代做ISyE 6339 Physical Internet Engineering Casework 2代写留学生Matlab语言程序
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Casework 2
Hyperconnected Road-Based Freight Transportation in Continental USA
These caseworks focus on truck-based freight transportation across the Continental United States of America, with an emphasis on contrasting the current system with a Physical Internet enabled hyperconnected system. Examination of the tasks will reveal clearly the distinctions of focus and scope between caseworks 2.1 and 2.2.
Addressed trucking flow includes flow from and to ports as well as railyards to support multimodal transfer, without exploring the intensification of multimodality to contain casework complexity.
The casework relies on statistics and forecasts compiled by the Federal Highway Administration of the US Department of Transportation, within its Freight Analysis Framework (FAF). We suggest students to study carefully the FAF website at https://ops.fhwa.dot.gov/freight/freight_analysis/faf/.
The Freight Analysis Framework simplifies the freight transportation demand by aggregating it by specific mode and commodity (among a set of 42) between 132 FAF zones.
• List of FAF zones can be found in 2017_CFS_Metro_Areas_with_FAF_Table.xlsx at
https://faf.ornl.gov/faf5/
• FAF zone shape files can be obtained at
https://faf.ornl.gov/faf5/data/2017_CFS_Metro_Areas_with_FAF.zip
FAF provides numerous Tables and Maps depicting the estimated freight flow. Below is an example focused on mapping the truck-based estimated average daily flow volumes on the national highway system in 2030.
To simplify data processing and analysis in this casework, as depicted and tabled below, we have aggregated the truck-based flows along the main highways: North-South 5, 15, 25, 35, 55, 65, 75, 85, and 95; East-West 10, 20, 40, 70, 80, and 90; and used a set of 39 highway intersection proxies. Multi-source and multi-destination routing of flows within a specific FAF zone is not addressed in this casework, each zone being treated as a single point as done in the FAF modeling.
The creation of the regional flow file utilizing Freight Analysis Framework (FAF) data involved series of steps to model the movement and volume of freight across different regions. Here's a comprehensive overview of the steps taken to achieve this:
1. Data Acquisition: We first downloaded the FAF origin-destination tonnage data
specifically for FAF regions. This foundational step ensured access to a rich dataset detailing the quantities of goods moved between various geographic locations.
2. Commodity Selection: To refine the data for practical analysis, we focused on
commodities that could logically be grouped together, intentionally excluding categories such as live animals, oil, and liquid chemicals due to their unique transportation requirements and regulations.
3. Payload Factor Estimation: Leveraging the payload factors provided by FAF, we
estimated the number of trucks required for each commodity type from each origin to destination. This critical step involved applying specific coefficients that represent the average load carried by trucks, thus enabling the conversion of tonnage data into a more tangible measure of freight traffic in terms of truck movements.
4. Flow Calculation: We finally aggregated these estimated truck movements across all
origin-destination (O-D) pairs to derive a total flow metric. This comprehensively represents the entirety of freight movement within the dataset's scope, providing a clear picture of regional freight dynamics.
We are providing the “FAF based casework Data_File” workbook, composed of three worksheets described hereafter. The first worksheet is called “FAF_Regional_Flows” . Below is an overview of the columns present in the worksheet:
1. Origin FAF Zone: Indicates the origin zones of freight.
2. Origin_lat & Origin_long: Geographical coordinates (latitude and longitude) of the origin zones.
3. Destination FAF Zone: Indicates the destination zones of freight.
4. Destination_lat & Destination_long: Geographical coordinates (latitude and longitude) of the destination zones.
5. Thousand tons in 2022: The volume of freight in thousand tons for the year 2022.
6. Thousand tons in 2025: Predicted volume of freight in thousand tons for the year 2025.
7. Thousand tons in 2025_high: High growth prediction for the volume of freight in thousand tons for the year 2025.
8. Thousand tons in 2030: Predicted volume of freight in thousand tons for the year 2030.
9. Thousand tons in 2030_high: High growth prediction for the volume of freight in thousand tons for the year 2030.
10. Trucks in 2022: The conversion of the 2022 freight volume into the equivalent number of trucks, based on the payload of the commodities.
11. Trucks in 2025: The conversion of the predicted 2025 freight volume into the equivalent number of trucks, based on the payload of the commodities.
12. Trucks in 2025_high: The conversion of the predicted high growth 2025 freight volume into the equivalent number of trucks, based on the payload of the commodities.
13. Trucks in 2030: The conversion of the predicted 2030 freight volume into the equivalent number of trucks, based on the payload of the commodities.
14. Trucks in 2030_high: The conversion of the predicted high growth 2030 freight volume into the equivalent number of trucks, based on the payload of the commodities.
