1. COURSE “SYSTEM DESIGN: STRUCTURAL APPROACH” DETC2006-99547 Inst. for Information Transmission Problems Russian Academy of Sciences, Moscow 127994, Russia Email: mslevin@acm.org Mark Sh. LEVIN http: //www.iitp.ru/mslevin/ The 18 th DTM, Sept. 10-13, 2006, Philadelphia, Pennsylvania, USA Recent course “Design of Systems: structural approach”, Moscow Inst. of Physics & Technology (State Univ.), since Sept. 2004 http: //www.iitp.ru/mslevin/SYSD.HTM

2. PLAN 1.About Moscow Inst. of Physics & Technology 2.Decision cycle 3.Structure of the course 4.Three-layer hierarchy 5.Example: allocation problem (problem family) 6.Four illustrative examples for complex schemes 7.Examples of student’s projects 8.Conclusion

3. ABOUT STUDENTS in MIPT Special Selection Process: to select the best students 1.Educational background from schools 2.Ability to Mathematics, Physics 3.Creativity 4.Background in IT (all components) 5.Ability to learn 6.Ability to plan 7.Motivations (& interests in applied domains)

4. Recent course in MIPT: place of course Mathematics Physics IT T 0 3 year Real-World Science & Engineering 6 year CHANGE OF STYLE STYLES: FROM: Learning (analogues) TO: Creation in Research&Engineering

5. MY FACULTY (now) Software Hardware VLSI design Radio Physics Information systems Communication systems Management systems Organizational systems Space systems MODELING & DESIGN: Multidisciplinary systems (& processes) Faculty of Cybernetics & Radio Engineering (over 100 students: each year) APPLIED DOMAINS:

6. DECISION CYCLE Applied problem(s) Math. Model(s) Solving scheme// algorithms Programs/ procedures Solving process (e.g., computing DECISION

7. DESIGNED SYSTEMS REQUIREMENTS: OBJECTIVES, CRITERIA STANDARDS SYSTEMS: 1.PRODUCTS / PRODUCT FAMILIES 2.PROCESSES

8. Structure of course I.BASIC SYSTEMS ISSUES 1.1.Systems engineering (life cycle engineering) 1.2.Structural models (graphs, networks, binary relations) II.SYSTEM ANALYSIS & DECISION MAKING 2.1.Principles of systems analysis 2.2.Methods for ranking III. COMBINATORIAL OPTIMIZATION & OPTIMIZATION 3.1.Basic problems (e.g., knapsack, TSP, scheduling, routing, graph coloring) 3.2.Complexity issues of combinatorial problems 3.3.Optimization (convex programming, Mixed Int. Progr.) IV.DESIGN FRAMEWORKS (series, hierarchy, cascade-like) V.MORPHOLOGICAL DESIGN APPROACHES VI.ADDITIONAL SYSTEM ISSUES (maintenance, system testing, requirements engineering) TECHNOLOGICAL SYSTEMS PROBLEMS: design, improvement/upgrade, multistage design, revelation of bottlenecks, evaluation, modeling of evolution/development

9. Recent course in MIPT: 3-layer hierarchy LAYER 3: Methods & Models Optimi- zation Decision making Combinatorial optimization AI LAYER 1: Applied complex systems Hierarchical Systems (modular multi-level approach) A B Y C S=A*B*C*D VUX *Graphs *Networks *Binary relations LAYER 2: Design frameworks Solving schemes Composite solving schemes (solving engineering/technology )

10. Recent course in MIPT: 3-layer hierarchy LAYER 3: Methods & Models LAYER 1: Applied complex systems LAYER 2: Design frameworks Solving schemes

11. Recent course in MIPT: 3-layer hierarchy LAYER 3: Methods & Models LAYER 1: Applied complex systems LAYER 2: Design frameworks Solving schemes

12. Towards Optimization Models & Solving Approaches BASIC MODELS FOR LABORATORY WORKS: 1.Multicriteria decision making (ranking, 3 methods) 2.Knapsack problem 3.Multiple choice problem 4.Clustering 5.Proximity to an ideal decision 6.Evaluation of a hierarchical modular system 7.Combinatorial morphological synthesis 8.Assignment / allocation problem 9.TSP 10.By choice APPROACHES AND MODELS IN LECTURE MATERIALS: 1.Continuous optimization 2.Multidisciplinary optimization 3.Mixed integer mathematical programming 4.Parameter Space Investigation (PSI) approach 5.Combinatorial optimization models, basic algorithm types, heuristics, and complexity issues

13. Allocation problem Allocation (assignment, matching, location): MAPPING BIPARTITE GRAPH 1 2 3 4 5 6 7 8 a b c d e f g h Positions (locations, sites) Set of elements (e.g., personnel, facilities)

14. Allocation problem: applied examples for elements & positions 1.Boys -- Girls (marriage problem) 2.Workers -- Work positions 3.Facilities --Positions in manufacturing system (facility layout) 4.Tasks -- Processors in multiprocessor system 5.Anti-rockets --Targets in defense systems 6.Files -- Databases in distributed information systems Etc.

