Appendix - C GOPALAN COLLEGE OF ENGINEERING AND MANAGEMENT Department of Computer Science and Engineering Academic Year: 2016-17 Semester: EVEN COURSE PLAN Semester: VI Subject Code& Name: 10CS661 & OPERATIONS RESERACH Name of Subject Teacher: J.SOMASEKAR Name of Subject Expert (Reviewer): SUPARNA For the Period: From: 13-02-17 to 02-06-17 Details of Book to be referred: Text Books T1: Frederick S. Hillier and Gerald J. Lieberman, Introduction to Operations Research, 8thEdition, Tata McGraw Hill, 2005. Reference Books R1: S.Kalavathy, Operations Research, 4th edition, vikas publishing house Pvt.Ltd. R2: Hamdy A Taha: Operations Research: An Introduction, 8th edition, pearson education, 2007. Practical Deviation How Made Remarks Book Lecture Applications & Brief Planned Executed Reasons Good / by HOD Topic Planned refereed with No objectives Date Date thereof Reciprocate page no. arrangement Introduction to the subject OR Objective: Introduce UNIT-1 : the concept of Linear T1:1-2 1. 13-02-17 Linear Programming : programming and Introduction solving by using graphical method. Scope and limitations of Also formulation of T1: 1-4 2. OR. Also applications of LPP for the data T2:2,4 14-02-17 OR provided by the Mathematical model of organization for T1: 27-35 3. LPP and solution by optimization T2:19-23 15-02-17 graphical method Application: minimization of T1: 27-33 4. LPP problems solutions by 16-02-17 graphical method. product cost, T2:21-24 Special cases of graphical maximization of T1: 27-33 method solution namely profit in company T2:25-26 5. unbound solution, no (or) industry 20-02-17 feasible solution and multiple solutions. Outcome: Able to Various illustrations of understand the need T1: 27-35 6. special cases of graphical of LPP , formulation T2:25-26 21-02-17 method of LPP for given data and solution of General formulation of LPP T1: 25-26 7. LPP by graphical T2:7-11 22-02-17 for the given information method Various illustrations of T1: 45 8. mathematical model of LPP T2:7-11 23-02-17 for given data Combination of T1:78-79 formulation and its solution T2:28 9. by graphical method. Also 27-02-17 previous questions papers discussion. UNIT – 2: T1:97-99 Simplex method-1: T2: 32-34 10. Slack and surplus variables 28-02-17 to make standard form of LPP Algorithm of simplex T1:105-109 method for solution of T2: 35-37 11. given LPP Objective: 01-03-17 To find the solution Solution of LPP by simplex of LPP for more than T1: 110-120 02-03-17 12. method for finding optimal two variables value Application: industry, Business, Defense for Solution of LPP by simplex optimization T1: 123-134 13. method with two or more 03-03-17 variables problems If the objective function is T1: 133-137 04-03-17 minimization then finding 14. solution of LPP by simplex method. Set of feasible solutions for Outcome: To T1: 140-148 06-03-17 the given linear equations. T2: 36-40 15. Understand the Also solution is degenerate concept of simplex or not. method and find the Various special cases of T2:40-45 07-03-17 16. solution of LPP by simplex method. using simplex Tie breaking in simplex T2:42-46 17. method 08-03-17 method. Revision / discussing T2:42-46 18. previous years question 13-03-17 papers problems. Unit-3: T1: 120-124 Simplex method-2: Objective: The T2: 47-51 19. Artificial variables and main objective of 14-03-17 standard form of LPP by this unit is to find using artificial variables. optimal value of the LPP by using Big-M T1: 120-123 Big-M method algorithm 20. method and two- T2:51-56 15-03-17 for solution of LPP phase method. Also need of artificial Solution of LPP by using variables. T1: 124-126 21. Big-M method 16-03-17 Application: Penalty method with more Industry, economics, T1: 12 22. than two variables with agriculture, defense T2:50-58 17-03-17 more than two constraints for finding optimal value No feasible solution case T1: 126-129 23. by using penalty method 20-03-17 Solution of LPP when T1: 592-597 24. inequality having equal Outcome: Able to 21-03-17 sign understand the Post optimality analysis artificial variables T1: 134-145 25. and for solving LPP 22-03-17 having equal to sign Unit Test (UT)-1 by suing Big-M and T1: 142-149 26. two-phase method. 23-03-17 UNIT – 4 : T1:174-180 Revised Simplex Method: T2: 90-92 Introduction 27. 