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   	<dc:title>Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm</dc:title>
   	<dc:creator>Bossek, Jakob</dc:creator>
   	<dc:creator>Grimme, Christian</dc:creator>
   	<dc:creator>Meisel, Stephan</dc:creator>
   	<dc:creator>Rudolph, Günter</dc:creator>
   	<dc:creator>Trautmann, Heike</dc:creator>
   	<dc:creator>Deb, Kalyanmoy</dc:creator>
   	<dc:creator>Goodman, Erik</dc:creator>
   	<dc:creator>Coello Coello, Carlos A.</dc:creator>
   	<dc:creator>Klamroth, Kathrin</dc:creator>
   	<dc:creator>Miettinen, Kaisa</dc:creator>
   	<dc:creator>Mostaghim, Sanaz</dc:creator>
   	<dc:creator>Reed, Patrick</dc:creator>
   	<dc:subject>Combinatorial optimization</dc:subject>
   	<dc:subject>Dynamic optimization</dc:subject>
   	<dc:subject>Metaheuristics</dc:subject>
   	<dc:subject>Multi-objective optimization</dc:subject>
   	<dc:subject>Vehicle routing</dc:subject>
   	<dc:description>We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.</dc:description>
   	<dc:publisher>Springer International Publishing</dc:publisher>
   	<dc:date>2019</dc:date>
   	<dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
   	<dc:type>doc-type:conferenceObject</dc:type>
   	<dc:type>text</dc:type>
   	<dc:type>http://purl.org/coar/resource_type/c_5794</dc:type>
   	<dc:identifier>https://ris.uni-paderborn.de/record/48841</dc:identifier>
   	<dc:source>Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In: Deb K, Goodman E, Coello Coello CA, et al., eds. &lt;i&gt;Evolutionary Multi-Criterion Optimization (EMO)&lt;/i&gt;. Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528. doi:&lt;a href=&quot;https://doi.org/10.1007/978-3-030-12598-1_41&quot;&gt;10.1007/978-3-030-12598-1_41&lt;/a&gt;</dc:source>
   	<dc:language>eng</dc:language>
   	<dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-12598-1_41</dc:relation>
   	<dc:relation>info:eu-repo/semantics/altIdentifier/isbn/978-3-030-12598-1</dc:relation>
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