S. Sen. Minimal cost set covering using probabilistic methods. In ACM Symp. Applied Computing, Indianapolis, pages 157--164, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....commercial LP IP solvers. While guaranteeing optimal results, such exact methods can be computationally prohibitiveeven for modestly sized IPs. For most medium to large IPs, this leaves a choice of approximate and heuristic algorithms [9] including greedy search (GS) 4] simulated annealing (SN) [13], genetic algorithm (GA) 2] Lagrangian relaxation [1] and neural network [6] methods. Most published performance results [6] 1] deal with random, lowdensity data sets. The VPP falls into the category of a mediumto large IP with non random data and moderate density. A representative size for a ....

S. Sen. Minimal cost set covering using probabilistic methods. In ACM Symp. Applied Computing, Indianapolis, pages 157--164, 1993.

....commercial LP IP solvers. While guaranteeing optimal results, such exact methods can be computationally prohibitiveeven for modestly sized IPs. For most medium to large IPs, this leaves a choice of approximate and heuristic algorithms [9] including greedy search (GS) 5] simulated annealing (SN) [13], genetic algorithms (GA) 2] Lagrangian relaxation [1] and neural network [7] methods. Most published performance results [7] 1] deal with random, lowdensity data sets. The VPP falls into the category of a medium to large IP with nonrandom data and moderate density[12] Finally,we ....

S. Sen. Minimal cost set covering using probabilistic methods. In ACM Symp. Applied Computing, Indianapolis, pages 157--164, 1993.

....i whereas the outgoing aggregate is X . Each incoming aggregate is associated with the energy cost w i . Therefore, the energy cost of the outgoing aggregate is the minimum weight of the cover plus 1. Approximate algorithms for this problem include greedy heuristics [4, 6] probabilistic methods [17], geneticalgorithms based heuristics [15] neural networks based techniques [9] and Lagrangian heuristics [5] We chose the greedy heuristic because of its high quality solutions (The worst ratio between the cost of a greedy solution and the optimal solution is ln d 1 where d is the maximum ....

S. Sen. Minimal cost set covering using probabilistic methods. In Proceedings

....Schulze Kremer, Steffen, 915, 916, 917] Schwefel, Hans Paul, 75, 918, 919] Schwehm, Markus, 920, 921, 922] Schwenderling, Peter, 600] Sedlmeyer, R. L. 316] Sekharan, D. Ansa, 923, 1049, 1052] Semertzidis, Michael T. 924] Semmler, Klaus, 67] Sen, Mrinal K. 656] Sen, Sandip, [891, 892] Seredynski, F. 1116] Sethares, William A. 546] Shamir, Joseph, 674] Shamir, N. 925] Sharma, S. K. 558, 559] Sharman, K. C. 926] Sharpe, Peter K. 927] Shaw, Michael J. 936] Sheble, Gerald B. 191] Shenoi, Sujeet, 406] Sheth, Beerud, 669] Sheung, Julian, 40] Shiba, ....

.... [945] greedy, 859] heuristics, 403] hill climbing, 908, 987] Levenberg Marquartd, 667] MSX, 234] Nelder Mead, 234, 251] pattern search, 1097] random search, 234] responce surface method, 1097] scheduling fitness function, 484] scheduling methods, 995] simulated annealing, [40, 72, 226, 363, 435, 731, 892, 945, 972, 987] statistical package, 878] tabu search, 72, 945] variants, 811] Very Fast Simulated Annealin, 1035] Very Fast Simulated Re Annealing, 251] competition, 817] complexity, 45, 880] compression, 631, 700] computational geometry, 1084] triangulation, 1051] computer graphics, 617] ....

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Sandip Sen. Minimal cost set covering using probabilistic methods. In ?, editor, Proceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing, pages 157--164, Indianapolis, IN, 14.-16. February 1993. ACM, New York. y(Sen/News) ga:SSen93b.