15. Evolution chart of allocation-like problems Basic assignment problem Quadratic assignment problem PLUS: distance matrix for positions Generalized assignment problem PLUS: resource (s) for positions Generalized quadratic assignment problem Multicriteria quadratic assignment problem Multicriteria generalized assignment problem Multicriteria generalized Quadratic assignment problem Multicriteria assignment problem PLUS: multicriteria description PLUS: distance matrix for position PLUS: resource (s) for positions PLUS: multicriteria description

16. 1 2 3 4 a b c d e f g h Segments of market PRODUCTS EXAMPLE 1: Clustering, Assignment, Multiple Choice Problem CUSTOMERS 2 Groups of products Marketing strategies X X X

17. S=X*Y*Z Y Z=P*Q*U*V Z 1 =P 2 *Q 3 *U 1 *V 5 Z 2 =P 1 *Q 2 *U 3 *V 1 Y1Y2Y3Y1Y2Y3 AB A1A2A3A1A2A3 B1B2B3B4B1B2B3B4 C1C2C3C4C5C1C2C3C4C5 D=I*J I1I2I3I1I2I3 J1J2J3J4J1J2J3J4 P1P2P3P1P2P3 Q1Q2Q3Q4Q1Q2Q3Q4 U1U2U3U1U2U3 V1V2V3V4V5V6V1V2V3V4V5V6 C X=A*B*C*D P QU V X 1 =A 1 *B 2 *C 4 *D 3 X 2 =A 3 *B 4 *C 2 *D 1 D 1 =I 1 *J 1 D 2 =I 1 *J 2 D 3 =I 3 *J 4 S 1 =X 2 *Y 3 *Z 2 S 2 =X 1 *Y 2 *Z 1 J I EXAMPLE 2: Hierarchical Design

18. EXAMPLE 3: Multistage design (lectures) Stage 1... T0 Stage 3... Stage 2... Trajectory

19. EXAMPLE 4: Evolution as Generations of software DSS COMBI (lectures) System 0 S 0 = T Techniques T T1T1 System 1 S 1 = T * U Techniques T T2T2 T1T1 T3T3 U L1L1 User interface System 2 S 2 = T *U(L)*Y Techniques T T2T2 T1T1 T3T3 U=L L1L1 User interface Language L Y1Y1 Tool for synthesis of solving strategy Y

20. System 3 S 3 = T *U(L*G)*Y*E*H Techniques T T2T2 T1T1 T3T3 L1L1 User inter- face U=L*G Language L Y1Y1 Tool for synthesis of solving strategy Y G1G1 E1E1 Library of examples E H1H1 Hyper- text H L2L2 System 4 S 4 = T *U(L*G)*E*H Techniques T T2T2 T1T1 T3T3 User inter- face U=L*G Language L E1E1 Library of examples E H1H1 Hyper- text H L2L2 G2G2 G G Graphics EXAMPLE 4: Evolution as Generations of software DSS COMBI (lectures)

21. System 0 T 0 System 1 System 2 System 3 System 4 Improvement EXAMPLE 4: Evolution as Generations of software DSS COMBI (lectures)

22. STUDENT PROJECTS (RESULTS OF LAB. WORKS; examples) 1.Software for signal simulation (software) 2.Computer class (educational multidisciplinary environment) 3.Plan of body building (sport) 4.Musical project (art) 5.Allocation of communication devices (configuration of communication facilities) 6.Plan of system testing (“probing” for communication) 7.Organization of sport event (sport) 8.Control system for computer memory 9.Multicriteria analysis of computer protocols 10.Car

23. CONCLUSION 1.Collection of student’s materials 2.Organization of student’s homepages with results 3.Preparation of student’s results: presentations, papers

24. That’s All Gr8 Thanks!

25. MY BASIC BOOKS (& my articles) 1.Levin M.Sh., Composite Systems Decisions. Springer, 2006. 2. Levin M.Sh., Combinatorial Engineering of Decomposable Systems, Kluwer, 1998. 3. Belkin A.R., Levin M.Sh., Decision Making: Combinatorial Models of Information Approximation, Nauka Publishing House (Russian Academy of Sciences), Moscow, 1990 (in Russian) 4. Levin M.Sh., Application of Combinatorial Models in Computer-Aided Systems. VNIITEMR, Moscow, 1986 (in Russian)