The effective application of a new approach to the generalized orienteering problem
The Orienteering Problem (OP) is an important problem in network optimization in which each city in a network is assigned a score and a maximum-score path from a designated start city to a designated end city is sought that is shorter than a pre-specified length limit. The Generalized Orienteering Problem (GOP) is a generalized version of the OP in which each city is assigned a number of scores for different attributes and the overall function to optimize is a function of these attribute scores. In this paper, the function used was a non-linear combination of attribute scores, making the problem difficult to solve. The GOP has a number of applications, largely in the field of routing. We designed a two-parameter iterative algorithm for the GOP, and computational experiments suggest that this algorithm performs as well as or better than other heuristics for the GOP in terms of solution quality while running faster. Further computational experiments suggest that our algorithm also outperforms the leading algorithm for solving the OP in terms of solution quality while maintaining a comparable solution speed.
KeywordsGeneralized orienteering problem Heuristics
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- Chao, I.-M.: Algorithms and solutions to multi-level vehicle routing problems. Ph.D. thesis, University of Maryland, College Park, MD (1993) Google Scholar
- Dongarra, J.: Performance of various computers using standard linear equations software. Technical report, University of Tennessee (2008) Google Scholar
- Geem, Z.W., Tseng, C.-L., Park, Y.: Harmony search for generalized orienteering problem: Best touring in China. In: Advances in Natural Computation. Lecture Notes in Computer Science, vol. 3612, pp. 741–750. Springer, Berlin/Heidelberg (2005) Google Scholar
- Tsiligirides, T.: Heuristic methods applied to orienteering. J. Oper. Res. Soc. 35(9), 797–809 (1984) Google Scholar
- Wang, Q., Sun, X., Golden, B.L.: Using artificial neural networks to solve generalized orienteering problems. In: Dagli, C., Akay, M., Chen, C., Fernández, B. (eds.) Intelligent Engineering Systems Through Artificial Neural Networks, vol. 6, pp. 1063–1068. ASME Press, New York (1996) Google Scholar