An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: The outer approximation with equality relaxation and augmented penalty algorithm
It is of great importance to develop an optimal supply chain (SC) batch-sizing policy that collectively embodies green policies and a vendor-managed inventory (VMI) with consignment stock (CS) agreement. This article provides a mathematical model that includes the buyers' total cost (TC) and th...
Uložené v:
| Vydané v: | Applied Mathematical Modelling Ročník 69; s. 223 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Elsevier BV
01.05.2019
|
| Predmet: | |
| ISSN: | 1088-8691, 0307-904X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | It is of great importance to develop an optimal supply chain (SC) batch-sizing policy that collectively embodies green policies and a vendor-managed inventory (VMI) with consignment stock (CS) agreement. This article provides a mathematical model that includes the buyers' total cost (TC) and the vendor's TC in an SC under penalty, green, and quality control (QC) policies and a VMI-CS agreement. The proposed model is a multiproduct, multi-buyer model and has real stochastic constraints. Moreover, the model differentiates between the holding costs for financial and nonfinancial components, in which the first includes the investment in the space and the second includes the cost due to physical storage, movement, and insurance of the products. Financial components are carried by the vendor on implementation of the VMI-CS agreement, while holding costs for stocking items in the buyers' warehouses are carried by the buyers as nonfinancial components. The objective is to determine the optimal batch-sizing policy with the minimum TC in the integrated SC that finds both the number of the vendor's batches for each of the transported products and the volume of the batches transported to the buyers so as to minimize the TC of the integrated SC while the stochastic constraints are satisfied. Because of the complexity of the optimization model and mathematical formulations, an outer approximation with equality relaxation and augmented penalty algorithm is presented to determine the optimal batch-sizing policy. With application of this technique, the large-scale and hard-to-solve mixed-integer nonlinear programming problem is minimized. The optimality criteria results obtained in numerical examples and sensitivity analysis demonstrate the excellent performance of the method used. Finally, managerial insights, analytical results, and future research directions are provided. |
|---|---|
| AbstractList | It is of great importance to develop an optimal supply chain (SC) batch-sizing policy that collectively embodies green policies and a vendor-managed inventory (VMI) with consignment stock (CS) agreement. This article provides a mathematical model that includes the buyers' total cost (TC) and the vendor's TC in an SC under penalty, green, and quality control (QC) policies and a VMI-CS agreement. The proposed model is a multiproduct, multi-buyer model and has real stochastic constraints. Moreover, the model differentiates between the holding costs for financial and nonfinancial components, in which the first includes the investment in the space and the second includes the cost due to physical