Center for Engineering Logistics and Distribution (CELDi) An NSF sponsored Industry/University Cooperative Research Center Logistics of Using Underground Pipelines for Freight Transportation Freight Pipeline Company James S. Noble, Ph.D., P.E. & Mustafa Sir, Ph. D. Gaohao Luo, Anna McLaughlin, Nichole Smith AGENDA – October 28, 2009 Problem Statement / Approach Current Work Operations Optimization Load / Unload Analysis Simulation 1

Logistics of Using Underground Pipelines for Freight Transportation Research Team: James Noble (PI), Mustafa Sir, Gaohao Luo Anna McLaughlin, Nichole Smith Sponsor: Freight Pipeline Company Problem in context: Many large metro areas around the world are highly congested hindering the flow of freight in and out. Underground freight pipelines or tubes can reduce congestion, reduce environmental impact of freight movement and reduce overall transportation cost. Projects are currently in the evaluation stage in New York, Sydney, Shanghai and others. Important/Expected Results Tube network design – I/O location, flow path Capsule dispatching / control algorithms Cargo tracking approaches Design of load / unloading processes Capacity analysis Technical Approach Assess related logistics issues Develop object oriented simulation model for analyzing dispatching / control approaches Formulate design / operation models Development of solution algorithms Model sensitivity analysis Implementation scenario analysis What can other members use? Network design algorithms Loading/unloading algorithms Cargo tracking strategies Dispatching / control algorithms % Complete 0% 100% 2

Problem Statement Logistics issues associated with freight tube system –Tube network design – I/O location, flow path –Dispatch/control of capsules according to freight shipment needs (capacity and schedule) Tracking of cargo in transit in the pipe and in storage room –Design of cargo loading and unloading process at freight pipeline terminals –Capacity analysis 3

Literature review of related problem areas (i.e. pneumatic pipeline, AGV systems, rail systems) Determination of modeling issues –Technology constraints –# vehicles / train length –Route / network design –Buffer size / load sizes –… Development of simulation model (Simio) Development of optimization models for select design issues Model analysis Project Approach 4

5 Vehicle Technology Vehicle Rqmts - size and # Operation - dispatching - routing Network Design - flow path - # & location P/D Information - ID (RFID) Problem Domain

6 Operation Optimization

7 Minimize Subject to: Operation Optimization Total squared tardiness Sequential operations Capacity

8 Operation Optimization: Case Example Due date (d ij ) Processing Time (p ij ) Number of Capsules Required (c ij ) O O O We assume that there is ONE capsule in the system. The parameters of the example are shown below:

9 Operation Optimization: Case Example S ij (starting time of O ij ) Processing Time (p ij ) Due date (d ij )Tardiness O O O All three operations can be completed using one of the following 3 schedules: 1.O 12  O 23  O 31,then the sum of square of total tardiness = = 14 2.O 23  O 31  O 12, then the sum of square of total tardiness = = 37 3.O 31  O 12  O 23, then the sum of square of total tardiness = = 41 Lingo Results

Load/Unload Concepts 10

Load/Unload Concepts 11

Load/Unload Concepts 12

DemandUnload Rate (mins)Lift Rate (ft/min)Buffer SizeTotal Time# Moved in 24 Hours 10 (expo 6) (expo 6) (expo 6) (expo 6) (expo 6) Unload Demand: 10 containers / hour, 100 / day 13

Unload Demand: 40 containers / hour, 500 /day Demand Unload Rate (Mins) Lift Rate (ft/min) Buffer Size Total Time # Moved in 24 Hours 40 (expo 1.5) (expo 1.5) (expo 1.5) (expo 1.5) (expo 1.5)

Unload Demand: 80 containers / hour, 1000 / day DemandUnload Rate (mins)Lift Rate (ft/min)Buffer SizeTotal Time# Moved in 24 Hours 80 (expo 0.75) (expo 0.75) (expo 0.75) (expo 0.75) (expo 0.75)

System Simulation – Small Loop 16