Hello!Attached are the questions that need to be on 1 Excel document and clearly labeled (1a, 1b, 1c etc..). Each question should be done on a separate sheet (for a total of 4 sheets).

Please answer the following questions using a minimum of 250 words for each question. Each question should include at least one reference. Topic 3 DQ 1Description:Before computers were widespread, almost all risk analysis was done without simulation. Therefore,only a handful of scenarios could be formulated to understand the risk of a decision. Typically, abest-case and worst-case scenario was determined and decisions were based on these twoscenarios. What are some of the drawbacks of this decision-making approach? Specifically, howdoes the capability to summarize 1,000s of simulated scenarios improve the approach? Topic 3 DQ 2Description:By definition, simulations require a distribution to be specified (e.g., normal, Poisson). Many times,the exact distribution to be used is unknown, so it must be assumed. One argument against usingsimulations to perform risk analysis is that there is no real benefit because the set of assumptions issimply shifted from assumed parameter values to assumed distributions of parameters. Comment onthis argument and justify your opinions with reasons, facts, and examples. Topic 4 DQ 1Description:Many times, linear optimization is used to maximize an objective function because profit, productivity,or efficiency is the outcome of interest. Provide two examples where the goal is to optimize a processby minimizing an objective function. In your examples, identify the outcome and any constraints thatwould need to be met. Topic 4 DQ 2Description:When many constraints are present in a linear optimization problem, there is a greater chance that aredundant constraint exists. Assume you are trying to maximize an objective function and you havetwo decision variables, X1 and X2. If a redundant constraint exists, does the constraint becomenecessary if you try to minimize (instead of maximize) the same objective function? Why? Do youneed an objective function to determine if a constraint is redundant? Explain. Topic 5 DQ 1Description:Many linear optimization problems can be solved by finding a graphical solution, but there are someproblems that require more advanced spreadsheets and software to find an optimal solution.Describe an optimization problem in which finding a solution would be impossible using thefeasible-region approach. Discuss the attributes the problem would have to make it impossible tosolve using the feasible-region approach. Topic 5 DQ 2Description:Optimization techniques are used in many applications. For example, when customers order productsfrom an online store, the shipper has to determine the optimal way to get the product delivered to thecustomer. The delivery path that is chosen is the path that minimizes shipping costs whilesimultaneously satisfying these constraints:The product must arrive by a promised date.The shipper must deliver a finite set of items.The product must originate from one of several warehouse hubs across the country.Discuss whether there can be multiple solutions (i.e., more than one path to get the product to yourhouse). Explain why. Is there a guarantee that a solution always exists? Explain. Topic 6 DQ 1Description:Most transshipment network modeling problems assume the costs are constant. For example, thecosts of shipping a product from one city to another are assumed fixed. This can change over time iffuel costs change. If you knew the distribution of fuel costs, how could the distribution of fuel costs beincorporated into the transshipment problem? Discuss the benefits of employing this approach. Topic 6 DQ 2Description:Minimum spanning trees were initially design to solve electrical grid problems but now have manymore applications such as computer networks, transportation networks, and supply networks.Describe a business problem where minimum spanning trees can be used to find a solution. Topic 7 DQ 1Description:Can linear and nonlinear optimization problems use the same approach to find a solution? Forexample, if the GRG algorithm is used to solve a nonlinear optimization problem, will it work to solvea linear optimization problem? Discuss whether or not the GRG algorithm will always find a cornerpoint similar to the feasible-region approach. Topic 7 DQ 2Description:Nonlinear optimization problems can have multiple solutions, and a solution can be local or global.Can there be multiple local solutions? Explain your answer. Can there be multiple global solutions?Explain our answer. Topic 8 DQ 1Description:Betamax (or Beta) was a video recording format developed by Sony in the 1970s. Sony conductedresearch and found that consumers wanted a high-quality picture when using a Beta cassette withtheir home recording equipment. Sony developed the technology with video quality in mind and, as aconsequence, limited the recording time to only 60 minutes.At the same time, JVC developed the Video Home System (VHS) but without much consumerresearch. JVC was more interested in developing a unified standard for broadcast operations.Consequently, they focused more on extending the recording time for their VHS cassettes at theexpense of picture quality.In the end, the VHS format won by eventually squeezing the Beta format out of the market. Discussthe approaches these two companies took in the good decisions/good outcomes context. Topic 8 DQ 2Description:Suppose you received two job offers when looking for a job, one from Company A and one fromCompany B. To make a decision, what type of methodology would you use: probabilistic ornonprobabilistic? Whichever you choose, describe the items to consider and the decision rules youwould use before deciding which job offer to accept.

Be sure to show all steps for arriving at your solutions. Use Equation Editor (i.e., Equation Tools/Equation Ribbon) as needed, and submit everything in one file.For additional details, please refer to the Final Exam Problem Set and the Problem Set Rubric document.If you have any questions please let me know! 😊