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Full Description
Guiding you through the key stages of a simulation project, this successful textbook provides a comprehensive introduction to the discipline.
The textbook enables you to develop conceptual models, perform simulation experiments, analyse the results and draw insightful conclusions. Simulation takes a non-software specific view and can be used regardless of individual software preferences. Almost all of the techniques described also apply to the use of non-specialist simulation software, such as programming languages and spreadsheets.
In line with recent developments in the field of simulation, this updated edition details contemporary advancements such as hybrid simulation, multiple recursive random number generators and digital twins. A new chapter has also been added on single-use systems, which solidifies the text's holistic approach to simulation studies.
Critical engagement is encouraged through the inclusion of practical case studies and complementary online resources. Additionally, each simulation model is accompanied by illustrative examples, comparative discussions and references, all of which serve to promote deeper understanding of each depicted model. Every chapter is bookended by key concept definitions at the start of each chapter, and summaries at the end, making it an invaluable learning companion for simulation studies.
Packed full of practical tips that can be immediately employed in any model development cycle - regardless of your knowledge level - Simulation is an ideal textbook for students studying simulation on upper level undergraduate and postgraduate degree courses in Business and Management, Operations Management, Logistics and Supply Chain Management, Computer Science, Engineering, Mathematics and Statistics. The text would also be an invaluable resource for practitioners in the area, such as business analysts.
Contents
1. Defining simulation: what, why and when?
2. Inside simulation software
3. Software for simulation
4. Simulation studies: an overview
5. Conceptual modelling
6. Developing the conceptual model
7. Data collection and analysis
8. Model coding
9. Experimentation: obtaining accurate results
10. Experimentation: searching the solution space
11. Implementation
12. Verification, validation and confidence
13. The practice of simulation
