代写network、代做chocolate pipeline、代写R语言、R设计代写代做Python程序|代写 Statistics

ChocolateWilly Wonka has a chocolate pipeline distribution network as shown below. Each node corresponds to asingle storage tank, and the numbers on the edges represent flow capacities (per hour) of some unit offluid chocolate. Note that flows are allowed in both directions between some, but not all, of the tanks.Willy would like to determine the maximum flow that can be sent from tank 1 to tank 8 per hour.1. Formulate this problem in an Excel spreadsheet using Solver. Your deliverable should be calledp1.xlsx.2. As a sensitivity analysis, the company is also considering increasing the capacity of all arcs leading outof tank 1, and all arcs leading into tank 8, and it wants to know whether this will allow it to double themaximum flow per hour from tank 1 to tank 8. (Assume we multiply the arc capacities in question by aconstant k, simultaneously.) Solve the original problem using an appropriate algorithm from the optreesor similar package in R, and ensure you achieve the same result as your Solver model. Then, perform thesensitivity analysis as requested. Is there a limit to increasing the expansion factor k? Your deliverableshould be a simple R script called p2.R.3. Extra Credit: Set up the simplex tableau (complete matrix in augmented form) as a simple matrix in R.Use the rref() function from the pracma package in R to solve. Deliverable is p3_extra.R.Walrus WorldA large retailer, Walrus World (WW), has dozens of regional distribution centers and thousands of retailstores and it wants to use optimization to minimize its annual cost of transportation. WW has a policy ofserving each of its stores from one and only one distribution center (DC), and it must also pay attentionnot to exceed the trailer capacity of its DC’s, which are measured in the number of outgoing trailers peryear that can be loaded from that facility. WWmanagers must also ensure that each retail store receivesthe number of trailers per week that are required to keep shelves stocked. Before the analysis canbegin, some data wrangling must happen.Part IProvided for you are two tables in a database, ww_dcs, and ww_stores. When you examine these files,you will see that they are addresses of 46 dry goods distribution centers and 4535 stores, scraped fromthe web. Using Yahoo’s Geocoding API (PlaceFinder), the addresses have been reconciled to latitude andlongitude coordinates (in degrees).4. Now that the web scraping and geocoding is complete, your first task is to pick up where the previousdata scientist left off. You must write an R script to populate the table ww_mileage, consisting of all ofthe (dc,store) pairs. However, given the distances involved, the simple Euclidean distance will not do.Rather, you will employ the Haversine1 distance formula, which takes into account the curvature of theEarth2. Though the function is simple to write, you can use the function haversine() from the R pracmapackage. To receive credit, you must use R to read from the ww_dcs and ww_stores tables and write tothe ww_mileage table in the database. Think of it as flexing your R muscles. (The astute analyst—you—should notice that we can solve this problem in a single SQL stat代写network作业、代做chocolate pipeline作业、代写R语言作业、R课程设计作业代写 代做Pythoement, with no R. But then you wouldnot be flexing your R muscles, would you?) Leave your ww_mileage table populated. This deliverable isan R script p4.R.1See http://rosettacode.org/wiki/Haversine_formula and Wikipedia: Haversine formula.2After this rough optimization model is complete, a more accurate distance function can be used takinginto account All-Pairs Shortest Paths using actual roads.Part IITo simplify things, you will solve this supply chain optimization for a subset of the DC’s (10) and stores(1100) in the original problem. Data regarding the selected DC’s and their capacities are contained in thedc table. Similarly, the store table specifies how many trailers per year are required by each store. Thedatabase also has a mileage table for the distance between each pairing of DC and store. The cost ofeach trip is a fixed $200, plus $0.75 per mile.5. Formulate the problem using Solver, using just a few samples of rows (say, 4 each) from the dc andstore tables. This is part of the prototyping process for your model. The deliverable is p5.xlsx.6. Write a Python program that optimizes the same prototype model using Gurobi, and validate yoursolution by ensuring your results match your Solver model. Thus, you will need to use the same sampleinputs as in your Solver model. This deliverable is p6.py.7. Now for the real model. Write a Python program that optimizes this problem. To receive credit, youmust dynamically retrieve the data from the database tables mentioned above to drive your model(store, dc, and mileage). After solving the model using Gurobi, write the results back to the result table.The deliverable is p7.py.8. Use mysqldump or other export feature (e.g. within MySQL Bench) to create a dump of yourdatabase. If your username is jsmith, your dump file should be jsmith.sql. You can optionally compressthe file as jsmith_sql.zip. Please substitute your WM user name as appropriate.9. How would you revise your Python-MySQL-Gurobi program to incorporate a different transportationcost structure? Each trip still has a $200 fixed cost and $0.75 per mile traveled. In addition, if a trip isover 150 miles long then an additional expense of $250 is incurred for driver lodging and mealallowance—this is because the driver will need to stay overnight in a hotel between the outbound and inbound legs of their trip. Describe an objective function with this conditional cost, and any otherchanges to your model. Modify your Solver prototype to incorporate this new objective and deliver asp9.xlsx.10. Submit a summary of your results—one for each problem—as lastname.PDF. If the question involvedan optimization, here is where you include your optimal objective function value, and any commentaryor business interpretation. If your solution for that problem is non-functional for any reason, pleaseprovide your perspective here. Also, if you made any modeling assumptions, included any tricky code, orjust wish to say “hello, it’s me”, please include those comments here. All of your results should beposted to Blackboard in a .ZIP archive, by end of day, Dec 14, 2018. An extension of at most 1 day will begranted by permission.转自：http://ass.3daixie.com/2018121327928244.html