Mathematical modelling in supply chains is an interactive computer-based module that serves the decision-making needs of engineering managers and analysts. It provides them with information that enables them to make both semi-structured and unstructured decisions. It employs various analytical models to perform a low-level analysis of data and produce information. In this module, it is aimed: To provide students with knowledge on mathematical models applicable to supply chains. To provide students with modeling and software capabilities to apply mathematical models to supply chains.

This module is an interactive computer-based system that serves the decision-making needs of engineering managers. It provides them with information that enables them to make both semi-structured and unstructured decisions. It employs various analytical models to perform a low-level analysis of data and produce information. Introduction to Operations Research Origins of operations research, example applications of mathematical modeling in supply chains, process of applying mathematical models, overview of mathematical model types, overview of software used in mathematical modeling. Mathematical Modeling - Software: Introduce and provide students with base skills to use software to solve mathematical models. The focus is primary on introducing the student to spread sheet modeling, but brief introductions to other modeling and optimization software will be given. Students will apply software modeling skills obtained here to subsequent topics. Linear programming Basic definition of Linear programming, demonstrate method via graphical method, model formulation applications in supply chains. Linear programming solution Simplex method, Artificial starting solution method, interpretation of simplex tableau, sensitivity analysis. Network models Transport model, Assignment model, Shortest Route model, Network Minimisation model, Maximum Flow Model, Transhipment model Integer programming Binary and integer applications in supply chains, solution methods, branch-and-bound, heuristics solution methods, genetic algorithms and simulated annealing.

On successful completion of this module students will be able to: 1. Relate the theoretical underpinnings of decision analytics to managerial decision making. 2. Develop mathematical models of basic manufacturing and logistics considering their sustainability aspects. 3. Apply various solution algorithms to solve linear, network, integer, and binary programming models. 4. Utilize mathematical computer software to solve modelled problems. 5 Demonstrate a sufficient level of competency in applying the covered mathematical models to a variety of real-world business operational problems and projects.

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During each teaching week, recent research activities related to the topics covered that week are discussed in a forum and scientific research articles are provided as extra reading materials. The current structure follows UL's Integrated Curriculum Development Framework (ICDF), which is challenge-driven, research-led, and collaborative. This module's topics align with Sustainable Development Goal 12: responsible consumption and production. We cover various topics related to optimising supply networks that help with the efficient use of resources, which are directly aligned with SDG 12's aims and objectives. The following graduate attributes are considered in developing this module curriculum: Articulate: Through the integration of weekly e-tivities, a collaborative environment is introduced where learners are exposed to group work in finding solutions for weekly introduced e-tivity problems. Agile: The two individual assignments provide authentic assessment components, and the case study project provides a means for problem/ project-based learning. The learners define their own case studies and then apply the content covered in the module to solve the defined case study problem. Courageous: The weekly e-tivities provide an opportunity for the learners to express their opinions and solutions without any formative assessment grade. This will allow them to be creative and innovative in presenting their work solutions. Other peers have the opportunity to comment on each other's solutions, providing a cooperative learning environment. Curious The project assignment allows the learners to define problems and case studies that require them to critically examine their understanding of the knowledge covered in this module. Extra reading materials are provided each week to further educate those learners who really want to understand the practical aspects of the covered topics.