In this blog you will find the correct answer of the Coursera quiz Business Analytics for Decision Making Coursera Week 4 Quiz mixsaver always try to bring the best blogs and best coupon codes
Week 4 Quiz
Which of the following is not a benefit of using binary variables?
- Models are easy to solve (i.e., the solvers can find optimal solutions faster) because the variables can only be zero or one.
- Binary variables are useful in selection problems.
- Binary variables can be used to model yes/no decisions.
- Binary variables can enforce logical conditions.
An optimization model has 5 binary decision variables. How many possible integer solutions are there to this problem?
A company wants to select no more than 2 projects from a set of 4 possible projects. Which of the following constraints ensures that no more than 2 will be selected, assuming that the P variables are binary and represent whether a project is selected (value of 1) or not (value of 0)?
- P1+P2+P3+P4 = 2
- P1+P2+P3+P4 ≤ 2
- P1+P2+P3+P4 ≥ 2
- P1+P2+P3+P4 ≥ 0
A company must invest in project 1 in order to invest in project 2. P1 is a binary variable representing whether project 1 is chosen (value of 1) or not (value of 0). P2 has the same interpretation for project 2. Which of the following constraints ensures that if project 2 is chosen then project 1 must also be chosen?
- P1+P2 = 0
- P1+P2 = 1
- P1-P2 ≥ 0
- P1-P2 ≤ 0
An optimization model for a production process must deal with the following situation. The model must decide whether or not to produce a product. If the decision is to produce the product, then the policy is that at least 100 units of this product must be produced. The following Excel cells are part of a spreadsheet model for this problem:
Cell B1 contains a binary decision variable, where 1 = produce and 0 = not produce. B4 is a decision variable indicating the amount to produce. Which of the following combination of an Excel function for B3 and a solver constraint enforces the production policy?
- =B1*B2 and B4 >= B3
- =B1*B3 and B3 >= B4
- =B1+B2 and B4 >= B3
- =B1*B4 and B3 >= B2
Which of the following statements is not true about metaheuristic optimization?
- Metaheuristics provide great modeling flexibility.
- Metaheuristics can solve optimization models with nonlinear and/or non-smooth functions.
- The metaheuristic solver in the Analytic Solver Platform is called the Evolutionary Engine.
- Metaheuristics are exact procedures that guarantee finding an optimal solution.
In market basket analysis, the Lift Ratio tells us how much more likely it is for item Y to be purchased given that item X has been purchased ?
A chance constraint is a special type of constraint that it is satisfied only in a fraction of the trials in a simulation.
An optimization model includes a chance constraint to satisfy demand of a particular product. The demand is uncertain and is modeled with an integer uniform distribution with parameter value of 0 and 4. That is, the probability that the demand is 0, 1, 2, 3, or 4 is exactly the same. A decision is made to order 2 units of the product from a supplier in order to satisfy the uncertain demand. What is the value at risk (VaR) for the demand constraint?
Week 4 Application Assignment – Simulation Optimization
A technology company has $2 million to invest in new research and development projects. The following table summarizes the initial cost, probability of success, and revenue potential for each of the projects under consideration.
Management has built the Monte Carlo simulation model in the Excel file Project Selection SO and would like to find the portfolio that maximizes the probability of making at least $1 million in profits. Questions 1, 2, and 3 guide you through the implementation of an optimization model. Add the optimization model as you answer these questions. (Hint: The three elements of the optimization model, decision variables, constraints, and the objective function, are of the “Normal” type. Also turn on the Simulation Bulb in the Solver Action group of the Analytic Solver Platform.)
Portfolio Selection SO.xlsx
The decision variables in the optimization model are:
- Select? (H5:H12)
- Success? (I5:I12)
- Revenue (J5:J12)
- Profit (K5:K12)
The constraints in the optimization model are:
- Revenue >= Profit (J5:J12 >= K5:K12)
- Select? >= Success? (H5:H12 >= I5:I12)
- Total cost <= Available funds (H14 <= H15) and binary variables
- Total profit >= Probability that the total profit is at least $1 million (K14 >= K15)
The objective function in the optimization model is:
- Minimize total cost (Min H14)
- Maximize the probability that the total profit is at least $1 million (Max K15)
- Maximize total profit (Max K14)
- Maximize available funds (Max H15)
Use the Evolutionary Engine to solve the optimization model that results from your answers to questions 1, 2, and 3. (Make sure that the number of trials is set to 10000.) Compare the solution that you found with the following solutions. Which of the following solutions is the best? (Hint: The Evolutionary Solver might not have found the best solution, so try all these solutions in your model before answering the question.)
- Projects 1, 2, 3, 6, 7, and 8
- Projects 2, 4, 5, 6, and 8
- Projects 1, 2, 3, 5, 6, and 8
- Projects 1, 2, 3, 4, and 7
- Business Analytics for Decision Making Coursera Week 1 Quiz
- Business Analytics for Decision Making Coursera Week 2 Quiz
- Business Analytics for Decision Making Coursera Week 3 Quiz