Optimal pricing for tandem queues with finite buffers
We consider optimal pricing for a two-station tandem queueing system with finite buffers, communication blocking, and price-sensitive customers whose arrivals form a homogeneous Poisson process. The service provider quotes prices to incoming customers using either a static or dynamic pricing scheme....
Gespeichert in:
Veröffentlicht in: | Queueing systems 2019-08, Vol.92 (3-4), p.323-396 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 396 |
---|---|
container_issue | 3-4 |
container_start_page | 323 |
container_title | Queueing systems |
container_volume | 92 |
creator | Wang, Xinchang Andradóttir, Sigrún Ayhan, Hayriye |
description | We consider optimal pricing for a two-station tandem queueing system with finite buffers, communication blocking, and price-sensitive customers whose arrivals form a homogeneous Poisson process. The service provider quotes prices to incoming customers using either a static or dynamic pricing scheme. There may also be a holding cost for each customer in the system. The objective is to maximize either the discounted profit over an infinite planning horizon or the long-run average profit of the provider. We show that there exists an optimal dynamic policy that exhibits a monotone structure, in which the quoted price is non-decreasing in the queue length at either station and is non-increasing if a customer moves from station 1 to 2, for both the discounted and long-run average problems under certain conditions on the holding costs. We then focus on the long-run average problem and show that the optimal static policy performs as well as the optimal dynamic policy when the buffer size at station 1 becomes large, there are no holding costs, and the arrival rate is either small or large. We learn from numerical results that for systems with small arrival rates and no holding cost, the optimal static policy produces a gain quite close to the optimal gain even when the buffer at station 1 is small. On the other hand, for systems with arrival rates that are not small, there are cases where the optimal dynamic policy performs much better than the optimal static policy. |
doi_str_mv | 10.1007/s11134-019-09618-x |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2238260477</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2238260477</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-73de946f1dcdc72be70abcd24ebd6e048c0f9db442ab7241c80a921021772f663</originalsourceid><addsrcrecordid>eNp9kEtPwzAQhC0EEqXwBzhZ4mxYP2LHR1QBRarUC5wtx4-Sqk2KnYjy7zEEiRunPezM7M6H0DWFWwqg7jKllAsCVBPQktbkeIJmtFKMaCH4KZoBq1RZczhHFzlvAUCySs9QtT4M7d7u8CG1ru02OPYJD7bzYY_fxzCGjD_a4Q3HtmuHgJsxxpDyJTqLdpfD1e-co9fHh5fFkqzWT8-L-xVxnOqBKO6DFjJS77xTrAkKbOM8E6HxMoCoHUTtGyGYbRQT1NVgNaPAqFIsSsnn6GbKPaS-fJMHs-3H1JWThjFeMwlCqaJik8qlPucUoill9jZ9GgrmG4-Z8JiCx_zgMcdi4pMpF3G3Cekv-h_XFyrGaBI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2238260477</pqid></control><display><type>article</type><title>Optimal pricing for tandem queues with finite buffers</title><source>SpringerLink Journals - AutoHoldings</source><creator>Wang, Xinchang ; Andradóttir, Sigrún ; Ayhan, Hayriye</creator><creatorcontrib>Wang, Xinchang ; Andradóttir, Sigrún ; Ayhan, Hayriye</creatorcontrib><description>We consider optimal pricing for a two-station tandem queueing system with finite buffers, communication blocking, and price-sensitive customers whose arrivals form a homogeneous Poisson process. The service provider quotes prices to incoming customers using either a static or dynamic pricing scheme. There may also be a holding cost for each customer in the system. The objective is to maximize either the discounted profit over an infinite planning horizon or the long-run average profit of the provider. We show that there exists an optimal dynamic policy that exhibits a monotone structure, in which the quoted price is non-decreasing in the queue length at either station and is non-increasing if a customer moves from station 1 to 2, for both the discounted and long-run average problems under certain conditions on the holding costs. We then focus on the long-run average problem and show that the optimal static policy performs as well as the optimal dynamic policy when the buffer size at station 1 becomes large, there are no holding costs, and the arrival rate is either small or large. We learn from numerical results that for systems with small arrival rates and no holding cost, the optimal static policy produces a gain quite close to the optimal gain even when the buffer at station 1 is small. On the other hand, for systems with arrival rates that are not small, there are cases where the optimal dynamic policy performs much better than the optimal static policy.