storage, movement, and insurance of the products. Financial components are carried by the vendor on implementation of the VMI-CS agreement, while holding costs for stocking items in the buyers' warehouses are carried by the buyers as nonfinancial components. The objective is to determine the optimal batch-sizing policy with the minimum TC in the integrated SC that finds both the number of the vendor's batches for each of the transported products and the volume of the batches transported to the buyers so as to minimize the TC of the integrated SC while the stochastic constraints are satisfied. Because of the complexity of the optimization model and mathematical formulations, an outer approximation with equality relaxation and augmented penalty algorithm is presented to determine the optimal batch-sizing policy. With application of this technique, the large-scale and hard-to-solve mixed-integer nonlinear programming problem is minimized. The optimality criteria results obtained in numerical examples and sensitivity analysis demonstrate the excellent performance of the method used. Finally, managerial insights, analytical results, and future research directions are provided. |
| Author | Gharaei, Abolfazl Karimi, Mostafa Shekarabi, Seyed Ashkan Hoseini |
| Author_xml | – sequence: 1 givenname: Abolfazl surname: Gharaei fullname: Gharaei, Abolfazl – sequence: 2 givenname: Mostafa surname: Karimi fullname: Karimi, Mostafa – sequence: 3 givenname: Seyed surname: Shekarabi middlename: Ashkan Hoseini fullname: Shekarabi, Seyed Ashkan Hoseini |
| BookMark | eNo1kUlv2zAQhYkiBeqk_QG9DdCrpXCRZbG3IOgSIEAuCdCbQZGUTJciFS5N_NN7C73kNJg38-Z7wFyiC-edRugrwTXBpL3e1WKeaopJVxNSY7b6gBaY4XXFcfPnAi0I7rqqazn5hC5j3GFMaNfiBfp_48C4pMcgklYwZZtMNQevskzLc9vnvQ4Q8zzbPcitMA6yU0WatRM27ZcwBq3dEoRT8JyFNanseZeCtzB7a6SOx5mAf9opH2ASTowFZ1wRkg97eDFpe_BEM7qpaBCTl39BHC4f-u_wuNXgcypYMZeAr2YSyXh3cup3bNBWvJ4GR2IeD-6COmcFYUcfimX6jD4Owkb95Vyv0NPPH4-3v6v7h193tzf3lWR0lSpFetZ0TK97umZMtYMkjaIDH2SLlcC8kV3PGeecqabhK76SQ8tp35JBUK4wY1fo2-luCf2cdUybnc-hhIkbSstb1oQ1lL0B4qmUig |
| CitedBy_id | crossref_primary_10_1016_j_knosys_2019_04_019 crossref_primary_10_1016_j_jclepro_2020_123307 crossref_primary_10_1016_j_matpr_2021_06_282 crossref_primary_10_1080_23311916_2021_1930493 crossref_primary_10_1016_j_cie_2019_06_047 crossref_primary_10_1016_j_knosys_2020_105982 crossref_primary_10_1016_j_jclepro_2020_122572 crossref_primary_10_1080_00207543_2020_1727040 crossref_primary_10_1016_j_asoc_2019_02_039 crossref_primary_10_1080_23311975_2020_1826718 crossref_primary_10_1016_j_jclepro_2019_117894 crossref_primary_10_1016_j_jclepro_2020_121809 crossref_primary_10_1016_j_cie_2019_106200 crossref_primary_10_1016_j_jretconser_2020_102137 crossref_primary_10_3390_su12239919 crossref_primary_10_1016_j_conbuildmat_2022_127272 crossref_primary_10_1080_23311916_2021_2018929 crossref_primary_10_1007_s41660_024_00457_9 crossref_primary_10_1080_23302674_2021_1941394 crossref_primary_10_1016_j_ejor_2020_08_015 crossref_primary_10_1080_23302674_2021_1962428 crossref_primary_10_1155_2023_9293749 crossref_primary_10_1108_JBIM_07_2020_0321 crossref_primary_10_1007_s10479_021_04118_9 crossref_primary_10_1016_j_jclepro_2020_120376 crossref_primary_10_1016_j_jclepro_2020_122554 crossref_primary_10_1016_j_foodcont_2024_111003 