |
description | This study analyses simultaneous ordering and pricing decisions for retailers working in a multi‐retailer competitive environment for an infinite horizon. Retailers compete for the same market where the market demand is uncertain. The customer selects the winning agent (retailer) in each term on the basis of random utility maximization, which depends primarily on retailer price and random error. The complexity of the problem is increased by competitiveness, necessity for simultaneous decisions and uncertainty in the nature of increases, and is not conducive to examination using standard analytical methods. Therefore, we model the problem using reinforcement learning (RL), which is founded on stochastic dynamic programming and agent‐based simulations. We analyse the effects of competitiveness and performance of RL on three different scenarios: a monopolistic case where one retailer employing a RL agent maximizes its profit, a duopolistic case where one retailer employs RL and another utilizes adaptive pricing and ordering policies, and a duopolistic case where both retailers employ RL. |
doi_str_mv | 10.1111/exsy.12054 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2789869370</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3582047491</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4004-f6405bdc2b127f77366d30a0368b2a79da743bb02e2236f00b52a81b86522c403</originalsourceid><addsrcrecordid>eNp9kEtPwzAQhC0EEqVw4RdE4oaUsn7ETo5V1QeohQNFQC-WnTiQkkdxUmj_PW4DHLuX3cM3M9pB6BJDD7u5MZt628MEAnaEOpjx0AcasWPUAcK5zwSBU3RW10sAwELwDpr1PWuyMq1sbApTNl5ulC2z8s1Tq5WtVPzuNZUXV8XKNFmTfRmvsomxe6BMvJXN4t3tUJ2b4hydpCqvzcXv7qKn0XA-mPjTh_HtoD_1YwbA_JQzCHQSE42JSIWgnCcUFFAeaqJElCjBqNZADCGUpwA6ICrEOuQBIc6CdtFV6-tyP9embuSyWtvSRUoiwijkERUHKcwDHAWUE-ao65aKbVXX1qTSPVUou5UY5K5UuStV7kt1MG7h7yw32wOkHL48vv5p_FaT1Y3Z_GuU_ZBcUBHI5_uxXMwni9H8LpAz-gP3NYfX</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1651953624</pqid></control><display><type>article</type><title>A reinforcement learning approach to competitive ordering and pricing problem</title><source>EBSCOhost Business Source Complete</source><source>Access via Wiley Online Library</source><creator>Dogan, Ibrahim ; Güner, Ali R.</creator><creatorcontrib>Dogan, Ibrahim ; Güner, Ali R.</creatorcontrib><description>This study analyses simultaneous ordering and pricing decisions for retailers working in a multi‐retailer competitive environment for an infinite horizon. Retailers compete for the same market where the market demand is uncertain. The customer selects the winning agent (retailer) in each term on the basis of random utility maximization, which depends primarily on retailer price and random error. The complexity of the problem is increased by competitiveness, necessity for simultaneous decisions and uncertainty in the nature of increases, and is not conducive to examination using standard analytical methods. Therefore, we model the problem using reinforcement learning (RL), which is founded on stochastic dynamic programming and agent‐based simulations. We analyse the effects of competitiveness and performance of RL on three different scenarios: a monopolistic case where one retailer employing a RL agent maximizes its profit, a duopolistic case where one retailer employs RL and another utilizes adaptive pricing and ordering policies, and a duopolistic case where both retailers employ RL.</description><identifier>ISSN: 0266-4720</identifier><identifier>EISSN: 1468-0394</identifier><identifier>DOI: 10.1111/exsy.12054</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>agent-based simulation ; Analysis ; Decision analysis ; Decision support systems ; Duopoly ; Dynamic programming ; Expert systems ; Pricing ; Pricing policies ; Purchasing ; Random errors ; reinforcement learning ; Retail stores ; Retailing industry ; Studies ; supply chain ; Supply chains</subject><ispartof>Expert systems, 2015-02, Vol.32 (1), p.39-48</ispartof><rights>2013 Wiley Publishing Ltd</rights><rights>Copyright © 2015 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4004-f6405bdc2b127f77366d30a0368b2a79da743bb02e2236f00b52a81b86522c403</citedby><cites>FETCH-LOGICAL-c4004-f6405bdc2b127f77366d30a0368b2a79da743bb02e2236f00b52a81b86522c403</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fexsy.12054$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fexsy.12054$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Dogan, Ibrahim</creatorcontrib><creatorcontrib>Güner, Ali R.</creatorcontrib><title>A reinforcement learning approach to competitive ordering and pricing problem</title><title>Expert systems</title><addtitle>Expert Systems</addtitle><description>This study analyses simultaneous ordering and pricing decisions for retailers working in a multi‐retailer competitive environment for an infinite horizon. Retailers compete for the same market where the market demand is uncertain. The customer selects the winning agent (retailer) in each term on the basis of random utility maximization, which depends primarily on retailer price and random error. The complexity of the problem is increased by competitiveness, necessity for simultaneous decisions and uncertainty in the nature of increases, and is not conducive to examination using standard analytical methods. Therefore, we model the problem using reinforcement learning (RL), which is founded on stochastic dynamic programming and agent‐based simulations. We analyse the effects of competitiveness and performance of RL on three different scenarios: a monopolistic case where one retailer employing a RL agent maximizes its profit, a duopolistic case where one retailer employs RL and another utilizes adaptive pricing and ordering policies, and a duopolistic case where both retailers employ RL.