| CODE | MGT2031 | ||||||||
| TITLE | Operations Research | ||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 5 | ||||||||
| ECTS CREDITS | 4 | ||||||||
| DEPARTMENT | Business and Enterprise Management | ||||||||
| DESCRIPTION | This study-unit introduces students to key Operations Research (OR) techniques used in business decision-making, with a strong emphasis on practical application. Students will explore how mathematical models support complex problem-solving in areas such as resource allocation, project management, and inventory control. Topics include linear programming, project scheduling, network models, inventory analysis, and simulation. A hands-on component using Excel ensures students can apply theoretical concepts to real-world problems and develop electronic models to support decision-making. This study-unit provides a foundation for applying analytical tools to managerial challenges and forms an essential skillset for data-informed business practice. Study-Unit Aims: The study-unit aims to: - Equip students with foundational knowledge of operations research tools relevant to business and management; - Develop students鈥� ability to model and solve business problems using quantitative methods; - Enable students to use Excel as a modelling and problem-solving tool in operations research contexts; - Strengthen analytical thinking and structured decision-making skills. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Identify key concepts, techniques, and applications of operations research in business; - Explain the methodology behind common OR models such as linear programming, PERT/CPM, and simulation. 2. Skills: By the end of the study-unit the student will be able to: - Formulate and solve linear programming, project scheduling, and inventory problems using Excel; - Apply network analysis to solve operational problems such as shortest route and maximum flow; - Conduct basic simulation exercises to model business scenarios; - Interpret outputs from OR models to support informed managerial decisions. Indicative Content: - Introduction to Quantitative Analysis (QA): an overview of the QA and its importance in decision-making; the QA approach and its steps; developing a QA model; potential challenges in applying the QA approach; the role of implementation as an integral part of the process; and an introduction to break-even analysis as an application of QA; - Linear Programming: Graphical solutions; constraints; maximisation and minimization; Excel Solver; and sensitivity analysis; - Project Management: Activity-on-node (AON); PERT; CPM; project crashing; and Excel modelling; - Network Models: Minimum spanning tree technique; maximal flow technique; shortest path methods; and Excel applications; - Inventory Analysis: Economic Order Quantity (EOQ); Production Order Quantity (POQ), Quantity Discounts; and Use of Safety Stock; - Simulation: Monte Carlo method with applications to queues, inventory control, and maintenance policy; and Excel simulations. Main Text/s and any supplementary readings: Main Texts: - Render, B., Stair, R.M., Hanna, M. E. & Hale, T.S. (2023). Quantitative Analysis for Management (14th ed., Global Edition). Essex: Pearson Education. Supplementary Readings: - Anderson, D.R., Sweeney, D.J., Williams, T.A., Camm, T.A., Cochran, J.J., Fry, M.J. & Ohlmann, J.W. (2021). An Introduction to Management Science: Quantitative Approaches to Decision Making (15th ed.). Boston, MA: Cengage Learning. - Taylor, B.W. (2022). Introduction to Management Science (13th ed., Global Edition). Harlow: Pearson. |
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| STUDY-UNIT TYPE | Lecture | ||||||||
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| LECTURER/S | Frank H. Bezzina |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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