1. 1 Transportation Network Optimization Project GPRE Inc. Group Members: Aditya Nambiar, Anuj Gandhi, Ashwin Mishra, Daksh Sabharwal, Graham Thomas, Sandeep Prakash

2. 2 2 Overview

3. 3 Goal: Develop a tool in Gurobi to optimize transportation network for minimizing weekly freight costs Problem Formulation Operational Implementation Financial Benefits Adding Value Truck Premium Discussion

4. 4 4 Problem Formulation

5. 5 Rates[plant][destination][rail_road] = Rates for transport from plant to destination through a particular rail road/route Min Cars [plant][destination][rail_road] = Min Cars in a plant Max Cars [plant][destination][rail_road] = Max Cars in a plant Demand [load_no][destination][rail_road] = Demand at a destination Carb_Int [plant] = Carbon Intensity for a plant Carb_Int [destination] = Carbon Intensity for a destination FOB = Flag denoting Shipment is FOB or not Parameters: Problem Formulation

6. 6 Problem Formulation - Model Variable: Car_Quant [load_no][plant][destination][rail_road] = No. of cars from a plant to destination through a particular rail road for a load no. Objective Function: Minimize Sum (over load_no, plant, destination, rail_road) { Rates[plant][destination][rail_road]* Car_Quant [load_no][plant][destination][rail_road] }

7. 7 Constraints: For meeting the customer demand for all individual destinations… Sum (over all plants, rail_road) { Car_Quant [load_no][plant][destination][rail_road] } = Demand [load_no][destination][rail_road] Minimum cars out of plants requirement… Sum (over load_no, plant, rail_road) { Car_Quant [load_no][plant][destination][rail_road] } > = Min Cars [plant][destination][rail_road] Maximum cars out of plant requirement… Sum (over load_no, plant, rail_road) { Car_Quant [load_no][plant][destination][rail_road] } <= Max Cars [plant][destination][rail_road] 7 Problem Formulation - Constraints

8. 8 FOB constraint: Here sum of quantity going from plants of a particular FOB region should be equal to demand of the load_no for the customer… if (FOB){ Sum (over plants, rail_road) { Car_Quant [load_no][plant][destination][rail_road] } = Demand[load_no][destination][rail_road] } Carbon intensity constraint: Carbon intensity of the plant sending the shipment should be less than or equal carbon intensity requirement of the destination Sum (over plant, rail_road) {Carb_Int [destination] * Car_Quant [load_no][plant][destination][rail_road] >= {Car_Quant [load_no][plant][destination][rail_road] * Carb_Int [plant] } Problem Formulation - Constraints

9. 9 Mock Nominations Constraint: Here sum of quantity going to destinations of a particular destination region should be equal to demand of the load_no for the customer… If (Region) { Sum (over plants, rail_road, destination) { Car_Quant[load_no][plant][destination][rail_road] } = Demand [load_no][destination-region][rail_road]} Non-Negativity and Integer constraints Car_Quant [load_no][plant][destination][rail_road] are positive integers Problem Formulation - Constraints

10. 10 Optimization Tool

11. 11 Inputs – Shipments & Origin Threshold Output – Optimized Shipments Optimization Tool - Input / Output

12. 12 Operational Implementation To be used weekly once to optimize delivery of shipments Integrated with ShipXpress where user enters shipment data and min-max for plants Users will use the tool via ShipXpress to determine the optimum amount to be sold in Spot Market opportunity

13. 13 Destination Carbon Intensity

14. 14 Cost Comparison: Financial Benefits Net Weekly Savings: $40,000

15. 15 Origin Transportation Costs

16. 16 Scalability –Feature to incorporate Carbon Intensity for All locations –Number of Plants/Destinations can be increased –Provision to increase number of carriers to four Mock Nominations –Gives optimal destination to ship in a region Value Additions

17. 17 Suggestions Location parameters should be consistent across all tables to get best results Incorporating Spot Market / Truck Premium opportunity in the tool

18. 18 Current Model w/o Truck Premium Plants P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Min-Max 100 30 40 55 30 60 45 75-85 95-115 105-115 Rail Cars 100 30 40 55 30 60 45 75 96 105

19. 19 Transportation Cost - Premium Plants P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 New Min-Max 94-100 30-32 40-42 55-57 30 60 45 75-85 95-115 105-115 Optimized Rail Cars 94 30 40 55 30 60 45 77 98 107

20. 20 Spot Price:0.8 Truck Rate in terms of Rail Car:1000 Truck Premium Demand:6 Plants P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Cost 0.1 0.11 0.09 0.1 0.15 0.2 0.25 0.14 0.17 0.19 New Min- Max 94-100 30-32 40-42 55-57 30 60 45 75-85 95-115 105-115 Spot Price - Cost 0.8 0.69 0.71 0.7 0.65 0.6 0.55 0.66 0.63 0.61 Premium Quantity q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 Premium Cost 0.7*q1 0.69*q2 0.71*q3 0.7*q4 0.65*q5 0.6*q6 0.55*q7 0.66*q8 0.63*q9 0.61*q10 Comprehensive Model including Costs

21. 21 Cost per Plant 0.1*Q1 + 6300*Q1 0.11*Q2 + 6632*Q2 0.09*Q3 + 5000*Q3 0.1*Q4 + 5200*Q4 0.15*Q5 + 6500*Q5 0.2*Q6 + 6800*Q6 0.25*Q7 + 5500*Q7 0.14*Q8 + 4900*Q8 0.17*Q9 + 5000*Q9 0.19*Q10 + 5100*Q10 Total Cost 0.1*Q1 + 6300*Q1 - 0.7*q1 + q1*1000 0.11*Q2 + 6632*Q2 - 0.69*q2 + q2*1000 0.09*Q3 + 5000*Q3 - 0.71*q3 + q3*1000 0.1*Q4 + 5200*Q4 - 0.7*q4 + q4*1000 0.15*Q5 + 6500*Q5 - 0.65*q5 + q5*1000 0.2*Q6 + 6800*Q6 - 0.6*q6 + q6*1000 0.25*Q7 + 5500*Q7 - 0.55*q7 + q7*1000 0.14*Q8 + 4900*Q8 - 0.66*q8 + q8*1000 0.17*Q9 + 5000*Q9 - 0.63*q9 + q9*1000 0.19*Q10 + 5100*Q10 - 0.61*q10 + q10*1000 Comprehensive Model including Costs Contd. Constraint: 1. qi <= spot-market demand near each plant 2. All qi’s are Non-negative

22. 22 Thank You!

23. 23 Questions