Freight Research at The Ohio State University: Selected Projects

Transcription

1 Freight Research at The Ohio State University: Selected Projects Mark R. McCord The Ohio State University Columbus, OH 2010 Ohio Conference on Freight Toledo, Ohio September 14, 2010

2 Cutting Logistics Costs with Ocean Carrier Selection

3 Ocean Carrier Selection Sponsor: Fortune 500 company (NDA requests confidentiality) OSU Senior Investigators: John Saldanha, Marketing & Logistics, Fisher College of Business Other Collaborators Pennsylvania State University, University Park, PA Industry partner (provided data)

4 Std Dev (days) Setting: Waterborne imports from Asia and Europe to the Mid-West Fast, Unreliable Slow, Unreliable COS (12) ALL (8) CMA (10) KHL (11) CSC (5) HYU (1) HAN (4) SIN (7) OCL (2) KLN (3) SEN (9) MSK (6) Fast, Reliable Slow, Reliable Transit Time (days) Objective: In the D2D move in an international supply chain, investigate if (contrary to extant beliefs) variations in transit-time and transit-time reliability in the ocean carriage segment resulted in differences in products landed costs even with significant variance in the land-side segments Principal Beneficiaries Carriers of ocean freight U.S. importers and exporters

5 Values x 10^-2 Values x 10^-2 Values x 10^-2 Values x 10^-2 Values x 10^-2 Methodology Triang(72.020, , ) Triang(71.890, , ) Normal( , ) Weibull(1.8498, ) Shift= Normal( , ) % 90.0% 5.0% Export Country 5.0% 90.0% 5.0% < 5.0% 90.0% 5.0% > Inter-Continental Transport 91.0% Shipper Interviews Chu and Huang (2005) Database (2005) (2005) Monte-Carlo simulation to build distributions for D2D transit-times All land-side segment distributions held constant varying ocean carrier segment distributions to simulate D2D transit-times for each ocean carrier The D2D distributions for each carrier used as inputs to the inventory theoretic model to calculate the landed cost of different types of products The inventory theoretic model considers the transportation, inventory, order cost trade-off to select optimal transportation and inventory parameters at fixed level of customer service (e.g. 99% fill-rate to customer) 5.0% > Import Country (U.S.) < 5.0% 90.0% 5.0% > Plant to Port Port of departure Ocean Carrier U.S. Port of Entry Direct Truck Lloyds MIU Seasearcher Leachman & Associates Leachman & Associates 5

6 Example Results: S. Korea to Chicago via Pusan and Long Beach 1. Even in the presence of large transit-time variance in land-side segments, transit-time variance for the ocean carrier segment has a significant effect on the D2D transit time variance. 2. The magnitude of the variance of slow less reliable carriers (COSCO- COS) compared to faster more reliable carriers (Hyundai-HYU) is significant enough to have an effect on the landed costs of products (imports and exports). Relative influence of segment variance on D2D variance Figures in parenthesis identifies segment sequence from origin to destination 6

7 Use of Results Ocean Carriers Help with developing a market strategy Target customers for specific services (importers and exporters) Pricing policy based on service characteristics speed and reliability Shippers Carrier and mode selection Better understanding of cost components for different levels ofocean service Logistics cost structure for D2D multi-modal international transport at different levels of ocean carrier service

8 Freight Hubs and Gateways

9 Freight Hubs and International Gateways OSU Senior Investigator Morton O Kelly, Dept. of Geography, Social and Behavioral Sciences, College of Arts and Sciences. Other Collaborators Rafael Figueiredo and Prof. Nelio Pizzolato, Industrial Engineering, PUC, Rio de Janeiro Brazil Sponsors: CAPES, Graduate Support for Figueiredo

10 Hubs: Switching Points for Flows in Interaction Systems with Many Origins and Destinations Hub and spoke systems Flows are channeled from origins to destinations via central switching locations Typical journeys involve one or more intermediate stops Methodology: Mathematical modeling, optimization, and simulation Minimize Subject to

11 Example Results Example Questions Which city pairs have sufficient flow to warrant special treatment (direct connection, rather than hub connection)? Which cities can be served from relatively smaller aircraft or trucks (modal integration)? Where are the major hubs located? Where should they be located? Example Results: N. American S. American Flows Domestic vs International Flows Sensitivity to number and location of hubs Need for Gateways

12 Exploiting Geo-fences to Document Truck Activity Times at International Border Crossings

13 Documenting Truck Activity Times with Geo-fences Sponsor U.S. DOT/RITA, Commercial Remote Sensing and Spatial Information Technologies Program OSU Senior Investigators Mark McCord,Civil Eng/City & Regional Planning, College of Engineering Prem Goel, Statistics, College of Natural and Mathematical Science Other Collaborators University of Arizona, Tucson, AZ Michigan Tech Research Institute, Ann Arbor, MI Center for Automotive Research, Ann Arbor, MI Skycomp, Inc., Columbia, MD Principal Stakeholders CEVA Logistics, Inc. General Motors Corporation Michigan DOT (Blue Water Bridge Operations)

