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    <completedYear>2019</completedYear>
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    <language>deu</language>
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    <type>bookpart</type>
    <publisherName>Fachverlag Deutscher Wirtschaftsdienst</publisherName>
    <publisherPlace>Köln</publisherPlace>
    <creatingCorporation/>
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    <belongsToBibliography>0</belongsToBibliography>
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    <title language="deu">Geoinformationssysteme für Logistikmanager: Eine praxisorientierte Einführung am Beispiel der Freeware QGIS</title>
    <parentTitle language="deu">Praxishandbuch Logistik: Erfolgreiche Logistik in Industrie, Handel und Dienstleistungsunternehmen</parentTitle>
    <identifier type="isbn">978-3-87156-340-9</identifier>
    <enrichment key="opus.import.data">@incollectionepub38358, year = 2019, month = Februar, publisher = Dt. Wirtschaftsdienst, author = Maximilian Lukesch and Florian Kellner, address = Köln, title = Geoinformationssysteme für Logistikmanager: Eine praxisorientierte Einführung am Beispiel der Freeware QGIS, booktitle = Praxishandbuch Logistik: Erfolgreiche Logistik in Industrie, Handel und Dienstleistungsunternehmen, abstract = Der Beitrag gibt eine prägnante Einführung in die Verwendung des frei verfügbaren Geoinformationssystems QGIS. Dabei fokussiert der Beitrag auf die Anwendungsmöglichkeiten von QGIS für die Belange der Distributionslogistik. Im Mittelpunkt des Beitrags steht ein Anwendungsfall, innerhalb dessen ein komplexes und auf realen Daten basierendes Distributionsnetzwerk visualisiert und auf beispielhafte Fragestellungen hin analysiert wird., keywords = Logistik, GIS, Geoinformationssysteme, Distributionslogistik, QGIS, url = https://epub.uni-regensburg.de/38358/</enrichment>
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    <author>Maximilian Lukesch</author>
    <author>Florian Kellner</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Logistik</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GIS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Geoinformationssysteme</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Distributionslogistik</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>QGIS</value>
    </subject>
  </doc>
  <doc>
    <id>2082</id>
    <completedYear>2019</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>505</pageFirst>
    <pageLast>522</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>272</volume>
    <type>article</type>
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    <title language="eng">An a posteriori decision support methodology for solving the multi-criteria supplier selection problem</title>
    <abstract language="eng">This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.</abstract>
    <parentTitle language="eng">European Journal of Operational Research</parentTitle>
    <identifier type="doi">10.1016/j.ejor.2018.06.044</identifier>
    <enrichment key="opus.import.data">@articleepub37534, author = Florian Kellner and Bernhard Lienland and Sebastian Utz, address = AMSTERDAM, year = 2019, number = 2, publisher = ELSEVIER SCIENCE BV, volume = 272, title = An a posteriori decision support methodology for solving the multi-criteria supplier selection problem, pages = 505–522, journal = European Journal of Operational Research, abstract = This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ’risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a nonstandard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company’s strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting. (C) 2018 Elsevier B.V. All rights reserved., keywords = ANALYTIC HIERARCHY PROCESS; PORTFOLIO SELECTION; CHAIN MANAGEMENT; ORDER ALLOCATION; MUTUAL FUNDS; SUSTAINABILITY; RISK; PERFORMANCE; CRITERIA; OPTIMIZATION; Supply chain management; Supplier selection; Sustainability; Logistics; Decision support systems, url = https://epub.uni-regensburg.de/37534/</enrichment>
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    <author>Florian Kellner</author>
    <author>Bernhard Lienland</author>
    <author>Sebastian Utz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Supply chain management; Supplier selection; Sustainability; Logistics; Decision support systems</value>
    </subject>
  </doc>
  <doc>
    <id>2083</id>
    <completedYear>2019</completedYear>
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    <language>eng</language>
    <pageFirst>296</pageFirst>
    <pageLast>313</pageLast>
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    <issue>1</issue>
    <volume>278</volume>
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    <title language="eng">Further insights into the allocation of greenhouse gas emissions to shipments in road freight transportation: the pollution routing game</title>
