zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Modeling and algorithms of the crew rostering problem with given cycle on high-speed railway lines. (English) Zbl 1264.90117
Summary: We study the modeling and algorithms of crew roster problem with given cycle on high-speed railway lines. Two feasible compilation strategies for work out the crew rostering plan are discussed, and then an integrated compilation method is proposed in this paper to obtain a plan with relatively higher regularity in execution and lower crew members arranged. The process of plan making is divided into two subproblems which are decomposition of crew legs and adjustment of nonmaximum crew roster scheme. The decomposition subproblem is transformed to finding a Hamilton chain with the best objective function in network which was solved by an improved ant colony algorithm, whereas the adjustment of nonmaximum crew rostering scheme is finally presented as a set covering problem and solved by a two-stage algorithm. The effectiveness of the proposed models and algorithms are testified by a numerical example.
90B90Case-oriented studies in OR
90C90Applications of mathematical programming
90C35Programming involving graphs or networks
90B35Scheduling theory, deterministic
Full Text: DOI
[1] J. Buhr, “Four methods for monthly crew assignment-a comparison of efficiency,” in Proceedings of the AGIFORS Symposium, vol. 18, pp. 403-430, 1978.
[2] B. Nicoletti, “Automatic crew rostering,” Transportation Science, vol. 9, no. 1, pp. 33-42, 1975.
[3] D. Teodorović and P. Lu\vcić, “A fuzzy set theory approach to the aircrew rostering problem,” Fuzzy Sets and Systems, vol. 95, no. 3, pp. 261-271, 1998.
[4] R. Moore, J. Evans, and H. Noo, “Computerized tailored blocking,” in Proceedings of the AGIFORS Symposium, vol. 18, pp. 343-361, 1978.
[5] J. Byrne, “A preferential bidding system for technical aircrew,” Proceedings of the AGIFORS Symposium, vol. 28, pp. 87-99, 1988.
[6] P. Lu\vcić and D. Teodorović, “Simulated annealing for the multi-objective aircrew rostering problem,” Transportation Research A, vol. 33, no. 1, pp. 19-45, 1999.
[7] H. Dawid, J. König, and C. Strauss, “An enhanced rostering model for airline crews,” Computers and Operations Research, vol. 28, no. 7, pp. 671-688, 2001. · Zbl 0990.90062 · doi:10.1016/S0305-0548(00)00002-2
[8] A. Monfroglio, “Hybrid genetic algorithms for a rostering problem,” Software: Practice and Experience, vol. 26, no. 7, pp. 851-862, 1996. · Zbl 0853.68104
[9] A. Ernst, M. Krishnamoorthy, and D. Dowling, “Train crew rostering using simulated annealing,” in Proceedings of the International Conference on Optimization: Techniques and Applications (ICOTA ’98), Perth, Australia, 1998.
[10] D. M. Ryan, “The solution of massive generalized set partitioning problems in air crew rostering,” Journal of the Operational Research Society, vol. 43, pp. 459-467, 1992.
[11] M. Gamache and F. Soumis, “A method for optimally solving the rostering problem,” Cahier Du GERAD, G-90-40 H3T 1V6, Ecole des Hautes Etudes Commerciales, Montreal, Canada, 1993.
[12] M. Dorigo and L. M. Gambardella, “Ant colonies for the travelling salesman problem,” BioSystems, vol. 43, no. 2, pp. 73-81, 1997. · doi:10.1016/S0303-2647(97)01708-5
[13] F. D. Croce, R. Tadei, and G. Volta, “A genetic algorithm for the job shop problem,” Computers and Operations Research, vol. 22, no. 1, pp. 15-24, 1995. · Zbl 0816.90081 · doi:10.1016/0305-0548(93)E0015-L
[14] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 26, no. 1, pp. 29-41, 1996.
[15] M. Desrochers and F. Soumis, “A column generation approach to the urban transit crew scheduling problem,” Transportation Science, vol. 23, no. 1, pp. 1-14, 1989. · Zbl 0668.90043 · doi:10.1287/trsc.23.1.1