</description><identifier>ISSN: 0257-0130</identifier><identifier>EISSN: 1572-9443</identifier><identifier>DOI: 10.1007/s11134-019-09618-x</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Buffers ; Business and Management ; Communications systems ; Computer Communication Networks ; Control ; Customers ; Markov analysis ; Operations Research/Decision Theory ; Poisson density functions ; Pricing ; Pricing policies ; Probability Theory and Stochastic Processes ; Profits ; Queues ; Queuing theory ; Supply Chain Management ; Systems Theory</subject><ispartof>Queueing systems, 2019-08, Vol.92 (3-4), p.323-396</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Queueing Systems is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-73de946f1dcdc72be70abcd24ebd6e048c0f9db442ab7241c80a921021772f663</citedby><cites>FETCH-LOGICAL-c319t-73de946f1dcdc72be70abcd24ebd6e048c0f9db442ab7241c80a921021772f663</cites><orcidid>0000-0001-5434-7024</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11134-019-09618-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11134-019-09618-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Wang, Xinchang</creatorcontrib><creatorcontrib>Andradóttir, Sigrún</creatorcontrib><creatorcontrib>Ayhan, Hayriye</creatorcontrib><title>Optimal pricing for tandem queues with finite buffers</title><title>Queueing systems</title><addtitle>Queueing Syst</addtitle><description>We consider optimal pricing for a two-station tandem queueing system with finite buffers, communication blocking, and price-sensitive customers whose arrivals form a homogeneous Poisson process. The service provider quotes prices to incoming customers using either a static or dynamic pricing scheme. There may also be a holding cost for each customer in the system. The objective is to maximize either the discounted profit over an infinite planning horizon or the long-run average profit of the provider. We show that there exists an optimal dynamic policy that exhibits a monotone structure, in which the quoted price is non-decreasing in the queue length at either station and is non-increasing if a customer moves from station 1 to 2, for both the discounted and long-run average problems under certain conditions on the holding costs. We then focus on the long-run average problem and show that the optimal static policy performs as well as the optimal dynamic policy when the buffer size at station 1 becomes large, there are no holding costs, and the arrival rate is either small or large. We learn from numerical results that for systems with small arrival rates and no holding cost, the optimal static policy produces a gain quite close to the optimal gain even when the buffer at station 1 is small. On the other hand, for systems with arrival rates that are not small, there are cases where the optimal dynamic policy performs much better than the optimal static policy.</description><subject>Buffers</subject><subject>Business and Management</subject><subject>Communications systems</subject><subject>Computer Communication Networks</subject><subject>Control</subject><subject>Customers</subject><subject>Markov analysis</subject><subject>Operations Research/Decision Theory</subject><subject>Poisson density functions</subject><subject>Pricing</subject><subject>Pricing policies</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Profits</subject><subject>Queues</subject><subject>Queuing theory</subject><subject>Supply Chain Management</subject><subject>Systems Theory</subject><issn>0257-0130</issn><issn>1572-9443</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kEtPwzAQhC0EEqXwBzhZ4mxYP2LHR1QBRarUC5wtx4-Sqk2KnYjy7zEEiRunPezM7M6H0DWFWwqg7jKllAsCVBPQktbkeIJmtFKMaCH4KZoBq1RZczhHFzlvAUCySs9QtT4M7d7u8CG1ru02OPYJD7bzYY_fxzCGjD_a4Q3HtmuHgJsxxpDyJTqLdpfD1e-co9fHh5fFkqzWT8-L-xVxnOqBKO6DFjJS77xTrAkKbOM8E6HxMoCoHUTtGyGYbRQT1NVgNaPAqFIsSsnn6GbKPaS-fJMHs-3H1JWThjFeMwlCqaJik8qlPucUoill9jZ9GgrmG4-Z8JiCx_zgMcdi4pMpF3G3Cekv-h_XFyrGaBI</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Wang, Xinchang</creator><creator>Andradóttir, Sigrún</creator><creator>Ayhan, Hayriye</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PADUT</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-5434-7024</orcidid></search><sort><creationdate>20190801</creationdate><title>Optimal pricing for tandem