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Faculty of Science Handbook, Session 2017/2018

SIN3006 PRODUCTION AND INVENTORY SYSTEM programming. Cutting plane. Multi-objectives linear goal
programming. Graphical. Simplex iterative and modified
The importance of inventory in management. Advanced methods.
EOQ models. Inventory model for time-dependent demand:
linear increase or decrease cases. Exact and approximate Assessment
methods by minimizing ordering and holding costs. Continuous Assessment: 40%
Applications to real-world problems. Final Examination: 60%
Assessment Medium of Instruction:
Continuous Assessment: 40% Bahasa Malaysia/English
Final Examination: 60%
Humanity Skill:
Medium of Instruction: CS4, CT3, LL2, TS2
Bahasa Malaysia
References
Humanity Skill: 1. Markland, R.E & Sweigart, J.R, Quantitative Methods:
CS3, CT3, LL2 Applications to Managerial Decision Making , John
Wiley & Sons. 1987
References 2. Moore, L.J, Lee, S.M & Taylor, B.W, Management
th
1 . Hamdy A. Taha(2011), An Introduction to Operational Science, 4 edition, Allyn and Bacon. 1993
Research, 8 , New York, Mcmillan 3. Taha, H.A, Operations Research: An Introduction, 5 th
th
2. E. Naddor(1966), Inventory Systems, J. Wiley. edition, Macmillan Pub. Co. (edisi Bahasa Malaysia
3. Hadley G. and Whitin T.M.(1963), Analysis of oleh USM-DBP). 1992
Inventory Systems, Prentice-Hall, Inc., Englewood 4. Winston, W.L, Operations Research: Applications and
Cliffs, New Jersey. Algorithms, Third Edition. Duxbury Press, 2013.
4. C.D.J. Waters(2003), Inventory Control and
Management, University of Calgary, Canada.
5. Hillier, Frederick S. (2005), Introductory to Operations SIN3009 INDUSTRIAL OPERATIONAL RESEARCH
Research, 8th edition, New York, McGraw-Hill.
Definition of a network. Node, branch, path, chain, cycle
and circuit. Examples of network flow model. Network flow:
SIN3007 HEURISTIC METHODS Shortest path, minimum spanning tree, maximum flow and
minimum cost maximum flow. Activity Network: Critical path
Introduction. Descent Heuristics: random solutions, greedy model method: Earliest and Latest time, slack activities and
solutions, exchange heuristics. Improvement Heuristics: critical path. Project valuation. Optimal path. Project
Local optimization, iterated local search, simulated scheduling. Network model as an example of a linear
annealing, tabu search. Artificial Intelligence: Genetic programming model.
algorithm, evolutionary algorithm, artificial neural network.
Evaluating heuristics. NP Completeness. Assessment
Continuous Assessment: 40%
Assessment Final Examination: 60%
Continuous Assessment: 40%
Final Examination: 60% Medium of Instruction:
Bahasa Malaysia/English
Medium of Instruction:
Bahasa Malaysia/English Humanity Skill:
CS4, CT3, LL2, TS2
Humanity Skill:
CT4, LL2 References
1. Groebner, D.F & Shannon, P.W (1991), Introduction to
References Management Science, International Edition, Dallen-
1. S. S. Skeina, The Algorithm Design, Springer-Verlag, Macmillan-Maxwell.
1997. 2. Lipin, L.L (1994), Quantitative Methods for Business
2. Ashraf Aboshosha, Yaser KhalifaGenetic Algorithms Decisions (with cases), 6th edition. Dryden Press.
Theories and Applications: Evolutionary Algorithms, 3. Taylor, B.W (1993), Introduction to Management
Optimization Techniques, Heuristics, Artificial Science, Allyn and Bacon.
Intelligence, Biologically inspired Algorithms,LAP 4. Winston, W.L, Operations Research: Applications and
LAMBERT Academic Publishing, 2012. Algorithms, Third Edition. Duxbury Press, 2013.
3. Z. Michalewicz, D.B. Fogel, How To Solve It: Modern
Heuristics, Springer-Verlag, 2005.
4. I.Osman and P. Kelly, Met-Heuristics: Theory and SIN3010 COMPUTATIONAL GEOMETRY
Applications: Kluwer, 1996.
5. E. Rich and K. Knight, Artificial Intelligence, Vector algebra, introduction to differential geometry, design
International Edition, McGraw-Hill Inc., 1991. surfaces for Bezier surfaces, triangular Bezeir surfaces, B-
6. Z. Michalewicz, Genetic Algorithms + Data Structures Spline, rational Bezier and Coons surfaces.
= EvolutionProgrammes, Springer-Verlag, 1992.
Assessment
Continuous Assessment: 40%
SIN3008 MATHEMATICAL PROGRAMMING Final Examination: 60%
The matrix of simplex theory and sensitivity analysis. Medium of Instruction:
Parametric linear programming. Revised simplex method. Bahasa Malaysia/English
The technique of upper bounded variables. Karmarkar’s
interior point algorithm. Dantzig-Wolf decomposition Humanity Skill:
principle. Pure, mixed and binary (0-1) integer CT3, LL2, CS2, TS2

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