A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry

This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assemb...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sustainability 2022-02, Vol.14 (4), p.2408
Hauptverfasser: Fadda, Edoardo, Perboli, Guido, Rosano, Mariangela, Mascolo, Julien Etienne, Masera, Davide
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 4
container_start_page 2408
container_title Sustainability
container_volume 14
creator Fadda, Edoardo
Perboli, Guido
Rosano, Mariangela
Mascolo, Julien Etienne
Masera, Davide
description This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion.
doi_str_mv 10.3390/su14042408
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2633188592</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2633188592</sourcerecordid><originalsourceid>FETCH-LOGICAL-c295t-fdd6951d917e1d1f5d5f17656746dedab8db9d60f282d37f3599a6ad74bb4fb13</originalsourceid><addsrcrecordid>eNpNUNtKAzEUDKJgqX3xCwK-Cau5bLKbx6XeCgWF6vOSzaWmtJs1F6F_79Yqel7OcJg5wwwAlxjdUCrQbcy4RCUpUX0CJgRVuMCIodN_-BzMYtygcSjFAvMJUA28M8pF53u4ysPgQ4KrfUxmB60PvyfXr-EqBZnM2in4ErzOKh0kzXbrlfyGrofp3cAmJ7_zyX0auOh1jinsL8CZldtoZj97Ct4e7l_nT8Xy-XExb5aFIoKlwmrNBcNa4MpgjS3TzOKKM16VXBstu1p3QnNkSU00rSxlQkgudVV2XWk7TKfg6vh3CP4jm5jajc-hHy1bwsfAdc0EGVnXR5YKPsZgbDsEt5Nh32LUHnps_3qkX9wwZpA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2633188592</pqid></control><display><type>article</type><title>A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Fadda, Edoardo ; Perboli, Guido ; Rosano, Mariangela ; Mascolo, Julien Etienne ; Masera, Davide</creator><creatorcontrib>Fadda, Edoardo ; Perboli, Guido ; Rosano, Mariangela ; Mascolo, Julien Etienne ; Masera, Davide</creatorcontrib><description>This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14042408</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Artificial intelligence ; Automobile industry ; Automotive engineering ; Aversion ; Decision making ; Decision support systems ; Knowledge ; Manufacturing ; Manufacturing engineering ; Mathematical models ; Optimization ; Probability distribution ; Risk aversion ; Stochasticity ; Supply chains ; Sustainability</subject><ispartof>Sustainability, 2022-02, Vol.14 (4), p.2408</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-fdd6951d917e1d1f5d5f17656746dedab8db9d60f282d37f3599a6ad74bb4fb13</citedby><cites>FETCH-LOGICAL-c295t-fdd6951d917e1d1f5d5f17656746dedab8db9d60f282d37f3599a6ad74bb4fb13</cites><orcidid>0000-0002-5599-6349 ; 0000-0002-6879-827X ; 0000-0001-6900-9917</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Fadda, Edoardo</creatorcontrib><creatorcontrib>Perboli, Guido</creatorcontrib><creatorcontrib>Rosano, Mariangela</creatorcontrib><creatorcontrib>Mascolo, Julien Etienne</creatorcontrib><creatorcontrib>Masera, Davide</creatorcontrib><title>A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry</title><title>Sustainability</title><description>This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion.</description><subject>Artificial intelligence</subject><subject>Automobile industry</subject><subject>Automotive engineering</subject><subject>Aversion</subject><subject>Decision making</subject><subject>Decision support systems</subject><subject>Knowledge</subject><subject>Manufacturing</subject><subject>Manufacturing engineering</subject><subject>Mathematical models</subject><subject>Optimization</subject><subject>Probability distribution</subject><subject>Risk aversion</subject><subject>Stochasticity</subject><subject>Supply chains</subject><subject>Sustainability</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUNtKAzEUDKJgqX3xCwK-Cau5bLKbx6XeCgWF6vOSzaWmtJs1F6F_79Yqel7OcJg5wwwAlxjdUCrQbcy4RCUpUX0CJgRVuMCIodN_-BzMYtygcSjFAvMJUA28M8pF53u4ysPgQ4KrfUxmB60PvyfXr-EqBZnM2in4ErzOKh0kzXbrlfyGrofp3cAmJ7_zyX0auOh1jinsL8CZldtoZj97Ct4e7l_nT8Xy-XExb5aFIoKlwmrNBcNa4MpgjS3TzOKKM16VXBstu1p3QnNkSU00rSxlQkgudVV2XWk7TKfg6vh3CP4jm5jajc-hHy1bwsfAdc0EGVnXR5YKPsZgbDsEt5Nh32LUHnps_3qkX9wwZpA</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Fadda, Edoardo</creator><creator>Perboli, Guido</creator><creator>Rosano, Mariangela</creator><creator>Mascolo, Julien