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MS3103 Resource Allocation Techniques
Part I Course Duration : One Semester
Credit Units : 3
Level: B3
Medium of Instruction: English
Prerequisite(s) : Nil
Precursors(s) : Nil
Equivalent Course(s) : Nil
Exclusive Course(s) : MS3401 Practical Management Science
Part II
Course Aims:
- provide students with a basic grounding in management science/operations research techniques, focusing on problem formulation, model construction, finding a quantitative solution, interpretation and performing of sensitivity analysis for deterministic problems using spreadsheets
- train students to be able to apply single- or multi-objective models, network optimization models to deterministic problems (e.g., resources allocation, transportation planning, portfolio selection, etc.) and realize the model assumptions
- enable students to formulate problems with nonlinear functions, develop solutions, and recognize the optimal properties of nonlinear programming models
- enhance the skills of using spreadsheets to implement decision-making models and interpret results
Course Intended Learning Outcomes (CILOs)
Upon successful completion of this course, students should be able to: | No. | CILOs | Weighting | | 1 | Demonstrate basic knowledge of management science operations research theories by identifying different types of business problems, e.g., resource allocation, inventory management, supply chain planning | - | | 2 | Apply the management science approach to problem-solving: Identify and formulate problems, make appropriate assumptions to develop models and solutions. Verify and validate model results. (Ability) | - | | 3 | Recognize the inter-relationship between supply chain partners and their functional activities in the framework of a network optimization model | - | | 4 | Explain the characteristics of multi-objective problems and apply different multi-objective approaches appropriately | - | | 5 | Demonstrate competence in using appropriate software (e.g., Excel and Solver add-ins) to develop a model, generate solutions and perform sensitivity analysis | - |
Teaching and Learning Activities (TLAs)
(Indicative of likely activities and tasks designed to facilitate students’ achievement of the CILOs. Final details will be provided to students in their first week of attendance in this course)
| CILO No. | TLAs | Hours/week (if applicable) | | 1,2,3,4,5 | 1.Lectures
Concepts and applications of various quantitative methods to problem solving and decision making are explained. In-class exercises are designed to test students’ understanding of the concepts and methods learned in the lectures. | - | | 1,2,3,4,5 | 2. Tutorial
Tutorial exercises are designed to develop students’ analytical skills in problem formulation, solution generation and interpretation. These are take-home exercises for students to practice after a lecture and to share and participate in during class discussions. Group presentation enhances communication and team work skills. | - | | 1,2,5 | 3. Assignment
Case assignments are used to provide training in analysing complex problem situations and solving business problems. Students are required to work in groups, observe existing practices and/or conduct research on related applications. They are expected to apply methodologies learned to solve a business-related problem. Findings are presented and a report is submitted, including a reflection upon their learning experiences and challenges. | - |
Constructive Alignment of CILOs and TLAs
| | TLA 1 | TLA 2 | TLA 3 | Hours/week (if applicable) | | CILO 1 | ü | ü | ü | - | | CILO 2 | ü | ü | ü | - | | CILO 3 | ü | ü | | - | | CILO 4 | ü | ü | | - | | CILO 5 | ü | ü | ü | - |
Assessment Tasks/Activities
(Indicative of likely activities and tasks designed to assess how well the students achieve the CILOs. Final details will be provided to students in their first week of attendance in this course)
| CILO No | Type of Assessment Tasks/Activities | Assessment Details | Weighting (if applicable) | | - | 1. Written Examination | The examination is designed to assess students’ abilities in problem formulation, model construction and development of a quantitative solution with logical interpretation. | 60% | | - | 2. Assignment | Students work together to analyse a business problem, research similar problems and solutions and design their own solution. The deliverable is a written report, including a reflection upon their learning experiences and challenges. | 20% | | - | 3. Mid-term test | The test is designed to assess students’ abilities in problem formulation, model construction and development of a quantitative solution with logical interpretation. | 10% | | - | 4. Group presentation | Take-home problems are designed for students to analyse and make a presentation to share during tutorial. They test students’ understanding of the concepts and methods learned, team work and presentation skills. | 10% |
Constructive Alignment of CILOs and Assessment Tasks
| | AT1 | AT2 | AT3 | AT4 | | CILO 1 | ü | ü | ü | ü | | CILO 2 | ü | ü | ü | ü | | CILO 3 | ü | | | ü | | CILO 4 | ü | | | ü | | CILO 5 | ü | ü | ü | ü |
Grading of Student Achievement :Refer to Grading of Courses in the Academic Regulations (Attachment) and to the Explanatory Notes.
AT1: Written Examination
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Student who is profiting from the university experience; understanding of the subject; ability to show some evidence of familiarity with literature. | | D | 1.0 | Marginal: | Sufficient familiarity with the subject matter to enable the student to progress without repeating the course. | | F | 0.0 | Failure: | Little evidence of familiarity with the subject matter; weakness in critical and analytic skills; limited or irrelevant use of literature. |
AT2: Assignment
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of understanding the key concepts and definitions of the learned subject; capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Student who is profiting from the university experience; understanding of the subject; ability to show some evidence of familiarity with literature. | | D | 1.0 | Marginal: | Sufficient familiarity with the subject matter to enable the student to progress further. | | F | 0.0 | Failure: | Little evidence of familiarity with the subject matter; limited or irrelevant use of literature. |
AT3: Mid-term test
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of original thinking; good organization, capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Some evidence of grasp of subject, little evidence of critical capacity and analytic ability; reasonable understanding of issues. | | D | 1.0 | Marginal: | Sufficient familiarity with the subject matter to enable the student to progress without repeating the case report. | | F | 0.0 | Failure: | Little evidence of familiarity with the subject matter; weakness in critical and analytic skills; limited or irrelevant use of literature. |
AT4: Group Presentation
| Letter Grade | Grade Point | Grade Definitions | | A+
A
A- | 4.3
4.0
3.7 | Excellent: | Strong evidence of understanding the key concepts and definitions of the learned subject; capacity to analyse and synthesize; superior grasp of subject matter; evidence of extensive knowledge base. | B+
B
B- | 3.3
3.0
2.7 | Good: | Evidence of grasp of subject, some evidence of critical capacity and analytic ability; reasonable understanding of issues; evidence of familiarity with literature. | C+
C
C- | 2.3
2.0
1.7 | Adequate: | Student who is profiting from the university experience; understanding of the subject; ability to show some evidence of familiarity with literature. | | D | 1.0 | Marginal: | Sufficient familiarity with the subject matter to enable the student to progress further. | | F | 0.0 | Failure: | Little evidence of familiarity with the subject matter; limited or irrelevant use of literature. |
Part III Keyword Syllabus:
1. Introduction
The nature of Management Science / Operations Research and overview of its modelling approach.
2. Linear Programming Models
Linear programming applications. Computer packages. Sensitivity analysis and computer solution. Special types of linear programming problems: transportation, transshipment and assignment problems. Limitations of Linear programming models.
3. Network Models
Shortest route through a network. Minimal spanning tree for a network. Maximal flow through a network. Minimum cost network flow model.
4. Integer Programming Models
Formulations. Integer programming applications. Piecewise linear functions.
5. Decision Making with Multiple Objectives
Multi-objective vs single-objective decision making. Goal programming formulations. Pareto optimality and tradeoff curves. The Analytic hierarchy Process.
6. Nonlinear Programming
Review of differential calculus. Convex and concave functions. Unconstrained optimization. Quadratic programming.
Related Links
Department of Management Sciences
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