@inproceedings{46338,
  abstract     = {{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.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{Evolutionary Multi-Criterion Optimization (EMO)}},
  editor       = {{Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}},
  isbn         = {{978-3-030-12597-4}},
  pages        = {{516–528}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}}},
  doi          = {{10.1007/978-3-030-12598-1_41}},
  volume       = {{11411}},
  year         = {{2019}},
}