crossref_primary_10_1016_j_knosys_2020_105522 crossref_primary_10_1016_j_knosys_2020_106177 crossref_primary_10_1016_j_cie_2019_06_023 crossref_primary_10_1007_s10479_022_05119_y crossref_primary_10_1080_23302674_2021_1958023 crossref_primary_10_1016_j_cie_2019_106103 crossref_primary_10_1016_j_eswa_2024_124162 crossref_primary_10_1007_s10479_022_04549_y crossref_primary_10_1007_s12351_025_00933_1 crossref_primary_10_1016_j_jclepro_2020_123411 crossref_primary_10_1016_j_jclepro_2020_120025 crossref_primary_10_1016_j_cie_2019_106206 crossref_primary_10_1016_j_cie_2019_106207 crossref_primary_10_1016_j_cie_2019_06_038 crossref_primary_10_1016_j_knosys_2020_105630 crossref_primary_10_1080_23322039_2021_1922179 crossref_primary_10_1016_j_knosys_2022_108664 crossref_primary_10_1016_j_jclepro_2020_121231 crossref_primary_10_1007_s10878_023_01002_z crossref_primary_10_1016_j_knosys_2020_105746 crossref_primary_10_1080_23311975_2020_1760477 crossref_primary_10_1080_23302674_2025_2452365 crossref_primary_10_1016_j_jclepro_2019_06_156 crossref_primary_10_1016_j_jclepro_2019_118279 crossref_primary_10_1080_23311916_2020_1865598 crossref_primary_10_1016_j_jclepro_2019_04_167 crossref_primary_10_1016_j_knosys_2020_106352 crossref_primary_10_1016_j_jclepro_2020_120510 crossref_primary_10_1016_j_asoc_2020_106811 crossref_primary_10_1016_j_jclepro_2019_06_329 crossref_primary_10_1108_JM2_09_2019_0230 crossref_primary_10_1016_j_jclepro_2020_120641 crossref_primary_10_1016_j_jclepro_2020_122268 crossref_primary_10_1016_j_eswa_2020_114549 crossref_primary_10_1007_s10479_021_03990_9 crossref_primary_10_1016_j_jclepro_2020_122382 crossref_primary_10_1016_j_jclepro_2019_01_231 crossref_primary_10_1016_j_jclepro_2020_124441 crossref_primary_10_1016_j_asoc_2022_108670 crossref_primary_10_1080_01605682_2024_2407467 crossref_primary_10_1016_j_knosys_2019_105094 crossref_primary_10_1016_j_jii_2023_100440 crossref_primary_10_1016_j_knosys_2021_107607 crossref_primary_10_1016_j_jclepro_2019_04_096 crossref_primary_10_1016_j_jclepro_2020_122235 crossref_primary_10_1080_21681015_2022_2107097 crossref_primary_10_1016_j_cie_2019_05_025 crossref_primary_10_1007_s40815_021_01209_4 crossref_primary_10_1079_foodsciencecases_2024_0003 crossref_primary_10_1080_0305215X_2021_2012658 crossref_primary_10_1016_j_cie_2019_04_005 crossref_primary_10_1016_j_knosys_2020_106484 crossref_primary_10_1016_j_cie_2019_04_008 crossref_primary_10_1016_j_jclepro_2020_122360 crossref_primary_10_1016_j_cie_2021_107558 crossref_primary_10_1016_j_jclepro_2020_123454 crossref_primary_10_1155_2020_8815983 crossref_primary_10_1016_j_compchemeng_2024_108725 crossref_primary_10_1016_j_jclepro_2020_120627 crossref_primary_10_1016_j_cie_2019_05_032 crossref_primary_10_1007_s10479_023_05812_6 crossref_primary_10_1016_j_cie_2020_106293 crossref_primary_10_1016_j_sftr_2025_101175 crossref_primary_10_1016_j_knosys_2019_06_021 crossref_primary_10_1080_23302674_2022_2083254 crossref_primary_10_1007_s41660_021_00159_6 crossref_primary_10_1016_j_apenergy_2023_122380 crossref_primary_10_1016_j_knosys_2020_106676 crossref_primary_10_1016_j_eswa_2021_114576 crossref_primary_10_1016_j_knosys_2020_106556 crossref_primary_10_1016_j_jclepro_2019_01_141 crossref_primary_10_1016_j_knosys_2021_107486 crossref_primary_10_1016_j_cie_2019_106040 