Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model
Nowadays, according to the importance of sustainability objectives in supply chain (SC) design and planning, in addition to the economic objective, the social, environmental, and customer-oriented considerations play a vital role in the SCs' performance. On the other hand, SC resilience will ca...
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Veröffentlicht in: | Environment, development and sustainability development and sustainability, 2024-11, Vol.26 (11), p.27485-27527 |
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description | Nowadays, according to the importance of sustainability objectives in supply chain (SC) design and planning, in addition to the economic objective, the social, environmental, and customer-oriented considerations play a vital role in the SCs' performance. On the other hand, SC resilience will cause sustainability to be less affected during disruption. This paper deals with the resilient sustainable supply chain design and planning (RSSCDP) problem under supply disruption risk. We developed a multi-objective robust model to solve the multi-product RSSCDP problem under various supply disruption scenarios. In this problem, the most important decisions related to SC designing and planning, such as facility location and determination of their capacities, supplier selection, inventory management, order allocation and transportation planning, lot sizing, and production planning, are taken into account. In the proposed model, four absorptive and adaptive resilience strategies are simultaneously applied, including (1) multiple sourcing, (2) backup supplier contracting, (3) raw material inventory pre-positioning, and (4) final product inventory pre-positioning. Also, a reasonable trading-off among four objectives, including economic, social, environmental, as well as customer-oriented objectives using the Lp-metric method, is made. In addition, a multi-objective robust scenario-based stochastic programming approach is employed to cope with the operational uncertainty of the parameters. Finally, some numerical studies of the RSSCDP problem are conducted to demonstrate the capabilities and effectiveness of the developed model. The results depict a 10 to 15 percent improvement in the objectives. The findings confirm that the proposed resilience strategies are efficient in mitigating supply disruptions and can maintain the SC sustainability under disruptions. |
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On the other hand, SC resilience will cause sustainability to be less affected during disruption. This paper deals with the resilient sustainable supply chain design and planning (RSSCDP) problem under supply disruption risk. We developed a multi-objective robust model to solve the multi-product RSSCDP problem under various supply disruption scenarios. In this problem, the most important decisions related to SC designing and planning, such as facility location and determination of their capacities, supplier selection, inventory management, order allocation and transportation planning, lot sizing, and production planning, are taken into account. In the proposed model, four absorptive and adaptive resilience strategies are simultaneously applied, including (1) multiple sourcing, (2) backup supplier contracting, (3) raw material inventory pre-positioning, and (4) final product inventory pre-positioning. Also, a reasonable trading-off among four objectives, including economic, social, environmental, as well as customer-oriented objectives using the Lp-metric method, is made. In addition, a multi-objective robust scenario-based stochastic programming approach is employed to cope with the operational uncertainty of the parameters. Finally, some numerical studies of the RSSCDP problem are conducted to demonstrate the capabilities and effectiveness of the developed model. The results depict a 10 to 15 percent improvement in the objectives. The findings confirm that the proposed resilience strategies are efficient in mitigating supply disruptions and can maintain the SC sustainability under disruptions.</description><identifier>ISSN: 1573-2975</identifier><identifier>ISSN: 1387-585X</identifier><identifier>EISSN: 1573-2975</identifier><identifier>DOI: 10.1007/s10668-023-03769-x</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Absorptivity ; Component and supplier management ; Customers ; Design ; Design optimization ; Disruption ; Earth and Environmental Science ; Ecology ; Economic Geology ; Economic Growth ; Economics ; Environment ; Environmental Economics ; Environmental Management ; inventories ; Inventory ; Inventory management ; Lot sizing ; Multiple objective analysis ; Optimization ; Optimization models ; Parameter robustness ; Parameter uncertainty ; Positioning ; Production planning ; Raw materials ; Resilience ; risk ; Robustness ; Stochastic programming ; Suppliers ; Supply ; supply chain ; Supply chain sustainability ; Supply chains ; Sustainability ; Sustainable Development ; transportation ; Transportation planning ; Uncertainty</subject><ispartof>Environment, development and sustainability, 2024-11, Vol.26 (11), p.27485-27527</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c303t-22fc396b900aaae161f31eab07cf8bd258cd316046fa172803226d48ed2a489a3</cites><orcidid>0000-0002-7677-0840 ; 0000-0002-7382-6351 ; 0000-0002-4602-2710</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/s10668-023-03769-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10668-023-03769-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Safari, Lida</creatorcontrib><creatorcontrib>Sadjadi, Seyed Jafar</creatorcontrib><creatorcontrib>Sobhani, Farzad Movahedi</creatorcontrib><title>Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model</title><title>Environment, development and sustainability</title><addtitle>Environ Dev Sustain</addtitle><description>Nowadays, according to the importance of sustainability objectives in supply chain (SC) design and planning, in addition to the economic objective, the social, environmental, and customer-oriented considerations play a vital role in the SCs' performance. On the other hand, SC resilience will cause sustainability to be less affected during disruption. This paper deals with the resilient sustainable supply chain design and planning (RSSCDP) problem under supply disruption risk. We developed a multi-objective robust model to solve the multi-product RSSCDP problem under various supply disruption scenarios. In this problem, the most important decisions related to SC designing and planning, such as facility location and determination of their capacities, supplier selection, inventory management, order allocation and transportation planning, lot sizing, and production planning, are taken into account. In the proposed model, four absorptive and adaptive resilience strategies are simultaneously applied, including (1) multiple sourcing, (2) backup supplier contracting, (3) raw material inventory pre-positioning, and (4) final product inventory pre-positioning. Also, a reasonable trading-off among four objectives, including economic, social, environmental, as well as customer-oriented objectives using the Lp-metric method, is made. In addition, a multi-objective robust scenario-based stochastic programming approach is employed to cope with the operational uncertainty of the parameters. Finally, some numerical studies of the RSSCDP problem are conducted to demonstrate the capabilities and effectiveness of the developed model. The results depict a 10 to 15 percent improvement in the objectives. The findings confirm that the proposed resilience strategies are efficient in mitigating supply disruptions and can maintain the SC sustainability under disruptions.</description><subject>Absorptivity</subject><subject>Component and supplier management</subject><subject>Customers</subject><subject>Design</subject><subject>Design optimization</subject><subject>Disruption</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Economic Geology</subject><subject>Economic Growth</subject><subject>Economics</subject><subject>Environment</subject><subject>Environmental Economics</subject><subject>Environmental Management</subject><subject>inventories</subject><subject>Inventory</subject><subject>Inventory management</subject><subject>Lot sizing</subject><subject>Multiple objective analysis</subject><subject>Optimization</subject><subject>Optimization models</subject><subject>Parameter robustness</subject><subject>Parameter uncertainty</subject><subject>Positioning</subject><subject>Production planning</subject><subject>Raw materials</subject><subject>Resilience</subject><subject>risk</subject><subject>Robustness</subject><subject>Stochastic programming</subject><subject>Suppliers</subject><subject>Supply</subject><subject>supply chain</subject><subject>Supply chain sustainability</subject><subject>Supply chains</subject><subject>Sustainability</subject><subject>Sustainable Development</subject><subject>transportation</subject><subject>Transportation planning</subject><subject>Uncertainty</subject><issn>1573-2975</issn><issn>1387-585X</issn><issn>1573-2975</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kctKxDAUhosoOI6-gKuAGzfRXDppu5TBGwwIoutw2qRjxjapSSuOT-Ljms4oigtXOYTv-3PCnyTHlJxRQrLzQIkQOSaMY8IzUeC3nWRCZxnHrMhmu7_m_eQghBUhjBRMTJKPex1MY7TtEViFwhB6MBbKRse565o1qp7iBVIRW9oN0zVgrbFLNFil_TemTPBD1xtnkTfhGQ1hRAC1Q9Mb7MqVrnrzGlMrbcEbh0sIWiHvyvgkctFszTts_NYp3RwmezU0QR99ndPk8eryYX6DF3fXt_OLBa444T1mrK54IcqCEADQVNCaUw0lyao6LxWb5ZXiVJBU1EAzlhPOmFBprhWDNC-AT5PTbW7n3cugQy9bE3ds4ie1G4LkdJaOfppH9OQPunKDt3G7SDGWZxEdKbalKu9C8LqWnTct-LWkRI5lyW1ZMpYlN2XJtyjxrRQibJfa_0T_Y30CJuSb_A</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>Safari, Lida</creator><creator>Sadjadi, Seyed Jafar</creator><creator>Sobhani, Farzad Movahedi</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7U6</scope><scope>8BJ</scope><scope>8FD</scope><scope>C1K</scope><scope>FQK</scope><scope>FR3</scope><scope>JBE</scope><scope>KR7</scope><scope>SOI</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-7677-0840</orcidid><orcidid>https://orcid.org/0000-0002-7382-6351</orcidid><orcidid>https://orcid.org/0000-0002-4602-2710</orcidid></search><sort><creationdate>20241101</creationdate><title>Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model</title><author>Safari, Lida ; Sadjadi, Seyed Jafar ; Sobhani, Farzad