New bounds for optimum traffic assignment in satellite communication

Amico, M. Dell'; Maffioli, Francesco; Trubian, Marco

doi:10.1016/s0305-0548(98)00012-4

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Because in many cases matrices P k1 and P k2 will have nonzero entries in most of their rows and columns, neither element t ab will be compatible with P k2 or element t cd with matrix P k1 . Although Dell'Amico, Maffioli, and Trubian [4] have used this kind of neighborhood for a similar problem, they pointed out themselves [13] that, for their version of the time slot assignment problem, many rows and columns do not have any nonzero entry, making it easy to generate a new feasible decomposition through the exchange of pairs of nonzero entries from two matrices appearing in the decomposition. We propose a new type of neighborhood for this problem.…”

Section: Local Searchmentioning

confidence: 99%

“…Only two permutations of L can be constructed: (1, 1) Ϫ (1, 2) and (1,2) Ϫ (1, 1). If we consider the first permutation, only element (1, 1) may be inserted in P 4 matrix T, the mode matrix P k under investigation for elimination moves, a parameter max_eliminations which defines the maximum number of neighbors that will be evaluated, and a seed for the pseudo random number generator. In line 1, we create a list L with the n nonzero entries of P k .…”

Section: Local Searchmentioning

confidence: 99%

“…Since it has com-plexity O(qn 2 ), the overall complexity of finding the best neighbor of the current solution is O(tq 2 n 2 ), where q is the number of mode matrices in the current solution and t ϭ max{max_permutations, max_eliminations} is an upper bound to the number of neighbors generated from each of them. Since in practice q Ϸ n, this amounts to O(tn 4 ). Due to this high computational cost, we implemented procedure LocalSearch as outlined in Fig.…”

Section: Local Searchmentioning

confidence: 99%

See 2 more Smart Citations

Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment

Prais

Ribeiro

2000

INFORMS Journal on Computing

223

145

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A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. In this paper, we describe a GRASP for a matrix decomposition problem arising in the context of traffic assignment in communication satellites. We review basic concepts of GRASP: construction and local search algorithms. The local search phase is based on the use of a new type of neighborhood defined by constructive and destructive moves. The implementation of a GRASP for the matrix decomposition problem is described in detail. Extensive computational experiments on literature and randomly generated problems are reported. Moreover, we propose a new procedure Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list during the construction phase is self-adjusted according to the quality of the solutions previously found. The approach is robust and does not require calibration efforts. On most of the literature problems considered, the new Reactive GRASP heuristic matches the optimal solution found by an exact column-generation with branch-and-bound algorithm.

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Section: Local Searchmentioning

confidence: 99%

Section: Local Searchmentioning

confidence: 99%

Section: Local Searchmentioning

confidence: 99%

See 1 more Smart Citation

Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment

Prais

Ribeiro

2000

INFORMS Journal on Computing

223

145

View full text Add to dashboard Cite

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“…Francesco's work on applications was mainly related to telecommunication network design (Dell'Amico et al., , , ; Amaldi et al., ), hazardous material transportation (Cappanera et al, ), and optimal facility location problems (Maffioli and Righini, ; Belotti et al, ).…”

mentioning

confidence: 99%