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

2 2 Overview

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 Problem Formulation

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 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 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 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 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 Optimization Tool

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

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 Destination Carbon Intensity

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

15 Origin Transportation Costs

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

18 Current Model w/o Truck Premium Plants P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Min-Max Rail Cars

19 Transportation Cost - Premium Plants P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 New Min-Max Optimized Rail Cars

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 New Min- Max Spot Price - Cost 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 Cost per Plant 0.1*Q *Q1 0.11*Q *Q2 0.09*Q *Q3 0.1*Q *Q4 0.15*Q *Q5 0.2*Q *Q6 0.25*Q *Q7 0.14*Q *Q8 0.17*Q *Q9 0.19*Q *Q10 Total Cost 0.1*Q *Q *q1 + q1* *Q *Q *q2 + q2* *Q *Q *q3 + q3* *Q *Q *q4 + q4* *Q *Q *q5 + q5* *Q *Q *q6 + q6* *Q *Q *q7 + q7* *Q *Q *q8 + q8* *Q *Q *q9 + q9* *Q *Q *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 Thank You!

23 Questions