Board 102 : Truck-Drone Two-tier Delivery Network Design

Ergin Erdem; Christopher Johnson; Sangho Shim; Jordan Williams

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: Industrial Engineering Division Poster Session
Tagged Division: Industrial Engineering
Page Count: 11
DOI: 10.18260/1-2--29859
Permanent URL: https://peer.asee.org/29859
Download Count: 473

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Paper Authors

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Ergin Erdem Robert Morris University

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Ergin Erdem is an assistant professor of Department of Engineering at Robert Morris University. Dr. Erdem holds BS and MS degrees in industrial engineering from Middle East Technical University, Turkey and a PhD in Industrial and Manufacturing Engineering from North Dakota State University He has previously worked as a lecturer and research associate at Atilim University and North Dakota State University. His research interests include; modeling for facility planning, genetic algorithms, education of manufacturing technologies, RFID applications in food and pharmaceutical applications, operations management in healthcare industry.

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Christopher Johnson Robert Morris University

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I highly enjoyed working with Dr. Sangho Shim at Robert Morris University where I graduated summa cum laude with a B.S. in Engineering concentrating in Industrial Engineering. Dr. Shim encouraged me to be a better student and professional during our work together on research and in the classroom. After success at RMU, I accepted a full time position as an Industrial Engineer with FedEx Ground where I apply many of the principles learned through my academic career.

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Sangho Shim Robert Morris University

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Dr. Sangho Shim is an Assistant Professor in the Department of Engineering at Robert Morris University (RMU) in Pennsylvania.

Before he joined RMU in Fall 2015, he had performed research projects on combinatorial optimization as a research staff member of Kellogg School of Management at Northwestern University. He also performed the General Motors Renewable Energy Portfolio project with Industrial Engineering and Management Sciences Department of Northwestern University since 2011. At Northwestern University and Georgia Institute of Technology, he also conducted the KT (Korea Telecom Corp) Smart Grid Project and the KT-LG Illinois Smart Building Project as Manager of KT in 2010-2011.

Dr. Shim was an Instructor at Georgia Institute of Technology in 2009-2010 since my PhD degree in Industrial and Systems Engineering from the institute.

Before he came to the US, he got BS in Mathematics at Seoul National University and MS in Computational Mathematics at POSTECH in Korea.

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Jordan Williams

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Abstract

We develop case study materials for the future of supply chain logistics; Truck-Drone two tier delivery network design.

FedEx Headquarter (HQ) in a city was used as the networks root node or central distribution center (DC). FedEx Office locations were chosen as potential drone distribution facilities. Three locations were used but the model was developed to make drone facility additions as needed. We chose major zip-code areas supported by the HQ to use as drone drop off locations with zip-code populations serving as product demand. Ten zip-codes were chosen, but this number can also be fluctuated as needed in the truck-drone model. In a depiction of the truck-drone model with FedEx HQ as the root node, linehaul to drone facility is the first arc and drone facility to destination as the second arc.

Two methods were used to capture distance along network arcs. Linehaul distance from HQ to drone facility was found using Google Map technology. This technology provided best highway route, mileage, and time determination for truck travel, as well as longitude and latitude information. After longitude and latitude coordinates were determined, Haversine’s Formula, which calculates distance between points on spheres, was used to calculate distance between drone facilities and zip-code destinations. An independent Worksheet of assumptions is made for future use in more realistic assumptions. The main Worksheet invoke the input data from the Worksheet of assumptions.

After all data concerning the network model was established and fixed and variable costs were estimated, the next step was to model the truck-drone delivery network in Excel and utilize the software’s solver capabilities for optimal route generation. Excel provided sufficient functionality needed to develop the truck-drone model; the platform can handle the Linear Programming formulation used over the truck-drone model and using Excel allows the formulation to be understood at novice and expert levels. Column-row functionality provided by Excel also allows the truck-drone model to have dynamic features. Drone facilities and zip-code or destinations can be added or subtracted as desired.

The initial Excel Worksheet is designed by an undergraduate senior student taking an undergraduate research course in Spring 2017. Using Excel Solver an add-in to Micro Soft Excel, the student solved the problem to select drone distribution facilities among 3 possible locations. His Worksheet is being refined by other students taking a Supply Chain Engineering course as course project in Fall 2017. To increase the scale of the problem including a realistic scale of 10 or more potential locations in a city, they use Open Solver another add-in to Micro Soft Excel which is endorsed by Computational Infrastructure for Operations Research (COIN-OR.)

Citation
Format

Erdem, E., & Johnson, C., & Shim, S., & Williams, J. (2018, June), Board 102 : Truck-Drone Two-tier Delivery Network Design Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--29859

TY - CPAPER
AB - We develop case study materials for the future of supply chain logistics; Truck-Drone two tier delivery network design.

FedEx Headquarter (HQ) in a city was used as the networks root node or central distribution center (DC). FedEx Office locations were chosen as potential drone distribution facilities. Three locations were used but the model was developed to make drone facility additions as needed. We chose major zip-code areas supported by the HQ to use as drone drop off locations with zip-code populations serving as product demand. Ten zip-codes were chosen, but this number can also be fluctuated as needed in the truck-drone model. In a depiction of the truck-drone model with FedEx HQ as the root node, linehaul to drone facility is the first arc and drone facility to destination as the second arc.

Two methods were used to capture distance along network arcs. Linehaul distance from HQ to drone facility was found using Google Map technology. This technology provided best highway route, mileage, and time determination for truck travel, as well as longitude and latitude information. After longitude and latitude coordinates were determined, Haversine’s Formula, which calculates distance between points on spheres, was used to calculate distance between drone facilities and zip-code destinations. An independent Worksheet of assumptions is made for future use in more realistic assumptions. The main Worksheet invoke the input data from the Worksheet of assumptions.

After all data concerning the network model was established and fixed and variable costs were estimated, the next step was to model the truck-drone delivery network in Excel and utilize the software’s solver capabilities for optimal route generation. Excel provided sufficient functionality needed to develop the truck-drone model; the platform can handle the Linear Programming formulation used over the truck-drone model and using Excel allows the formulation to be understood at novice and expert levels. Column-row functionality provided by Excel also allows the truck-drone model to have dynamic features. Drone facilities and zip-code or destinations can be added or subtracted as desired.

The initial Excel Worksheet is designed by an undergraduate senior student taking an undergraduate research course in Spring 2017. Using Excel Solver an add-in to Micro Soft Excel, the student solved the problem to select drone distribution facilities among 3 possible locations. His Worksheet is being refined by other students taking a Supply Chain Engineering course as course project in Fall 2017. To increase the scale of the problem including a realistic scale of 10 or more potential locations in a city, they use Open Solver another add-in to Micro Soft Excel which is endorsed by Computational Infrastructure for Operations Research (COIN-OR.)

AU - Ergin Erdem
AU - Christopher Johnson
AU - Sangho Shim
AU - Jordan Williams
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - Board 102 : Truck-Drone Two-tier Delivery Network Design
UR - https://peer.asee.org/29859
DO - 10.18260/1-2--29859
ER -