27-03-17 Algorithm for two-phase simplex method Objective: To find solution of LPP by Solution of given LPP by revised simplex T1:180-190 28. using two-phase simplex method and essence T2: 92-95 28-03-17 method of duality theory Finding degenerate feasible T2: 95-98 29. solution by using two- 30-03-17 phase simplex method Application: Miscellaneous cases of two Design problems, T2: 100-102 30. phase method defense, 31-03-17 Revised simplex method bioengineering T2:100-102 31. algorithm 03-04-17 Solution of LPP by using T1: 179-184 32. revised simplex method Outcome: 04-04-17 To understand the Previous question papers revised simplex discussion method for solution of LPP. 33. 05-04-17 UNIT – 5 :Duality in T1: 21-215 linear programming : 34. 06-04-17 Primal and dual problem. Definition of dual problem. Algorithm for converting T1:217-220 35. primal to dual problem and T2: 69-72 07-04-17 also special cases of duality Dual simplex method T1:26-270 algorithm T2: 72-75 Objective: It presents the concept 36. 10-04-17 of duality and solution of dual problem by dual simplex method. Solution of LPP by using T2: 78-81 37. 11-04-17 dual simplex method. Solution of different types T2: 81-84 38. of LPP by using dual 12-04-17 simplex method Application: Concept of sensitivity Marketing, T2: 85-89 39. analysis production 13-04-17 management, Special cases of sensitivity military operations T2: 78-89 40. analysis and problems. 20-04-17 Unit test-2 Outcome: Able to understand duality and dual simplex 41. method 21-04-17 Unit -6: Transportation Objective: To know T1:332-335 and assignment problem: the importance of T2: 144-146 42. Introduction of TP and AP, transportation and 24-04-17 mathematical formulation. assignment problem Solution of TP by NWCM for optimization Algorithm of minimum T1:337-339 43. cost method for finding Application: T2: 144-148 25-04-17 solution of given TP Agriculture, research Penalty method for finding and development, T1:340-348 44. solution of TP defense , industry T2: 148-151 26-04-17 Numerical examples of all Outcome: To find T1:332-335 methods for finding TP and T2: 152-154 45. the solution of 27-04-17 comparison of optimal cost transportation and of transportation assignment problems Concept of assignment (both balanced and T1:332-335 46. problem. Also difference unbalanced T2: 154-157 28-04-17 between TP and AP problems) for Hungarian method for minimizing cost of T1:345-350 47. 02-05-17 solution of AP transportation T2: 145-154 Miscellaneous problems of T1:367-384 48. AP and TP T2: 157-168 03-05-17 UT-3 49. 04-05-17 Unit -7: Game theory: T1:715-717 Introduction of game Objective: It T2: 428-430 50. theory, features, limitations focuses on modeling 05-05-17 and pay off matrix. of pay-off matrix and the solution of Maxima and minima game by different T1:717-720 principle of pay off matrix. methods for value of T2: 430-433 51. 08-05-17 also finding value of the the game. game using saddle point Games with pure strategies T1:718-720 52. 09-05-17 and solution of gamer Application: T2: 429-434 Dominance principle with Artificial T1:720-724 53. examples intelligence, T2: 431-434 10-05-17 Graphical method for resource allocation T2: 435-438 54. 11-05-17 finding value game and networking Algorithm for solving mx2 Outcome: To T2: 438-440 55. game understand the 12-05-17 concept of game and Concept of decision its solution by T1:720-730 56. making analysis different methods for 18-05-17 optimal strategies Tutorial (miscellaneous T2: 430-445 57. problems) 19-05-17 Unit test or discussion T2: 430-445 various illustrations of 58. games. 22-05-17 Revision/Class Test UNIT – 8 : Metaheuristics T1:670-672 Concept of heuristics and Objective: To 59. metaheuristics. Also understand the 23-05-17 objective and advantages. nature of metaheuristics and Simulated annealing. tabu search T1: 673-675 60. Algorithm of annealing. algorithms 24-05-17 Application: Travelling salesman algorithms, T1: 670-679 61. 25-05-17 problem computer vision. Pseudocode of tabu search Outcome: Able to T1: 680-685 62. algorithm Understand how to 25-05-17 solve travelling Concept of genetic T1: 685-690 salesman problems 63. algorithm 26-05-17 by Tabu search Problems using genetic algorithm and T1: 690-707 64. algorithm necessity of genetic 29-05-17 algorithms 65. 30-05-17 Revision VTU Question papers discussion , 66. Revision 01-06-17 doubt clarification, 67. solving problems 02-06-17 Revision Prepared By: J.Somasekar Reviewed by: SUPARNA Approved by: __________ Approved by: _____________ (Faculty) (Sub. expert) (HOD) (Principal/ Acad. Co) Date & Sign_______________ Date & Sign______________ Date & Sign __________ Date & Sign ______________ .