L. K. Fleischer and E. Tardos. Efficient continuous-time dynamic network flow algorithms. Operations Research Letters, 23:71--80, 1998. 32 Home/Search Document Details and Download
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Quickest Flows Over Time - Fleischer, Skutella Self-citation (Fleischer) (Correct)
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L. K. Fleischer and E. Tardos. Efficient continuous-time dynamic network flow algorithms. Operations Research Letters, 23:71--80, 1998. 32
Optimal Rounding of Instantaneous Fractional Flows Over Time - Fleischer, Orlin (1999) Self-citation (Fleischer) (Correct)
....over time is a time dependent flow f(t) through a with non zero supply and demand vector fl associated with V , and a time bound T . Below we give a discrete time model for transshipmentover time. However, our algorithm solves both the discrete time and continuous time problems, since, as noted in [5, 7], an optimal solution to the discrete time problem can be 1 easily transformed into an optimal solution for the continuous time problem. A transshipment over time obeys edge capacity constraints f(t) u for all t 2 f1# 2#: #Tg, flow conservation constraints P r t=1 P j f ij (t) fl i for all ....
....of time varying functions, and proving the convergence of algorithms that eventually find solutions. These algorithms are not polynomial and implementations do not seem able to handle problems with more than a few nodes. For the case in which capacity functions are constant, Fleischer and Tardos [7] extend the polynomial, discrete time transshipmentover time algorithm in [13] to work in the continuous time setting. There are some additional continuous time problems that have polynomial time algorithms. A universally quickest transshipment is a quickest transshipment that simultaneously ....
L. Fleischer and ' E. Tardos. Efficientcontinuous-time dynamic network flow algorithms. Operations Research Letters, 23:71--80, 1998.
A Faster Algorithm for the Quickest Transshipment Problem - Fleischer (1997) (1 citation) Self-citation (Fleischer) (Correct)
....of algorithms that eventually find solutions. These algorithms fall short of being efficient, either theoretically or practically, and implementations do not seem able to handle problems with more than a few nodes. For the case in which capacity functions are constant, Fleischer and Tardos [4] extend the polynomial, discrete time dynamic transshipment algorithm of Hoppe and Tardos [8] to work in the continuous time setting. The integral quickest transshipment algorithms presented in this paper returns both continuous time and discrete time solutions. 2 Notation A network N = V; E; ....
L. Fleischer and E. Tardos. Efficient continuous time dynamic network flow algorithms. Technical Report TR1166, Cornell University, Department of Operations Research and Industrial Engineering, 1996.
Faster Algorithms for the Quickest Transshipment Problem - L. Fleischer (1997) (1 citation) Self-citation (Fleischer) (Correct)
....of algorithms that eventually find solutions. These algorithms fall short of being efficient, either theoretically or practically, and implementations do not seem able to handle problems with more than a few nodes. For the case in which capacity functions are constant, Fleischer and Tardos [5] extend the polynomial, discrete time dynamic transshipment algorithm in [10] to work in the continuous time setting. A continuous dynamic transshipment is a flow f that varies over time. Let x be the rate of flow of f : x(t) df(t) dt. We assume that each x ij is a Lebesgue measurable function ....
.... = oe i ; 8i 2 V If this problem is feasible and T is integral, there is a solution f that changes only at times in f1; 2; Tg: A discrete time solution can be transformed into a continuous time solution by sending flow at rate f(t) in the interval (t Gamma 1; t] Fleischer and Tardos [5] prove that this transformation of the optimal discrete time solution is optimal for the continuous time problem. Thus, the continuous time problem is no harder than the discrete time problem; the integral quickest transshipment algorithms mentioned in the preceding section and presented in this ....
L. Fleischer and E. Tardos. Efficient continuous time dynamic network flow algorithms. Technical Report TR1166, Cornell University, Department of Operations Research and Industrial Engineering, 1996.
Optimal Rounding of Fractional Dynamic Flows when.. - L. Fleischer, J. B.. (1997) (1 citation) Self-citation (Fleischer) (Correct)
....of time varying functions, and proving the convergence of algorithms that eventually find solutions. These algorithms are not polynomial and implementations do not seem able to handle problems with more than a few nodes. For the case in which capacity functions are constant, Fleischer and Tardos [6] extend the polynomial, discrete time dynamic transshipment algorithm in [12] to work in the continuous time setting. A universally quickest transshipment is a quickest transshipment that simultaneously minimizes the amount of excess left in the network at every moment of time. An optimal solution ....
L. Fleischer and E. Tardos. Efficient continuous time dynamic network flow algorithms. Technical Report TR1166, Cornell University, Department of Operations Research and Industrial Engineering, 1996.
Multicommodity Flows over Time: Efficient Algorithms and.. - Hall, Hippler, Skutella (2003) (Correct)
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L. K. Fleischer and E. Tardos. Efficient continuous-time dynamic network flow algorithms. Operations Research Letters, 23:71--80, 1998.
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