LOG307 Optimisation And Simulation For Decision-Making Assignment Sample SUSS Singapore

The use of quantitative models in today’s highly competitive and complex business environment has become an essential tool for success. LOG307 Optimisation and Simulation for Decision Making stresses the analysis, development as well as solving of these mathematical equations to support decision making with a focus placed heavily upon supply chain management problems that need solutions such as optimizing output levels or predicting customer behaviour patterns based on data collected from past conductance experience.

This course provides students with a strong foundation in mathematical modelling, statistical analysis and computer simulations. Students will learn how to build and interpret quantitative models to help inform business decisions. They will also develop skills in using software packages to support their work, including Excel, VBA and Arena.

The course will teach you how to make decisions under uncertainty and use metaheuristic algorithms for problem-solving. You’ll learn about spreadsheet modelling, which is important in today’s world where data governs everything. You will also get to grips with different optimisation methods, such as integer programming and constraint programming. The course culminates in a group project where you’ll apply all the skills and knowledge you’ve learned to solve a real-world problem.

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Assignment Brief 1: Apply linear programming models to support decision-making in the industry.

Linear programming is a mathematical optimization technique that can be used to identify the best possible solution to a problem by finding the optimal balance between competing factors.

In business, linear programming can be used to model and optimize various decision-making scenarios such as production planning and scheduling, resource allocation, marketing mix strategies, and financial planning. By taking into account all the relevant constraints and objectives of a problem, linear programming can help businesses find the most efficient way to achieve their goals.

Linear programming can be applied to problems with multiple objectives, constraints, and variables. The first step in solving a linear programming problem is to identify all the relevant factors that need to be considered. Once all the relevant information has been gathered, it must be converted into a mathematical model that can be solved using optimization techniques. The next step is to solve the linear programming model to find the optimal solution that meets all the constraints and objectives.

Assignment Brief 2: Solve integer linear programming models to optimise various business decisions.

Integer linear programming (ILP) models are used to optimise various business decisions. By utilising the powerful optimisation techniques of ILP, businesses can boost their efficiency and performance. Some key benefits of solving ILP models include:

1. Finding the best possible solution to a problem: Unlike other mathematical programming techniques, which can only find a sub-optimal solution, ILP can find the optimal solution to a problem. This is extremely useful for businesses that need to make optimal decisions to maximise their profits or minimise their losses.
2. Handling constraints efficiently: ILP is very good at handling constraints, which is essential in many business situations. For example, when considering production timetable optimisation generated, ILP can take into account constraints such as machine availability, workers’ shift patterns and material delivery times.
3. Modelling real-world problems: ILP models can be used to model real-world business problems very closely. This means that the solutions by ILP are more likely to be effective in the real world.

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Assignment Brief 3: Appraise the role of nonlinear programming and metaheuristics in solving complex supply chain problems.

There is no doubt that nonlinear programming and metaheuristics can be extremely helpful in solving complex supply chain problems. In many cases, these techniques are the only way to obtain accurate solutions in a reasonable amount of time.

However, it is important to note that not all supply chain problems can be solved using these methods. In some cases, linear programming or other simpler methods may be more effective. It depends on the specific problem at hand.

Metaheuristics are especially well-suited for solving problems that are too difficult for traditional methods like linear programming. These techniques can often find near-optimal solutions in a fraction of the time it would take to find an exact solution.

In general, nonlinear programming and metaheuristics can be extremely useful tools for solving complex supply chain problems. However, it is important to select the right technique for the specific problem at hand. Sometimes simpler methods may be more effective.

Assignment Brief 4: Analyse different simulation tools for various complex problems.

Different simulation tools are better for different complex problems. 

For example, computational fluid dynamics (CFD) is a simulation tool used to analyze the flow of fluids, while structural analysis is used to analyze the stability and strength of structures. Other simulation tools include finite element analysis (FEA), which is used to study the response of materials and structures to various loads, and computer-aided engineering (CAE), which is used to improve the design process by predicting product performance.

Each of these simulation tools has its strengths and weaknesses, so it’s important to choose the right tool for the job. For example, if you need to analyze the flow of fluids, CFD would be a better choice than FEA.

In general, CFD is better for analyzing fluid flow, while FEA is better for analyzing the response of materials and structures to loads. CAE can be used for both purposes, but it is most commonly used in the design process.

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Assignment Brief 5: Construct simulation models to solve business problems.

There are many different types of business problems that can be solved through simulation modelling. For example, if you want to see how a new product will perform in the market, you can create a simulation model to test various marketing strategies. Or if you want to optimize your manufacturing process, you can create a model to determine the most efficient production line.

Simulation models allow you to test different scenarios and see which one provides the best results. This is especially useful for businesses that are making decisions about complex systems, such as supply chains or energy grids. By simulating different options, businesses can find the option that is most likely to succeed while avoiding costly mistakes.

If you’re interested in learning more about simulation modelling, there are many resources available online. For a general introduction, you can check out this article from the Harvard Business Review. If you want to dive deeper, there are a variety of software packages that offer more sophisticated modelling capabilities. Whichever route you choose, simulation modelling can be a powerful tool for solving business problems.

Assignment Brief 6: Demonstrate how to make decisions under uncertainty.

There is no one answer to this question since it depends on the individual and the specific situation. However, some general guidelines can be useful in making decisions under uncertainty. 

First, it is important to be aware of your own biases when making decisions. We all have different ways of interpreting information and making judgments, and these tendencies can lead us astray if we’re not careful. Recognizing your own biases can help you account for them when making decisions. 

Second, it is often helpful to consult with others who may have more knowledge or experience than you do in the area you’re uncertain about. Getting input from others can help you surface new information and ideas that you may not have considered on your own. 

Finally, it is important to be comfortable with making decisions that are not based on 100% certainty. In many cases, we have to make decisions with incomplete information and uncertainty is inherent in life. The key is to be as informed as possible and then make the best decision you can given the circumstances.

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