queues with finite buffers</title><author>Wang, Xinchang ; Andradóttir, Sigrún ; Ayhan, Hayriye</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-73de946f1dcdc72be70abcd24ebd6e048c0f9db442ab7241c80a921021772f663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Buffers</topic><topic>Business and Management</topic><topic>Communications systems</topic><topic>Computer Communication Networks</topic><topic>Control</topic><topic>Customers</topic><topic>Markov analysis</topic><topic>Operations Research/Decision Theory</topic><topic>Poisson density functions</topic><topic>Pricing</topic><topic>Pricing policies</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Profits</topic><topic>Queues</topic><topic>Queuing theory</topic><topic>Supply Chain Management</topic><topic>Systems Theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xinchang</creatorcontrib><creatorcontrib>Andradóttir, Sigrún</creatorcontrib><creatorcontrib>Ayhan, Hayriye</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering 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><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Research Library China</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Queueing systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xinchang</au><au>Andradóttir, Sigrún</au><au>Ayhan, Hayriye</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal pricing for tandem queues with finite buffers</atitle><jtitle>Queueing systems</jtitle><stitle>Queueing Syst</stitle><date>2019-08-01</date><risdate>2019</risdate><volume>92</volume><issue>3-4</issue><spage>323</spage><epage>396</epage><pages>323-396</pages><issn>0257-0130</issn><eissn>1572-9443</eissn><abstract>We consider optimal pricing for a two-station tandem queueing system with finite buffers, communication blocking, and price-sensitive customers whose arrivals form a homogeneous Poisson process. The service provider quotes prices to incoming customers using either a static or dynamic pricing scheme. There may also be a holding cost for each customer in the system. The objective is to maximize either the discounted profit over an infinite planning horizon or the long-run average profit of the provider. We show that there exists an optimal dynamic policy that exhibits a monotone structure, in which the quoted price is non-decreasing in the queue length at either station and is non-increasing if a customer moves from station 1 to 2, for both the discounted and long-run average problems under certain conditions on the holding costs. We then focus on the long-run average problem and show that the optimal static policy performs as well as the optimal dynamic policy when the buffer size at station 1 becomes large, there are no holding costs, and the arrival rate is either small or large. We learn from numerical results that for systems with small arrival rates and no holding cost, the optimal static policy produces a gain quite close to the optimal gain even when the buffer at station 1 is small. On the other hand, for systems with arrival rates that are not small, there are cases where the optimal dynamic policy performs much better than the optimal static policy.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11134-019-09618-x</doi><tpages>74</tpages><orcidid>https://orcid.org/0000-0001-5434-7024</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0257-0130 |
ispartof | Queueing systems, 2019-08, Vol.92 (3-4), p.323-396 |
issn | 0257-0130 1572-9443 |
language | eng |
recordid | cdi_proquest_journals_2238260477 |
source | SpringerLink Journals - AutoHoldings |
subjects | Buffers Business and Management Communications systems Computer Communication Networks Control Customers Markov analysis Operations Research/Decision Theory Poisson density functions Pricing Pricing policies Probability Theory and Stochastic Processes Profits Queues Queuing theory Supply Chain Management Systems Theory |
title | Optimal pricing for tandem queues with finite buffers |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T02%3A40%3A15IST&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=Optimal%20pricing%20for%20tandem%20queues%20with%20finite%20buffers&rft.jtitle=Queueing%20systems&rft.au=Wang,%20Xinchang&rft.date=2019-08-01&rft.volume=92&rft.issue=3-4&rft.spage=323&rft.epage=396&rft.pages=323-396&rft.issn=0257-0130&rft.eissn=1572-9443&rft_id=info:doi/10.1007/s11134-019-09618-x&rft_dat=%3Cproquest_cross%3E2238260477%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=2238260477&rft_id=info:pmid/&rfr_iscdi=true |