....14] Sakanashi, H. 254] Sakawa, Masatoshi, 61] Salama, M. M. A. 181] Sappington, David E. 239] Savini, A. 248] Schafer, K. L. 90] Scheraga, Harold A. 200, 110] Schneider, Bernd, 173] Schulze, J. 203] Seda, Milo s, 27] Selman, Bart, 262] Sen, Mrinal K. 34] Sen, Sandip, [39] Sen, S. 11] Sexton, Randall S. 29] Sha er, Ronald E. 122] Shankaranarayanan, G. 127] Shapiro, Bruce A. 105] Sharman, K. C. 45] She, Linlin, 127] Shen, Chang Yun, 80] Sheng, Fang, 158] Sheung, Julian, 136] Shiose, Atsushi, 259] Shiyou, Yang, 201] Shtub, Avraham, 205] ....

.... scheduling, 197, 205] integer programming, 61] Kernighan Lin, 160] linear controllers, 92] local search, 169] metaheuristics, 61] Metropolis, 19, 183] multistart, 28] neural networks, 92] Powell s method, 36] quasi Newton, 248] random search, 253, 96] simulated annealing, [146, 261, 40, 32, 33, 253, 34, 35, 36, 259, 260, 37, 38, 136, 235, 31, 245, 251, 256, 258, 39, 263, 264, 266, 153, 10, 154, 11, 74, 12, 75, 159, 78, 160, 13, 81, 163, 14, 15, 166, 169, 171, 16, 46, 85, 17, 18, 174, 86, 19, 177, 182, 49, 183, 20, 185, 92, 96, 190, 21, 191, 192, 100, 22, 197, 200, 204, 205, 30, 208, 211, 23, 212, 215, 24, 216, 218, 219, 25, 124, 26, 225, 27, 131, 230, 28, 232, 29, 233, 41] simulated annealing (best) 206] simulated annealing (SA superior) 239] simulated annealing; GA better, 226] simulated annealing; SA worse, 224] stepwise elimination, 256] stochastic tunneling, 28] tabu search, 35, 235, 263, 85, 174, 86, 21, 206, 24, 219, 27] comparison , 176] ....

[Article contains additional citation context not shown here]

Sandip Sen. Minimal cost set covering using probabilistic methods. In ?, editor, Proceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing, pages 157-164, Indianapolis, IN, 14.-16. February 1993. ACM, New York. ySen/News ga:SSen93b. Bibliography 41

....results than a number of other heuristics [3] 30] for problems involving up to 1,000 rows and 10,000 columns. Recently, Jacobs and Brusco [21] developed a heuristic based on simulated annealing and reported considerable success on problems with up to 1,000 rows and 10,000 columns. Sen [27] investigated the performances of a simulated annealing algorithm and a simple genetic algorithm on the minimal cost set covering problem, but few computational results were given. Earlier work, both optimal and heuristic solution algorithms for the SCP, can be found in [4, 5, 14, 17, 26] This ....

S. Sen. Minimal cost set covering using probabilistic methods. Proc. 1993 ACM/SIGAPP Symposium on Applied Computing, pages 157--164, 1993.

....[Bea90a] was tested on problems with up to 500 rows and 5000 columns. The approximation algorithm of [Wed94] was tested on problems generated for airline crew scheduling, involving up to 1600 rows and 105000 columns. Genetic algorithms and simulated annealing algorithms for set covering appear in [Sen93, BC94] Several neural network based algorithms were suggested or developed for problems related to SCP (like scheduling and diagnostic problems, cf. PR89, Jef91, CM91] but to our knowledge no neural network based algorithm for the SCP was actually presented and tested so far. One of the ....

S. Sen. Minimal cost set covering using probabilistic methods, 1993. AI Lab, University of Michigan. Manuscript.

.... As is the case with many NP hard problems, one of the only two algorithms, short of exhaustive enumeration, that delivers optimum solutions is based on the branch and bound technique (the other being dynamic programming) Approximation algorithms include greedy techniques [9] simulated annealing [22], Lagrangian relaxation and sub gradient optimization techniques [5] and GA based heuristics [17] The interested reader can find more references to the unicost and mscp including more applications in [4] The mscp can be recast by using matrix notation. Elements of E and subsets of F can be ....

S. Sen. Minimal cost set covering using probabilistic methods. In Proceedings 1993 ACM/SIGAPP Symposium on Applied Computing, pages 157--164, 1993.

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