    <abstract language="eng">At present, there is no globally accepted standard for the allocation of greenhouse gas (GHG) emissions to shipments in road freight transportation. The only official international standard for emission calculation of transport operations is the European Norm EN-16258. However, even this norm still allows choosing from several alternative emission allocation schemes. This research aims to harmonize the process of GHG declarations for supply chains by identifying among all allocation units specified by EN-16258 the one that describes a shipment's contribution to GHG emissions best. For this purpose, concepts of the cooperative game theory are used. First, we develop three transport scenarios that allow studying a shipment's effect on GHG: a vehicle routing problem, a network flow model, and a mixed scenario. Our approach extends previous research projects because we take into account that shipment characteristics in terms of origin, destination, weight, and volume consume transport capacities to different degrees, impact the routing of the commercial vehicles and, thus, determine GHG. Second, we present the results of a computational study that bases on the introduced transport scenarios and that compares the allocation vectors resulting from the EN-16258 allocation rules with the Shapley value, which serves as a benchmark for a shipment's contribution to GHG. Furthermore, we show how often the EN-16258 allocation principles are in line with a set of game theoretical fairness criteria. The results indicate that the allocation unit ‘distance’ is the closest to the game theory benchmark and most often in line with game theoretical fairness criteria.</abstract>
    <parentTitle language="eng">European Journal of Operational Research</parentTitle>
    <identifier type="doi">10.1016/j.ejor.2019.04.007</identifier>
    <enrichment key="opus.import.data">@articleepub40107, author = Florian Kellner and Miriam Schneiderbauer, address = AMSTERDAM, year = 2019, publisher = ELSEVIER SCIENCE BV, number = 1, volume = 278, title = Further insights into the allocation of greenhouse gas emissions to shipments in road freight transportation: the pollution routing game, journal = European Journal of Operational Research, pages = 296–313, url = https://epub.uni-regensburg.de/40107/, keywords = DELIVERY PROBLEM; SUPPLY CHAINS; COST ALLOCATION; TIME WINDOWS; PICKUP; OPPORTUNITIES; SUGGESTIONS; REDUCTION; NUCLEOLUS; BENEFITS; OR in environment and climate change; Allocation; Road freight transportation; Cooperative game theory; EN-16258, abstract = At present, there is no globally accepted standard for the allocation of greenhouse gas (GHG) emissions to shipments in road freight transportation. The only official international standard for emission calculation of transport operations is the European Norm EN-16258. However, even this norm still allows choosing from several alternative emission allocation schemes. This research aims to harmonize the process of GHG declarations for supply chains by identifying among all allocation units specified by EN-16258 the one that describes a shipment’s contribution to GHG emissions best. For this purpose, concepts of the cooperative game theory are used. First, we develop three transport scenarios that allow studying a shipment’s effect on GHG: a vehicle routing problem, a network flow model, and a mixed scenario. Our approach extends previous research projects because we take into account that shipment characteristics in terms of origin, destination, weight, and volume consume transport capacities to different degrees, impact the routing of the commercial vehicles and, thus, determine GHG. Second, we present the results of a computational study that bases on the introduced transport scenarios and that compares the allocation vectors resulting from the EN-16258 allocation rules with the Shapley value, which serves as a benchmark for a shipment’s contribution to GHG. Furthermore, we show how often the EN-16258 allocation principles are in line with a set of game theoretical fairness criteria. The results indicate that the allocation unit ’distance’ is the closest to the game theory benchmark and most often in line with game theoretical fairness criteria. (C) 2019 Elsevier B.V. All rights reserved.</enrichment>
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    <author>Florian Kellner</author>
    <author>Miriam Schneiderbauer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OR in environment and climate change; Allocation; Road freight transportation; Cooperative game theory; EN-16258</value>
    </subject>
  </doc>
  <doc>
    <id>2121</id>
    <completedYear>2019</completedYear>
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    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>462</pageFirst>
    <pageLast>474</pageLast>
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    <issue/>
    <volume>214</volume>
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    <title language="eng">Sustainability in supplier selection and order allocation: combining integer variables with Markowitz portfolio theory</title>