crossref_primary_10_1016_j_jclepro_2021_127230 crossref_primary_10_1080_23270012_2022_2030255 crossref_primary_10_1080_23302674_2022_2070296 crossref_primary_10_1080_23311975_2022_2155003 crossref_primary_10_1016_j_knosys_2020_106427 crossref_primary_10_1016_j_jclepro_2020_121529 crossref_primary_10_1080_23311975_2022_2143008 crossref_primary_10_1007_s41660_023_00338_7 crossref_primary_10_1016_j_cie_2020_106286 crossref_primary_10_1016_j_cie_2023_109056 crossref_primary_10_3390_su12104108 crossref_primary_10_1016_j_knosys_2020_106546 crossref_primary_10_2478_fcds_2022_0023 crossref_primary_10_11144_Javeriana_cao34_gscmf crossref_primary_10_1016_j_eswa_2021_115650 crossref_primary_10_1016_j_apm_2022_02_003 crossref_primary_10_3934_GF_2020005 crossref_primary_10_1080_02286203_2025_2479003 crossref_primary_10_1088_1757_899X_1116_1_012094 crossref_primary_10_1007_s10479_020_03876_2 crossref_primary_10_1016_j_jclepro_2020_122627 crossref_primary_10_1061_JCEMD4_COENG_15109 crossref_primary_10_1016_j_cie_2020_106274 crossref_primary_10_1016_j_cie_2019_106033 crossref_primary_10_1080_2331205X_2021_2012888 crossref_primary_10_1016_j_cie_2019_07_002 crossref_primary_10_1016_j_jclepro_2020_120774 crossref_primary_10_1080_23311975_2020_1870807 crossref_primary_10_1016_j_jclepro_2020_121744 crossref_primary_10_1016_j_sca_2023_100029 crossref_primary_10_1016_j_knosys_2021_107262 crossref_primary_10_1080_23302674_2021_1914767 crossref_primary_10_1016_j_knosys_2020_105486 crossref_primary_10_1016_j_jclepro_2020_122156 crossref_primary_10_1007_s10479_021_04345_0 crossref_primary_10_1080_23311975_2021_1930500 crossref_primary_10_1016_j_cie_2019_106062 crossref_primary_10_1016_j_cie_2019_106180 crossref_primary_10_1016_j_jclepro_2020_121747 crossref_primary_10_1016_j_jclepro_2019_04_215 crossref_primary_10_1007_s10489_021_02670_2 crossref_primary_10_1016_j_knosys_2020_106560 crossref_primary_10_1080_23311975_2021_1935185 crossref_primary_10_1016_j_jclepro_2020_120784 crossref_primary_10_1016_j_jclepro_2019_118674 crossref_primary_10_1155_2021_5584754 crossref_primary_10_1016_j_resconrec_2021_105445 crossref_primary_10_1016_j_cie_2019_106055 crossref_primary_10_3390_math11010042 crossref_primary_10_3390_su13137004 crossref_primary_10_1016_j_jclepro_2020_122654 crossref_primary_10_1080_23302674_2021_1921878 crossref_primary_10_1007_s10479_021_04361_0 crossref_primary_10_1016_j_apm_2022_09_033 crossref_primary_10_1016_j_jclepro_2020_122770 crossref_primary_10_1007_s10479_020_03720_7 crossref_primary_10_1080_23302674_2019_1701727 crossref_primary_10_1016_j_cie_2019_106000 crossref_primary_10_1016_j_esr_2022_100815 crossref_primary_10_1016_j_cie_2019_06_010 crossref_primary_10_1016_j_jclepro_2020_123752 crossref_primary_10_1016_j_knosys_2019_104966 crossref_primary_10_1007_s10845_019_01521_9 crossref_primary_10_1016_j_cie_2019_106229 crossref_primary_10_1016_j_cie_2019_06_019 crossref_primary_10_1016_j_jclepro_2020_123073 crossref_primary_10_1016_j_knosys_2020_105530 crossref_primary_10_3390_math9050495 crossref_primary_10_3846_tede_2022_17913 crossref_primary_10_1080_23302674_2019_1656296 crossref_primary_10_3390_logistics5020037 crossref_primary_10_1016_j_cie_2019_106237 crossref_primary_10_1016_j_jclepro_2022_135175 crossref_primary_10_1080_23302674_2021_1919336 