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Pete's Plastics manufactures plastic at plants in Miami, St. Louis and Cleveland. Pete needs to ship plastic to customers in Pittsburgh, Atlanta and Chicago. He wants to meet the customer's demand such that the cost of shipping the plastic from his plants to his customers is minimized. The data for the problem is summarized in the following excel sheet. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 A B Plant Miami St. Louis Cleveland Amount Received Demand (tons) Plant с Pittsburgh 40 D Total Transportation Cost } Markets Atlanta 60 E Chicago 20 Unit Shipping Costs (S/ton) Pittsburgh Atlanta B A Miami $ 1,200.00 $ 700.00 $ 1,300.00 St. Louis $ 700.00 $ 550.00 $ 300.00 Cleveland $ 125.00 $ 675.00 S 350.00 Chicago C F Amount Supply Shipped (tons) 30 40 50 The following list and dictionary were defined to solve the problem using pulp optimization library: Plants = ['Miami', 'StLouis', 'Cleveland' ] Plants Supply = { 'Miami': 30, 'StLouis':40, 'Cleveland':50 } G Customers= ['Pittsburgh', 'Atlanta', 'Chicago'] Customers_Demand= { 'Pittsburgh': 40, 'Atlanta':60, 'Chicago':20} Distances = { 'Miami':{'Pittsburgh': 12000, 'Atlanta':700, 'Chicago':1300), 'StLouis':{'Pittsburgh': 700, 'Atlanta':550, 'Chicago':300), 'Cleveland':{'Pittsburgh': 125, 'Atlanta':675, 'Chicago':350) Which of the following represent the correct code for the objective function? Note that "Amount" is the dictionary name for the decision variables. Done Pete Plastics_prob += 1pSum( Amount *Distances[j] for i in Plants for j in Customers) O Pete Plastics_prob += 1pSum( Amount *Distances[j] for i in Customers for j in Plants) O Pete_Plastics_prob += 1pSum( Amount[j]*Distances[j] for i in Customers for j in Plants) O Pete Plastics_prob += 1pSum( Amount[j]*Distances[j] for i in Plants for j in Customers)
Pete's Plastics manufactures plastic at plants in Miami, St. Louis and Cleveland. Pete needs to ship plastic to customers in Pittsburgh, Atlanta and Chicago. He wants to meet the customer's demand such that the cost of shipping the plastic from his plants to his customers is minimized. The data for the problem is summarized in the following excel sheet. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 A B Plant Miami St. Louis Cleveland Amount Received Demand (tons) Plant с 40 D Pittsburgh Atlanta Markets Total Transportation Cost } 60 E Chicago Chicago Pittsburgh Atlanta B A с Miami $ 1,200.00 $ 700.00 $ 1,300.00 St. Louis $ 700.00 $ 550.00 $ 300.00 Cleveland $ 125.00 $ 675.00 S 350.00 20 Unit Shipping Costs (S/ton) F Amount Supply Shipped (tons) 30 40 50 The following list and dictionary were defined to solve the problem using pulp optimization library: Plants = ['Miami', 'StLouis', 'Cleveland' ] Plants Supply = { 'Miami': 30, 'StLouis':40, 'Cleveland':50 } G Customers= ['Pittsburgh', 'Atlanta', 'Chicago'] Customers_Demand= { 'Pittsburgh': 40, 'Atlanta':60, 'Chicago':20} Distances = { 'Miami':{'Pittsburgh': 12000, 'Atlanta':700, 'Chicago':1300), 'StLouis':{'Pittsburgh': 700, 'Atlanta':550, 'Chicago':300}, 'Cleveland':{'Pittsburgh': 125, 'Atlanta':675, 'Chicago':350} Which of the following represents the total amount of products received by Pittsburgh? Ⓒ1pSum (Amount ['Pittsburgh'] for i in Plants) Ⓒ1pSum (Amount['Pittsburgh'] for i in Customers) O 1pSum (Amount ['Pittsburgh'][i] for i in Customers) O 1pSum (Amount[i]['Pittsburgh'] for i in Plants)