    <abstract language="eng">This research presents a decision support methodology for the multi-criteria supplier selection and order allocation problem. The proposed approach supports purchasing managers in assembling mid-term supplier portfolios while making them aware of the trade-offs between the supplier sustainability, the purchasing costs, and the overall supply risk. First, we propose a multi-objective optimization model with three objectives: to maximize the supplier sustainability, to select the supplier portfolio with the lowest purchasing costs, and to minimize the supply risk. Our model extends existing mathematical approaches that follow the portfolio theory fathered by H. Markowitz by integrating the aspect ‘risk’ into the supplier selection problem. Secondly, since we allow for integer variables in our model—in contrast to the classical Markowitz portfolio theory—we use the ε-constraint method to visualize the efficient surface. The possibility of considering the non-dominated set of supplier portfolios is advantageous for purchasing managers as they gain a picture of the different optimal supplier portfolios and are able to analyze the trade-offs between the different purchasing goals before making a decision. Finally, we illustrate the applicability of the proposed methodology in a real-world supplier selection and order allocation case from the automotive industry. In the example case, we identify 1754 optimal supplier portfolios that may be assembled based on the eight available suppliers. Our analyses show that each optimal portfolio consists of two suppliers, with one specific supplier being included in each portfolio. Furthermore, four suppliers are not part of any optimal solution.</abstract>
    <parentTitle language="eng">Journal of Cleaner Production</parentTitle>
    <identifier type="doi">10.1016/j.jclepro.2018.12.315</identifier>
    <enrichment key="opus.import.data">@articlekellner_sustainability_2019, title = Sustainability in supplier selection and order allocation: combining integer variables with Markowitz portfolio theory, volume = 214, url = https://epub.uni-regensburg.de/38171/, abstract = This research presents a decision support methodology for the multi-criteria supplier selection and order allocation problem. The proposed approach supports purchasing managers in assembling mid-term supplier portfolios while making them aware of the trade-offs between the supplier sustainability, the purchasing costs, and the overall supply risk. First, we propose a multi-objective optimization model with three objectives: to maximize the supplier sustainability, to select the supplier portfolio with the lowest purchasing costs, and to minimize the supply risk. Our model extends existing mathematical approaches that follow the portfolio theory fathered by H. Markowitz by integrating the aspect ’risk’ into the supplier selection problem. Secondly, since we allow for integer variables in our model in contrast to the classical Markowitz portfolio theory we use the e-constraint method to visualize the efficient surface. The possibility of considering the non-dominated set of supplier portfolios is advantageous for purchasing managers as they gain a picture of the different optimal supplier portfolios and are able to analyze the trade-offs between the different purchasing goals before making a decision. Finally, we illustrate the applicability of the proposed methodology in a real-world supplier selection and order allocation case from the automotive industry. In the example case, we identify 1754 optimal supplier portfolios that may be assembled based on the eight available suppliers. Our analyses show that each optimal portfolio consists of two suppliers, with one specific supplier being included in each portfolio. Furthermore, four suppliers are not part of any optimal solution. (C) 2019 Elsevier Ltd. All rights reserved., journal = Journal of Cleaner Production, author = Kellner, Florian and Utz, Sebastian, year = 2019, note = Place: OXFORD Publisher: ELSEVIER SCI LTD, keywords = FRAMEWORK, CHAIN MANAGEMENT, CRITERIA, OPTIMIZATION, PERFORMANCE, RISK, Supplier selection, Sustainability, DECISION-MAKING, Graphical decision support application, MODEL, Multi-criteria decision making, Order allocation, STOCHASTIC CONSTRAINTS, Supply risk, VENDOR SELECTION, pages = 462–474,</enrichment>
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    <author>Florian Kellner</author>
    <author>Sebastian Utz</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Supplier selection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Graphical decision support application</value>
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    <subject>
      <language>eng</language>
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      <value>Multi-criteria decision making</value>
    </subject>
    <subject>
      <language>eng</language>
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      <value>Order allocation</value>
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    <subject>
      <language>eng</language>
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      <value>Supply risk</value>
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  </doc>
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