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.
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Notes
- 1.
Two prominent strategies used also in this work for comparison reasons are Drive First (DF) and Distributed Waiting (DW). While in DF the vehicle only waits at its current customer location if both waiting time is available and the planned route only contains the end depot, the latter strategy distributes the amount of available waiting time equally among all customer locations of the current planned route.
- 2.
We adopt EAX [12] as the local search procedure with focus on tour length minimization. Note, that we need to solve a shortest Hamiltonian path problem, but EAX is a TSP solver. Thus, before application of the local search procedure, the problem is transformed into a TSP by a sequence of modifications to the distance matrix (see [10] for details).
- 3.
Note that in the bi-objective case this leads to a sorting in descendant order of the number of unvisited customers.
- 4.
Repository: https://github.com/jakobbossek/dynvrp/.
- 5.
We find similar behavior for all investigated (but not shown) topologies for multiple repetitions.
References
Azzouz, R., Bechikh, S., Ben Said, L.: Dynamic multi-objective optimization using evolutionary algorithms: a survey. In: Bechikh, S., Datta, R., Gupta, A. (eds.) Recent Advances in Evolutionary Multi-objective Optimization. ALO, vol. 20, pp. 31–70. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-42978-6_2
Berube, J.-F., Gendreau, M., Potvin, J.-Y.: An exact \(\in \)-constraint method for bi-objective combinatorial optimization problems: application to the traveling salesman problem with profits. Eur. J. Oper. Res. 194(1), 39–50 (2009)
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., Trautmann, H.: Local search effects in bi-objective orienteering. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, pp. 585–592. ACM, New York (2018)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Filippi, C., Stevanato, E.: Approximation schemes for bi-objective combinatorial optimization and their application to the TSP with profits. Comput. Oper. Res. 40(10), 2418–2428 (2013)
Ghannadpour, S.F., Noori, S., Tavakkoli-Moghaddam, R.: A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority. J. Comb. Optim. 28, 414–446 (2014)
Grimme, C., Meisel, S., Trautmann, H., Rudolph, G., Wölck, M.: Multi-objective analysis of approaches to dynamic routing of a vehicle. In: ECIS 2015 Completed Research Papers. Paper 62. AIS Electronic Library (2015)
Jozefowiez, N., Glover, F., Laguna, M.: Multi-objective meta-heuristics for the traveling salesman problem with profits. J. Math. Model. Algorithms 7(2), 177–195 (2008)
Meisel, S.: Anticipatory Optimization for Dynamic Decision Making. Operations Research/Computer Science Interfaces Series, vol. 51. Springer, New York (2011). https://doi.org/10.1007/978-1-4614-0505-4
Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., Trautmann, H.: Evaluation of a multi-objective EA on benchmark instances for dynamic routing of a vehicle. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp. 425–432. ACM, New York (2015)
Meisel, S., Wölck, M.: Evaluating idle time policies for real-time routing of a service vehicle. In: ECIS 2015 Completed Research Papers. Paper 132. AIS Electronic Library (2015)
Nagata, Y., Kobayashi, S.: A powerful genetic algorithm using edge assembly crossover for the traveling salesman problem. INFORMS J. Comput. 25(2), 346–363 (2013)
Nahum, O.E., Hadas, Y.: A framework for solving real-time multi-objective VRP. In: Żak, J., Hadas, Y., Rossi, R. (eds.) EWGT/EURO -2016. AISC, vol. 572, pp. 103–120. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-57105-8_5
Pillac, V., Gendreau, M., Guéret, C., Medaglia, A.L.: A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225(1), 1–11 (2013)
Raquel, C., Yao, X.: Dynamic multi-objective optimization: a survey of the state-of-the-art. In: Yang, S., Yao, X. (eds.) Evolutionary Computation for Dynamic Optimization Problems, pp. 85–106. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38416-5_4
Acknowledgments
J. Bossek, C. Grimme, S. Meisel and H. Trautmann acknowledge support by the European Research Center for Information Systems (ERCIS).
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Bossek, J., Grimme, C., Meisel, S., Rudolph, G., Trautmann, H. (2019). Bi-objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_41
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