![]() The interest in intra-firm improvement and excellence has more recently been extended to address also how firms can improve their operations in coordination with their supply chain counterparts. The Operations Management discipline has traditionally been concerned with how organisations achieve excellence in processes and operations, and to this end several (continuous) improvement approaches and tools and techniques have been stressed. Intelligent Data Analysis and Visualisation.GAMS, AMPL, MPL) and/or programming interfaces (PYOMO, CPLEX Concert, Gurobi Python Interface). The main topics covered are: facility location, network design, warehousing, vehicle routing and scheduling, and Terminal (airport) capacity management.ĭepending on students need and level of programming skill, the computer workshops will focus on either solver languages (e.g. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed. There will be a focus on modelling, the use of professional software, and the understanding of results. Algebraic formulations will be used as vehicle for describing models and discussing their relationships. Emerging logistical concepts will be introduced and the associated mathematical modelling needs will be discussed. The purpose of this course is to understand and use mathematical models in making strategic, tactical, and operational logistics decisions. A number of fundamental concepts, such as probabilistic variations, steady state behaviour and time-dependent behaviour are common to both areas. On the other hand, simulation can be used to investigate the sensitivity of stochastic models to some of their underlying assumptions. Insights from stochastic modelling can help in the design of simulation models. Simulation and stochastic modelling are inter-related in several ways. The application of these methods requires careful consideration of the dynamics of the real-world situation being modelled, and (in particular) the way that uncertainty evolves. Stochastic modelling methods provide analytical tools which enable Operational Researchers to gain insight into complicated and unpredictable real-world processes. Modern simulation packages are a valuable aid in building a simulation model and this module will emphasise the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this. For example, you will experience a real-life analytics project as part of your course.Ĭomputer simulation methods are among the most commonly used approaches within Operational Research. We place a very high emphasis on creating graduates with skills that are valuable in the job market. We constantly update this programme to reflect the dynamically changing landscape of business analytics. We have excellent partnerships with industry business analytics practitioners. Our programme is one of the few to teach the entire Business Analytics life cycle, covering Descriptive, Predictive and Prescriptive analytics. This involves generating relevant business insights using data-driven methodologies and tools. Not only do you learn the theory of business analytics but also how to apply it in practice. This course will train you in analytical decision-making. If you want a career in decision support, operational research, industrial engineering or management science, this is the ideal programme for you. Data is collected about everything in order to construct, operate and maintain systems. We live in a world where analytical decision-making happens every second.
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