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A. Dutta, "Integrating Heuristic Knowledge and Optimization Models for Communication Network Design," IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 6, pp. 9991017, December, 1993.  
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@article{ 10.1109/69.250088, author = {A. Dutta}, title = {Integrating Heuristic Knowledge and Optimization Models for Communication Network Design}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {5}, number = {6}, issn = {10414347}, year = {1993}, pages = {9991017}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.250088}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Integrating Heuristic Knowledge and Optimization Models for Communication Network Design IS  6 SN  10414347 SP999 EP1017 EPD  9991017 A1  A. Dutta, PY  1993 KW  topological design; optimization models; communication network design; heuristic design knowledge; computer communication networks; truth maintenance system; TMS; justification; dependencydirected backtracking mechanism; hybrid tool; modular structure; incremental refinement; blackboard; blackboard architecture; CAD; computer networks; decision support systems; knowledge based systems; telecommunications computing; truth maintenance VL  5 JA  IEEE Transactions on Knowledge and Data Engineering ER   
A method is developed for integrating heuristic design knowledge with optimization models to create a tool for the topological design of computer communication networks. Design choices are based on suggestions from optimization models, as well as heuristic knowledge, which interact through a blackboard. A truth maintenance system (TMS) records justification for current design choices, as well as promising alternatives. A dependencydirected backtracking mechanism works with the TMS to choose other alternatives as warranted. This hybrid tool can consider a wider range of design requirements than is possible using one type of knowledge alone, is flexible in handling variations in these requirements, and has a modular structure which facilitates incremental refinement. Computational results on separate networks show it is effective in identifying good lowcost solutions.
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