Etienne</creator><creator>Masera, Davide</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-5599-6349</orcidid><orcidid>https://orcid.org/0000-0002-6879-827X</orcidid><orcidid>https://orcid.org/0000-0001-6900-9917</orcidid></search><sort><creationdate>20220201</creationdate><title>A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry</title><author>Fadda, Edoardo ; Perboli, Guido ; Rosano, Mariangela ; Mascolo, Julien Etienne ; Masera, Davide</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-fdd6951d917e1d1f5d5f17656746dedab8db9d60f282d37f3599a6ad74bb4fb13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial intelligence</topic><topic>Automobile industry</topic><topic>Automotive engineering</topic><topic>Aversion</topic><topic>Decision making</topic><topic>Decision support systems</topic><topic>Knowledge</topic><topic>Manufacturing</topic><topic>Manufacturing engineering</topic><topic>Mathematical models</topic><topic>Optimization</topic><topic>Probability distribution</topic><topic>Risk aversion</topic><topic>Stochasticity</topic><topic>Supply chains</topic><topic>Sustainability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fadda, Edoardo</creatorcontrib><creatorcontrib>Perboli, Guido</creatorcontrib><creatorcontrib>Rosano, Mariangela</creatorcontrib><creatorcontrib>Mascolo, Julien Etienne</creatorcontrib><creatorcontrib>Masera, Davide</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fadda, Edoardo</au><au>Perboli, Guido</au><au>Rosano, Mariangela</au><au>Mascolo, Julien Etienne</au><au>Masera, Davide</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry</atitle><jtitle>Sustainability</jtitle><date>2022-02-01</date><risdate>2022</risdate><volume>14</volume><issue>4</issue><spage>2408</spage><pages>2408-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>This paper deals with the optimization problem faced by the manufacturing engineering department of an international automotive company, concerning its supply chain design (i.e., decisions regarding which plants to open, how many components to produce, and the logistic flow from production to assembly plants). The intrinsic characteristics of the problem, such as stochasticity, the high number of products and components, and exogenous factors, make it complex to formulate and solve the mathematical models. Thus, new decision support systems integrating human choices and fast solution algorithms are needed. In this paper, we present an innovative and successful use case of such an approach, encompassing the decision-maker as an integral part of the optimization process. Moreover, the proposed approach allows the managers to conduct what-if analyses in real-time, taking robust decisions with respect to future scenarios, while shortening the time needed. As a byproduct, the proposed methodology requires neither the definition of a probability distribution nor the investigation of the user’s risk aversion.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su14042408</doi><orcidid>https://orcid.org/0000-0002-5599-6349</orcidid><orcidid>https://orcid.org/0000-0002-6879-827X</orcidid><orcidid>https://orcid.org/0000-0001-6900-9917</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2071-1050
ispartof Sustainability, 2022-02, Vol.14 (4), p.2408
issn 2071-1050
2071-1050
language eng
recordid cdi_proquest_journals_2633188592
source MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals
subjects Artificial intelligence
Automobile industry
Automotive engineering
Aversion
Decision making
Decision support systems
Knowledge
Manufacturing
Manufacturing engineering
Mathematical models
Optimization
Probability distribution
Risk aversion
Stochasticity
Supply chains
Sustainability
title A Decision Support System for Supporting Strategic Production Allocation in the Automotive Industry
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T03%3A36%3A19IST&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%20Decision%20Support%20System%20for%20Supporting%20Strategic%20Production%20Allocation%20in%20the%20Automotive%20Industry&rft.jtitle=Sustainability&rft.au=Fadda,%20Edoardo&rft.date=2022-02-01&rft.volume=14&rft.issue=4&rft.spage=2408&rft.pages=2408-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su14042408&rft_dat=%3Cproquest_cross%3E2633188592%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=2633188592&rft_id=info:pmid/&rfr_iscdi=true