14 Documenting Truck Activity Times with Geo-fences Objective: Develop and demonstrate use of existing onboard systems and geo-spatial technologies to document truck times in multiple activities at international borders: approach on roadways, queuing, undergoing customs inspections, Setting: Site-specific empirical studies at Ambassador Bridge and Blue Water Bridge Border Crossings

15 Documenting Truck Activity Times with Geo-fences Geo-fence: electronic polygon encoded into onboard data unit GPS-based location triggers a record when truck crosses the fence Match records for same truck trip to determine time between locations Encode geo-fences to delimit important activities Geo-fence Approach BWB: CAN to US Customs Screening 15

16 Example Results: CA to US Activity Times at AMB Crossing AMB Activity Time ON-MI (min) (2008) Amb usplaza Amb Amb Amb Amb Amb tollfca Amb ustoll Amb usplaza usbridge cabridge caplaza huronchrchrd caapproach # Median P th-Median

17 Use of Results Facilities Operators and Planners Monitor facilities with ongoing data that have previously been not been collected Validate improvements with before and after data Temporal Patterns in Crossing Times Fleet Operators and Shippers Plan allowable time at crossings Monitor important travel time components Proportion* of US-bound trips traversing duty free polygon by HOD and DOW at Ambassador Bridge crossing Day Sun Mon Tue Wed Thu Fri Hour *noise added to proportions to preserve confidentiality of information

18 Improved Vehicle Classification for Monitoring Freight Movements

19 Sponsors Midwest Regional University Transportation Center (MRUTC) NEXTRANS University Transportation Center ODOT- OPREP Ohio Transportation Consortium (OTC) OSU Senior Investigators Benjamin Coifman, Civil Eng/Electrical & Computer Eng, College of Engineering Keith Redmill, Electrical & Computer Eng, College of Engineering Freight Vehicle Classification

20 Freight Vehicle Classification Objectives Research program began by seeking to extend vehicle classification to simpler vehicle detectors (single loop detectors) Second thrust emerged to reduce the labor required to generate ground truth data: Development of a LIDAR based classification system To validate classification performance of any existing classification station. To provide primary classification

21 Single Loop Detector Based Classification Key to the single loop detector work is estimating vehicle speed with an accuracy approaching that of dual loop detector measurements Use distribution of vehicle measurements to deduce speeds and lengths

22 LIDAR Based Classification Pair of vertical scanning LIDAR in speed trap configuration on either side of instrumented probe vehicle Use measured length, height, and shape to classify vehicles Identify when occlusions may degrade performance

23 Use of Results Classification data are used for many transportation applications, e.g., from planning to infrastructure management Extend classification to detector stations normally used strictly for real time traffic monitoring (e.g., Columbus has over 50 such stations) Potentially track freight movements through the network Provide a means that will allow conventional classification stations to continue operating if one detector goes down LIDAR based classification can provide an independent and automated validation of classification stations to ensure accurate performance LIDAR could potentially be used as a primary source of classification (ongoing work)

24 Freight Vehicle Research and Development at OSU s Center for Automotive Research Electric, Hybrid, and Plug-in Power

25 Center for Automotive Research Automotive Vehicle, Component, and System R&D Interdisciplinary Center in College of Engineering Participation by over 20 faculty members, 50 graduate students, 20 research staff, 25 industrial partners 35,000 sq. ft. with advanced experimental equipment and infrastructure Research exp. over $6M/yr Research, education and outreach

26 4WD Heavy Duty Chassis Dynamometer and High- Voltage Battery Emulator and Power Conversion Lab $3M expansion of CAR facilities to accelerate the introduction of new electric power conversion products for the commercial vehicle market Heavy-duty chassis dynamometer in high bay of OSU-CAR Proposed installation by Mustang Dynamometer, Twinsburg, OH

27 Expand CAR facilities, install equipment to test and develop electric power systems for commercial and other HD cycle vehicles Objectives Optimize converters for high voltage operation Integrate converters into existing vehicle electrical systems Meet cost objectives Expand market (niche market previously developed for refrigerated trucks) Third Frontier Wright Project Collaborators Vanner, Inc., Columbus, OH American Electric Power, Columbus, OH STMicroelectronics, Livonia, MI Fil-mor Express, Inc., Cannon Falls, MN Hybrid Vehicle with Converters and Plug-in Function

28 Development of (first ever) Electric Semi-tractor Towing capacity: 53,000 lbs trailer legal OH, 90,000 + max Max speed: 60mph electronically limited (72 mph max) Power 1100 Hp max, 350 continuous, direct drive, NO trami Range: 150 miles fully loaded (80,000 lbs GVW) Charging time: minutes or 5 minutes battery swept Battery life: 3000 cycles warranted, 4500 cycles expected Charge price: USD Unit price: 299,000 USD Investment recovery: 1-2 years Delivery time: 6 month

29 Acknowledgments C.G. Cantemir, Ctr. for Automotive Res., OSU B. Coifman, CEEGS/ECE, OSU M. O Kelly, Geography, OSU G. Rizzoni, Ctr. for Automotive Research, OSU J. Saldanha, Fisher College of Business, OSU The views, opinions and statements contained in this presentation are those of the presenter and do not necessarily represent views, position, or policy of any individual or organization referenced in the presentation. 29