By Giorgio Ausiello, Giuseppe F. Italiano, Luigi Laura, Umberto Nanni, Fabiano Sarracco (auth.), A. Ridha Mahjoub, Vangelis Markakis, Ioannis Milis, Vangelis Th. Paschos (eds.)
This publication constitutes the completely refereed post-conference court cases of the second one foreign Symposium on Combinatorial Optimization, ISCO 2012, held in Athens, Greece, in April 2012. The 37 revised complete papers awarded including four invited talks have been rigorously reviewed and chosen from ninety four average and 30 brief submissions. They current unique examine on all elements of combinatorial optimization, starting from mathematical foundations and idea of algorithms to computational reports and sensible applications.
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Additional resources for Combinatorial Optimization: Second International Symposium, ISCO 2012, Athens, Greece, April 19-21, 2012, Revised Selected Papers
Handbooks in Operations Research and Management Science 8, 35–139 (1995) 17. : A rolling stock circulation model for combining and splitting of passenger trains. European Journal of Operational Research 174(2), 1281–1297 (2006) 18. : Operations research in passenger railway transportation. Statistica Neerlandica 59(4), 467–497 (2005) 19. : Operational car assignment at via rail canada. Transportation Research Part B: Methodological 36(9), 755–778 (2002) 20. : Operations research models for railway rolling stock planning.
We say that ij is locally minimum if this quadrilateral is not convex or, else, if the weight of ij is smaller than the weight of kl. Figure 2 shows locally minimum segments. i i k k j l l j Fig. 2. Locally minimum segments. In both cases ij is locally minimum. When all the edges in a planar triangulation are locally minimum, we say that the triangulation itself is also locally minimum. Clearly, any minimum weight triangulation is locally minimum, however, not all locally minimum triangulations have minimum weight.
The score of trip j takes into account the reduced cost and the original cost of arc (i, j)k , and how “well” the capacity of the current train unit matches the request of trip j. Every time a trip is selected in the solution, we update its seat request. If the selected trip cannot be covered in the same day of the previous one, then we use another train unit of the same type. When no more train units of the current type are available, we end the workload of the train-unit type and move to the next type.