Movahedi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c303t-22fc396b900aaae161f31eab07cf8bd258cd316046fa172803226d48ed2a489a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Absorptivity</topic><topic>Component and supplier management</topic><topic>Customers</topic><topic>Design</topic><topic>Design optimization</topic><topic>Disruption</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Economic Geology</topic><topic>Economic Growth</topic><topic>Economics</topic><topic>Environment</topic><topic>Environmental Economics</topic><topic>Environmental Management</topic><topic>inventories</topic><topic>Inventory</topic><topic>Inventory management</topic><topic>Lot sizing</topic><topic>Multiple objective analysis</topic><topic>Optimization</topic><topic>Optimization models</topic><topic>Parameter robustness</topic><topic>Parameter uncertainty</topic><topic>Positioning</topic><topic>Production planning</topic><topic>Raw materials</topic><topic>Resilience</topic><topic>risk</topic><topic>Robustness</topic><topic>Stochastic programming</topic><topic>Suppliers</topic><topic>Supply</topic><topic>supply chain</topic><topic>Supply chain sustainability</topic><topic>Supply chains</topic><topic>Sustainability</topic><topic>Sustainable Development</topic><topic>transportation</topic><topic>Transportation planning</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Safari, Lida</creatorcontrib><creatorcontrib>Sadjadi, Seyed Jafar</creatorcontrib><creatorcontrib>Sobhani, Farzad Movahedi</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>International Bibliography of the Social Sciences</collection><collection>Engineering Research Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Environment, development and sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Safari, Lida</au><au>Sadjadi, Seyed Jafar</au><au>Sobhani, Farzad Movahedi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model</atitle><jtitle>Environment, development and sustainability</jtitle><stitle>Environ Dev Sustain</stitle><date>2024-11-01</date><risdate>2024</risdate><volume>26</volume><issue>11</issue><spage>27485</spage><epage>27527</epage><pages>27485-27527</pages><issn>1573-2975</issn><issn>1387-585X</issn><eissn>1573-2975</eissn><abstract>Nowadays, according to the importance of sustainability objectives in supply chain (SC) design and planning, in addition to the economic objective, the social, environmental, and customer-oriented considerations play a vital role in the SCs' performance. On the other hand, SC resilience will cause sustainability to be less affected during disruption. This paper deals with the resilient sustainable supply chain design and planning (RSSCDP) problem under supply disruption risk. We developed a multi-objective robust model to solve the multi-product RSSCDP problem under various supply disruption scenarios. In this problem, the most important decisions related to SC designing and planning, such as facility location and determination of their capacities, supplier selection, inventory management, order allocation and transportation planning, lot sizing, and production planning, are taken into account. In the proposed model, four absorptive and adaptive resilience strategies are simultaneously applied, including (1) multiple sourcing, (2) backup supplier contracting, (3) raw material inventory pre-positioning, and (4) final product inventory pre-positioning. Also, a reasonable trading-off among four objectives, including economic, social, environmental, as well as customer-oriented objectives using the Lp-metric method, is made. In addition, a multi-objective robust scenario-based stochastic programming approach is employed to cope with the operational uncertainty of the parameters. Finally, some numerical studies of the RSSCDP problem are conducted to demonstrate the capabilities and effectiveness of the developed model. The results depict a 10 to 15 percent improvement in the objectives. The findings confirm that the proposed resilience strategies are efficient in mitigating supply disruptions and can maintain the SC sustainability under disruptions.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10668-023-03769-x</doi><tpages>43</tpages><orcidid>https://orcid.org/0000-0002-7677-0840</orcidid><orcidid>https://orcid.org/0000-0002-7382-6351</orcidid><orcidid>https://orcid.org/0000-0002-4602-2710</orcidid></addata></record> |
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subjects | Absorptivity Component and supplier management Customers Design Design optimization Disruption Earth and Environmental Science Ecology Economic Geology Economic Growth Economics Environment Environmental Economics Environmental Management inventories Inventory Inventory management Lot sizing Multiple objective analysis Optimization Optimization models Parameter robustness Parameter uncertainty Positioning Production planning Raw materials Resilience risk Robustness Stochastic programming Suppliers Supply supply chain Supply chain sustainability Supply chains Sustainability Sustainable Development transportation Transportation planning Uncertainty |
title | Resilient and sustainable supply chain design and planning under supply disruption risk using a multi-objective scenario-based robust optimization model |
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