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Internet Engineering Czesław Smutnicki

Internet Engineering Czesław Smutnicki Discrete Mathematics – Location a nd Placement Problems i n Information a nd Communication Systems. PRESENTATION OUTLINE. location and placement problems, solution methodology, classical RND problems, more realistic RND problem,

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Internet Engineering Czesław Smutnicki

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  1. Internet Engineering Czesław Smutnicki DiscreteMathematics– Location and Placement Problems in Information and Communication Systems

  2. PRESENTATION OUTLINE • location and placement problems, • solution methodology, • classical RND problems, • more realistic RND problem, • map topology, cell model,coverage, • the optimization problem, • solution methods, • computer experiments, • conclusions

  3. LOCATION AND PLACEMENT PROBLEMS • VLSI floorplanning, • service or warehouse or facility location (known as QAP, Quadratic Assignment Problem), • databases and network services migration and replication, • antenna placement in mobile telecommunication, • cell planning for cellular networks, • distribution of access points in wireless networks, • ad hoc networks, • planning of distribution of wireless sensors • …

  4. SOLUTION METHODOLOGY. TIME OF CALCULATIONS/COST OF CALCULATION CURSE OF DIMENSIONALITY Please wait. Calculations will last 3 289 years NP-HARDNESS  LAB INSTANCE 5..20 VARIABLES ! ! ? NONLINEAR FUNCTION OF 2000 VARIABLES !!! INSTANCE FROM PRACTICE

  5. SOLUTION METHODOLOGY. CURRENT STATE IN DISCRETE OPTIMIZATION • Theory of NP-completness • Polynomial-time algorithms • Exact methods (B&B, DP, ILP, BLP, MILP, SUB,…) • Packages and solvers (LINDO, CPLEX, ILOG, …) • Approximate methods (…): heuristics, metaheuristics, meta2heuristics • Quality measures of approximation (absolute, relative, …) • Analysis of quality measures (worst-case, probabilistic, experimental) • Calculation cost (pessimistic, average, experimentally tested) • Approximation schemes (AS, polynomial-time PTAS, fully polynomial-time FPTAS) • Competitive analysis (no-line algorithms) • Inapproximality theory • Useful experimental methods (…) • „No free lunch” theorem • Public benchmarks • Parallel and distributed methods: new class of algorithms • Simulation

  6. SOLUTION METHODOLOGY. CURRENT STATE IN DISCRETE OPTIMIZATION

  7. SOLUTION METHODOLOGY. APPROXIMATE METHODS • constructive/improvement • priority rules • random search • greedy randomized adaptive • simulated annealing • simulated jumping • estimation of distribution • tabu search • adaptive memory search • variable neighborhhod search • evolutionary, genetic search • differential evolution • biochemistry methods • immunological methods • ant colony optimization • particle swarm optimization • neural networks • threshold accepting • bee search • path search • beam search • scatter search • harmony search • path relinging • adaptive search • constraint satisfaction • descending, hill climbing • multi-agent • memetic search • intelligent wather drops • harmony search • electromagnetic search • * * * * * METHODS RESISTANT TO LOCAL EXTREMES

  8. n x x CELL MODEL x m x x x k RADIO NETWORK DESIGN (RND) PROBLEM.CLASSICAL MATHEMATICAL MODEL

  9. RADIO NETWORK DESIGN (RND) PROBLEM. CLASSICAL MATHEMATICAL MODEL PROBLEM DATA SOLUTION CONSTRAINTS GOAL FUNCTION Percentage of covered region, =2

  10. RADIO NETWORK DESIGN (RND) PROBLEM. CLASSICAL MATHEMATICAL MODEL cont. MULTIPLE CRITERIA CASE • NP-hard problems • Balance between criteria • Scalarising • Pareto set, Pareto frontier • Approximate algorithms • Approximation of Pareto frontier

  11. MORE REALISTIC RND PROBLEMS. MAP TOPOLOGY

  12. MORE REALISTIC RND PROBLEMS. CELL MODEL Ri(Pi) Ci(Pi) Ci(Pi) Ci(Pi) Pi Pi Pi Pi

  13. MORE REALISTIC RND PROBLEMS. COVERAGE CHECKING POINT (pi, qi) SOLUTION; ANTENNA LOCATED IN POINTS FROM K; POWERS ARE Pi

  14. THE OPTIMIZATION PROBLEM GOAL FUNCTION VALUE UNDER CONSTRAINTS

  15. SOLUTION METHODS. DECOMPOSITION: LOWER LEVEL GOAL FUNCTION VALUE UNDER CONSTRAINTS

  16. SOLUTION METHODS. DECOMPOSITION: MIDDLE LEVEL GOAL FUNCTION VALUE UNDER CONSTRAINTS

  17. SOLUTION METHODS. DECOMPOSITION: UPPER LEVEL GOAL FUNCTION VALUE

  18. SOLUTION METHODS • LOWER LEVEL: EXACT SOLUTION • MIDDLE LEVEL: KNAPSACK (APPROXIMATION) • UPPER LEVEL: SIMULATED ANNEALING, AUTOTUNNIG VERSION • WITH BOLTZMAN COOLING SCHEME AND SOME STEPS IN FIXED • TEMPERATURE; SPECIFIC NEIGHBORHOOD BASED ON LOCAL VICINITY OF THE LOCATION POINT

  19. COMPUTER EXPERIMENTS

  20. CONCLUSIONS AND FURTHER RESEARCH • the algorithm offers more realistic model of RND problem • the model is smaller size and scalable • new constraints can be embedded in the model • model can be extended to multicriteria case • further research are needed for evaluating the quality of the proposed methods on broader test of instances • approximate solutions should be compared to exact solutions (CPLEX package) to evaluate their quality

  21. Thank you for your attention LOCATION AND PLACEMENT PROBLEMS IN INFORMATION AND COMMUNICATION SYSTEMS Czesław Smutnicki

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