crossref_primary_10_1016_j_knosys_2021_107467 crossref_primary_10_1016_j_jclepro_2020_120333 crossref_primary_10_1155_2020_3083761 crossref_primary_10_1016_j_jclepro_2020_120578 crossref_primary_10_1016_j_jclepro_2020_122757 crossref_primary_10_1016_j_knosys_2021_106811 crossref_primary_10_1007_s00500_023_09060_5 crossref_primary_10_1007_s10660_024_09878_7 crossref_primary_10_1016_j_knosys_2020_106651 crossref_primary_10_1007_s10479_023_05793_6 crossref_primary_10_1016_j_cie_2019_106141 crossref_primary_10_1016_j_cie_2019_07_038 crossref_primary_10_1016_j_jclepro_2019_04_257 crossref_primary_10_1016_j_cie_2019_106027 crossref_primary_10_1016_j_jclepro_2022_134098 crossref_primary_10_1016_j_cie_2019_07_033 crossref_primary_10_3390_en13215744 crossref_primary_10_1016_j_jretconser_2024_103887 crossref_primary_10_1080_00207543_2019_1696491 crossref_primary_10_1016_j_jclepro_2019_118193 crossref_primary_10_1016_j_jclepro_2020_123293 crossref_primary_10_1016_j_jclepro_2019_03_214 crossref_primary_10_1016_j_rineng_2023_101609 crossref_primary_10_1016_j_jclepro_2019_05_049 crossref_primary_10_1007_s40747_022_00642_8 crossref_primary_10_1080_23302674_2019_1646835 crossref_primary_10_1016_j_cie_2020_106335 crossref_primary_10_1016_j_jclepro_2019_05_280 crossref_primary_10_1016_j_cie_2019_106014 crossref_primary_10_1080_23311975_2021_1906052 crossref_primary_10_1016_j_cie_2019_03_042 crossref_primary_10_1080_23302674_2021_2015007 |
| ContentType | Journal Article |
| Copyright | Copyright Elsevier BV May 2019 |
| Copyright_xml | – notice: Copyright Elsevier BV May 2019 |
| DBID | 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.apm.2018.11.035 |
| DatabaseName | Computer and Information Systems Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Computer and Information Systems Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Psychology Mathematics |
| EISSN | 0307-904X |
| GroupedDBID | -W8 -~X .7I .GO .QK 0BK 0R~ 23M 2DF 4.4 53G 5GY 6J9 7SC 8FD 8VB AAGDL AAGZJ AAHIA AAHSB AAMFJ AAMIU AAPUL AATTQ AAZMC ABCCY ABDBF ABFIM ABIVO ABJNI ABLIJ ABPEM ABRYG ABTAI ABXUL ABXYU ABZLS ACGFS ACGOD ACHQT ACTIO ACTOA ACUHS ADAHI ADCVX ADKVQ ADYSH AECIN AEFOU AEGXH AEISY AEKEX AEMOZ AEMXT AEOZL AEPSL AEYOC AEZRU AFHDM AFRVT AGDLA AGMYJ AGRBW AHDZW AHQJS AIJEM AIYEW AJWEG AKBVH AKVCP ALMA_UNASSIGNED_HOLDINGS ALQZU AVBZW AWYRJ BEJHT BLEHA BMOTO BOHLJ CCCUG CQ1 CS3 DGFLZ DKSSO EAP EBR EBS EBU EDJ EJD EMK EPL EPS EST ESX E~B E~C F5P FEDTE G-F GTTXZ H13 HF~ HVGLF HZ~ J.O JQ2 K1G KYCEM L7M LJTGL L~C L~D M4Z NA5 O9- P2P PQQKQ QWB RNANH ROSJB RSYQP S-F STATR TASJS TBQAZ TDBHL TEH TFH TFL TFW TH9 TNTFI TRJHH TUROJ TUS TWZ UPT UT5 UT9 VAE ZL0 ~01 ~S~ |
| ID | FETCH-LOGICAL-c325t-d1b3483e7b2733d6fc14d2f9fc60da094c8b939993d449595cf692b61fa29d033 |
| ISICitedReferencesCount | 230 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000461728500014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1088-8691 |
| IngestDate | Mon Jul 14 07:47:57 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c325t-d1b3483e7b2733d6fc14d2f9fc60da094c8b939993d449595cf692b61fa29d033 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2210871342 |
| PQPubID | 2045280 |
| ParticipantIDs | proquest_journals_2210871342 |
| PublicationCentury | 2000 |
| PublicationDate | 20190501 |