</description><subject>agent-based simulation</subject><subject>Analysis</subject><subject>Decision analysis</subject><subject>Decision support systems</subject><subject>Duopoly</subject><subject>Dynamic programming</subject><subject>Expert systems</subject><subject>Pricing</subject><subject>Pricing policies</subject><subject>Purchasing</subject><subject>Random errors</subject><subject>reinforcement learning</subject><subject>Retail stores</subject><subject>Retailing industry</subject><subject>Studies</subject><subject>supply chain</subject><subject>Supply chains</subject><issn>0266-4720</issn><issn>1468-0394</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPwzAQhC0EEqVw4RdE4oaUsn7ETo5V1QeohQNFQC-WnTiQkkdxUmj_PW4DHLuX3cM3M9pB6BJDD7u5MZt628MEAnaEOpjx0AcasWPUAcK5zwSBU3RW10sAwELwDpr1PWuyMq1sbApTNl5ulC2z8s1Tq5WtVPzuNZUXV8XKNFmTfRmvsomxe6BMvJXN4t3tUJ2b4hydpCqvzcXv7qKn0XA-mPjTh_HtoD_1YwbA_JQzCHQSE42JSIWgnCcUFFAeaqJElCjBqNZADCGUpwA6ICrEOuQBIc6CdtFV6-tyP9embuSyWtvSRUoiwijkERUHKcwDHAWUE-ao65aKbVXX1qTSPVUou5UY5K5UuStV7kt1MG7h7yw32wOkHL48vv5p_FaT1Y3Z_GuU_ZBcUBHI5_uxXMwni9H8LpAz-gP3NYfX</recordid><startdate>201502</startdate><enddate>201502</enddate><creator>Dogan, Ibrahim</creator><creator>Güner, Ali R.</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201502</creationdate><title>A reinforcement learning approach to competitive ordering and pricing problem</title><author>Dogan, Ibrahim ; Güner, Ali R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4004-f6405bdc2b127f77366d30a0368b2a79da743bb02e2236f00b52a81b86522c403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>agent-based simulation</topic><topic>Analysis</topic><topic>Decision analysis</topic><topic>Decision support systems</topic><topic>Duopoly</topic><topic>Dynamic programming</topic><topic>Expert systems</topic><topic>Pricing</topic><topic>Pricing policies</topic><topic>Purchasing</topic><topic>Random errors</topic><topic>reinforcement learning</topic><topic>Retail stores</topic><topic>Retailing industry</topic><topic>Studies</topic><topic>supply chain</topic><topic>Supply chains</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dogan, Ibrahim</creatorcontrib><creatorcontrib>Güner, Ali R.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dogan, Ibrahim</au><au>Güner, Ali R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A reinforcement learning approach to competitive ordering and pricing problem</atitle><jtitle>Expert systems</jtitle><addtitle>Expert Systems</addtitle><date>2015-02</date><risdate>2015</risdate><volume>32</volume><issue>1</issue><spage>39</spage><epage>48</epage><pages>39-48</pages><issn>0266-4720</issn><eissn>1468-0394</eissn><abstract>This study analyses simultaneous ordering and pricing decisions for retailers working in a multi‐retailer competitive environment for an infinite horizon. Retailers compete for the same market where the market demand is uncertain. The customer selects the winning agent (retailer) in each term on the basis of random utility maximization, which depends primarily on retailer price and random error. The complexity of the problem is increased by competitiveness, necessity for simultaneous decisions and uncertainty in the nature of increases, and is not conducive to examination using standard analytical methods. Therefore, we model the problem using reinforcement learning (RL), which is founded on stochastic dynamic programming and agent‐based simulations. We analyse the effects of competitiveness and performance of RL on three different scenarios: a monopolistic case where one retailer employing a RL agent maximizes its profit, a duopolistic case where one retailer employs RL and another utilizes adaptive pricing and ordering policies, and a duopolistic case where both retailers employ RL.</abstract><cop>Oxford</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/exsy.12054</doi><tpages>10</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0266-4720 |
ispartof | Expert systems, 2015-02, Vol.32 (1), p.39-48 |
issn | 0266-4720 1468-0394 |
language | eng |
recordid | cdi_proquest_journals_2789869370 |
source | EBSCOhost Business Source Complete; Access via Wiley Online Library |
subjects | agent-based simulation Analysis Decision analysis Decision support systems Duopoly Dynamic programming Expert systems Pricing Pricing policies Purchasing Random errors reinforcement learning Retail stores Retailing industry Studies supply chain Supply chains |
title | A reinforcement learning approach to competitive ordering and pricing problem |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T23%3A19%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20reinforcement%20learning%20approach%20to%20competitive%20ordering%20and%20pricing%20problem&rft.jtitle=Expert%20systems&rft.au=Dogan,%20Ibrahim&rft.date=2015-02&rft.volume=32&rft.issue=1&rft.spage=39&rft.epage=48&rft.pages=39-48&rft.issn=0266-4720&rft.eissn=1468-0394&rft_id=info:doi/10.1111/exsy.12054&rft_dat=%3Cproquest_cross%3E3582047491%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1651953624&rft_id=info:pmid/&rfr_iscdi=true |