Green Supply Chains for Remote Locations

1 Green Supply Chains for Remote Locations YueGeng Industrial Engineering and Management Sciences Northwestern University Dissertation Proposal Part II

Green Supply Chains for Remote Locations YueGeng Industrial Engineering and Management Sciences Northwestern University Dissertation Proposal Part II

Agenda • Overview • Model • Emission Estimation • Results • Future Research Dissertation Proposal Part II

Overview Scope Objectives Dissertation Proposal Part II

The Greenland Physics Problem • THE KEY RESOURCE QUESTIONS • How much does it cost? • How much will the cost escalate? • How can we use less of it? • PRIMARY NODES • Kangerlussuaq • Summit Station • Thule Air Base • Remote Sites… Field • ON-ISLAND MODES • Military airlift (LC-130) • Commercial air (Twin Otter, Helos, ?) • Oversnow Traverse Field Dissertation Proposal Part II Dissertation Proposal Part II

Scope • Assess long term economical viability of Summit • Supporting transportation logistics needs • Operational considerations at Summit • Energy requirements • Environmental impact • Emissions from transportation • Renewable on-site generation Dissertation Proposal Part II

Travel on Site Dissertation Proposal Part II

Objectives • A tool to analyze scenarios • Cost • Emissions • Other key performance indicators • Given a possible scenario • Determine logistics requirements and expenses by means of optimization • Trade-off between cost and emissions Dissertation Proposal Part II

Model Network flow model Dissertation Proposal Part II

Network flow model • Standard time-spaced multi-commodity network flow model • Keeping track of fuel consumption at Summit • Base fuel consumption plus fuel consumed by human activities Dissertation Proposal Part II

Time (Days/Weeks) LC-130 from Scotia on day 8 arrive Kanger on day 9 Inventory left from previous day Vessel from VA Beach on day 2 arriving to Kanger on day 32 Time window for shipment A at origin Shipment A Location in the US 1 2 3 4 5 6 7 8 31 32 … … 1 2 3 4 5 6 7 8 31 32 … … Kanger 1 2 3 4 5 6 7 8 31 32 … … Summit … Time window for shipment A at destination Locations Dissertation Proposal Part II Aggregate by week Shipment A

Constraints • Flow balance for each commodity at each node • Consumable commodity • Non-consumable commodity • Capacity is not exceeded for each arc and each transportation mode • Inventory capacity has both lower and upper bound at specific nodes • Certain matchup of commodity and transportation mode is forbidden Dissertation Proposal Part II

Emission Estimation Background on transportation emissions Estimate emissions Dissertation Proposal Part II

Dissertation Proposal Part II Dissertation Proposal Part II

Estimate Emissions • Using fuel-based method • Emission factor * Gallons of fuel consumed • Estimate fuel consumption • Fuel consumed in transportation • On-site fuel consumption, including base consumption and consumption due to human activities Dissertation Proposal Part II

Solution Methods A. Bi-level Goal Programming • Phase 1 • Minimize cost • Constraints are imposed on the constructed network • Phase 2 • Minimize emissions • Constraints are imposed on the constructed network • Cost ≤ (1+f)∙ optimal cost from Phase 1 B. Weighting Method for Multi-objective Optimization Dissertation Proposal Part II

Results – Tradeoff between Cost and Emissions Emissions/cost Efficient Frontier Dissertation Proposal Part II

Results – Installation of Renewable Energy Sources Dissertation Proposal Part II Dissertation Proposal Part II

Summary of Contributions • The study provides insights about the trade-off between the transportation cost and emissions. • Results obtained will guide the logistics operations. • The algorithms under development will be useful for network flow problem with variable arc upper bound. Dissertation Proposal Part II

Future Research • Approximate Dynamic Programming (ADP) Approach • Year as a period • Inventory of fuel and number of people as state • Decomposition algorithms still needed within a year • Test for different levels of commodity aggregation • Easy to handle stochastic demands • Lagrangian Relaxation Method • Relax the vehicle capacity constraints (i.e., variable arc upper bound) • The problem is decomposed into an unconstrained problem and a multi-commodity flow problem with dependent demand (i.e. fuel) • Compare the results with a commercial solver and rolling horizon approach Dissertation Proposal Part II

On-site Renewable Energy Generationwith Storage YueGeng Industrial Engineering and Management Sciences Northwestern University Dissertation Proposal Part III

Agenda • Problem Description and Model • Summary • Future Research Dissertation Proposal Part III

Notation • Decision Variables • : power generated from energy source i in period t (in kw) • : power purchased from spot market in period t (in kw) • : Power sold to grid from source i, in period t (in kw) • : binary variable specifying whether renewable energy source i is selected/installed • : power generated from source i and consumed in period t (in kW) • Parameters • : demand in period t (in kW) • : capacity for generation from energy source i (in kW) Dissertation Proposal Part III

Model • Objective: to maximize profit or minimize cost over time • Installation cost • Cost or profit for energy purchased or sold • Battery bank cost • Other operations cost • Major Constraints: • Power generation • Demand balance • Battery related constraints • Three features are captured • Contract related constraints Dissertation Proposal Part III

Dynamic Programming Formulation • Optimality equation: , • State: • State of charge for each battery bank, • Remaining throughput for each battery bank, • Number of periods required to install a new battery bank, • Action: energy to generate, to sell, to buy, to store into each battery bank and to draw from each battery bank, on an hourly basis, • System dynamics: the value of state variables change over time, • Action constraints. Dissertation Proposal Part III

Summary of Contributions • Study on-site renewable energy installation problem using real data. • Explicitly capture the key properties of battery storage, • Abattery must be replaced after its lifetime throughput is exhausted, • Power for charging or discharging the battery bank cannot exceed a given bound, • Asmall portion of energy will be lost during the charging and discharging. • The ADP algorithm under development will be useful for other multi-period problems, • Handle the one-time installation cost in dynamic programming. Dissertation Proposal Part III

Future Research • To test different approximation strategies of ADP algorithms: • Stochastic approximation methods, • Value function approximations. • To handle the one-time installation cost: • variables is hard to formulate within DP, • Add a penalty if , • if for given , we can find quickly enough. Dissertation Proposal Part III

Green Supply Chains for Remote Locations