| PublicationDateYYYYMMDD | 2019-05-01 |
| PublicationDate_xml | – month: 05 year: 2019 text: 20190501 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Applied Mathematical Modelling |
| PublicationYear | 2019 |
| Publisher | Elsevier BV |
| Publisher_xml | – name: Elsevier BV |
| SSID | ssj0012860 ssj0005904 |
| Score | 2.6264594 |
| Snippet | It is of great importance to develop an optimal supply chain (SC) batch-sizing policy that collectively embodies green policies and a vendor-managed inventory... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| StartPage | 223 |
| SubjectTerms | Agreements Algorithms Approximation Constraint modelling Costs Inventory management Mathematical analysis Nonlinear programming Optimality criteria Optimization Policies Quality control Sensitivity analysis Sizing Supply chains Warehouses |
| Title | An integrated multi-product, multi-buyer supply chain under penalty, green, and quality control polices and a vendor managed inventory with consignment stock agreement: The outer approximation with equality relaxation and augmented penalty algorithm |
| URI | https://www.proquest.com/docview/2210871342 |
| Volume | 69 |
| WOSCitedRecordID | wos000461728500014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAWR databaseName: Taylor & Francis Journals Complete customDbUrl: eissn: 0307-904X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012860 issn: 1088-8691 databaseCode: TFW dateStart: 19970301 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtZ3Lb9NAEIdXbeFQDggKiEdBc0BcEkd-28stQo0qUQJSU8gt8mOdhgQ75FEl_OfcmNmHnVAJwYGLFXm1XjvzeV_-zQxjr-OYO6lXhJaIgsjyQyezuMD30StyhydhnNm-SjYR9fvxcMg_HRz6xhfmZhaVZbzZ8Pl_NTWeQ2OT6-w_mLu-KJ7A32h0PKLZ8fhXhu-WTQyIXAkGrbmK60r_pzqRrrcUxplSem7J-XdSyoS4i9ZcYAMqUcB4oZPW0966cr7c1tJ2Su5AYi4Z67WFPWZeLbQUlqI5kYqSvt4bZTvJRKTsAOea2bSV0LXNviSRWlFmCRXgfDNR3pSqrjANk9PNRhXINtdjGU40N3fcSmbjaoFVvu3Ot80k-0MdnZZ8kSn7z8yM2SQ-oqjVQuoaumk1K5Ifte7kfUJ5z-S2cYUT6aIexS6vxRRrpbLsUmyxke7yeord5Xm1FJNysrufQi5cwe5-SuPo83lnWMC-2IpDlVesI7S_mR1Z3Fb6UjOWqLQzZjBQntS3Bim1X_K1k8wpFIITdyiMrArash8QvP9x1Lu6uBgNzoaDN_PvFuVKI02BThxzyO64UcCpLx_0vjTCJk4rbP0hzY2Vo7x5APNhX0ocf7uBW9MROccaPGD39eIIugrqh-xAlCfsXmO75Qk7rkfs7SP2s1tCwzrssd6GHdJBkQ6SdJCkg-amDZLzNiBVoGEDTTloymVZAopy0JRDTTkQqbBDOUjKoab8LSDjIBmHPcZVTcM4NIyrFg3j5l6hZvwxu-qdDd6dWzqXiZV5brCycuwR_dgTUYrrBS8Pi8zxc7fgRRbaeWJzP4tTjosF7uW-zwMeZEXI3TR0isTlue15T9hRWZXiKYOAxACZb3t5GvhFnGMl3-auk4SFG_hR8YydGjOOdL-0HLkuIkB-4-7zPxe_YMfNW3HKjlaLtXjJ7mY3q8ly8UqS9gtJPu3Y |
| linkProvider | Taylor & Francis |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+integrated+multi-product%2C+multi-buyer+supply+chain+under+penalty%2C+green%2C+and+quality+control+polices+and+a+vendor+managed+inventory+with+consignment+stock+agreement%3A+The+outer+approximation+with+equality+relaxation+and+augmented+penalty+algorithm&rft.jtitle=Applied+Mathematical+Modelling&rft.au=Gharaei%2C+Abolfazl&rft.au=Karimi%2C+Mostafa&rft.au=Shekarabi%2C+Seyed+Ashkan+Hoseini&rft.date=2019-05-01&rft.pub=Elsevier+BV&rft.issn=1088-8691&rft.eissn=0307-904X&rft.volume=69&rft.spage=223&rft_id=info:doi/10.1016%2Fj.apm.2018.11.035&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1088-8691&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1088-8691&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1088-8691&client=summon |