This second worksheet named “Intersection_ID” is structured with the following columns:
1. Intersection ID: Unique identifier for each intersection.
2. Interstate_Intersection: Describes which two interstates are intersecting at this junction, providing crucial data for understanding traffic flow and planning.
3. Location: The name or description of the location of the intersection for easy identification.
4. Lat & Long: Geographical coordinates (latitude and longitude) pinpointing the precise location of the intersection.
This sheet also contains the visualization of the intersections along with the identifiers.
This third worksheet named “Distance_Intersections” is structured with the following columns:
1. Intersection_A & Intersection_B: Identifiers for the paired intersections being analyzed.
2. A_lat & A_long, B_lat & B_long: Geographical coordinates for intersections A and B, respectively.
3. Duration (mins): Travel time between the intersections.
4. Distance (km): Road distance between the intersections.
We are also providing the “Georgia 10 days” workbook, composed of two worksheets described hereafter. Both start with the same four columns as workbook “FAF_Regional_Flows” .
The worksheet “Atlanta Co. A, B, & C 10 days” focuses on three companies and their freight flow demand to/from the Atlanta FAF zone from/to each other FAF zone (each corresponding to a row) on simulated days 1 to 10 (corresponding each to a column), expressed in truck fractions.
The worksheet “Georgia 10 days” similarly focuses at a higher degree of aggregation on the overall freight flow demand to/from Georgia (its three FAF zones) from/to each other FAF zone (each corresponding to a row) on simulated days 1 to 10 (corresponding each to a column), expressed in truck fractions.
Tasks
1. Proceed to a Pareto analysis of the inter-zone flows, first accounting for unidirectional flows from a zone to another, then for bidirectional flows between zones, for 2025 and 2030 (average and max). Rank and plot flow-from-to pairs and flow-between pairs, as well as zones (and zone groups as you may deem pertinent), accounting for all their incoming and outgoing flows. Depict results in terms of tons and truck shipments per day, hour, minute, and then second. Analyze your results, aiming to provide key insights.
2. Provide vivid inter-zone flow maps depicting through direct links the flows between pairs of zones in 2025 and 2030 (average and max). For clarity and emphasis, you are encouraged to produce distinct maps, for example for groups of zone pairs distinguished through the Pareto analysis. Include a FAF-zone-specific zooming on a small representative set of FAF-zones, including the Atlanta FAF-zone. Also, make sure you emphasize the total bidirectional flows, unidirectional flows (A to B, B to A), as well as the flow imbalances. Analyze your results, aiming to provide key insights.
3. For each trip between each pair of zones with non-zero origin-destination (O-D) FAF freight flow estimate:
a. Compute the lower bound on traveled distance by assuming the truck take a direct path between the zones, so the distances are computable using the provided longitude and latitude coordinates.
b. Given your answer to (3.a), assuming your best 2025 and 2030 estimates based on published literature, compute the lower bound on:
o Energy consumption assuming internal combustion engine (ICE) or electric (E) trucks are used:
1. Kilowatt-hours (kwh) for ICE and E trucks
2. Diesel gallons (for ICE trucks)
o Transport time assuming a steady 60-miles/hour speed (excluding any stop);
o Travel time including transport and reenergization, assuming autonomous ICE or E trucks are used, given your estimates for:
1. Truck autonomy of each type
2. Fueling time of an ICE-truck
3. Charging time of an E-truck
4. Battery swapping time of an E-truck (assuming charged batteries are available when needed for reenergizing an E-truck).
o Travel time as in (3.b.iii) assuming single-driver and two-driver trucking, respecting the current state-specific trucking time regulations assuming that the usual 11-hour-maximum regulation applies in all states (see Georgia regulations for details).
c. Contrast and analyze your results, aiming to provide key insights.
4. Considering the estimated freight transportation demand between each pair of zones, and assuming loaded trucks are in fact loaded 60% of their capacity in average for each daily end-to-end O-D interzone transportation, compile 2025 and 2030 (average and max) overall lower bound estimates for total number of truck trips, travelled distance, energy consumption, greenhouse gas emission, transport time and travel time, trucks, and truckers based in each FAF zone (when pertinent), for the combinations of assumptions in (3). You have to account for two extreme empty truck flow estimations:
a. Assuming empty truck have to travel back from destination to origin;
b. Assuming daily inbound and outbound imbalances at each zone to have trucks not having to travel empty all the way from reached destination back to origin, but rather to have them travel empty to a zone distinct from the reached destination zone only to contribute to rebalancing nodal flow. This assumption has drastic implications for truckers and trucks as they may get back to their home base after long multi-zone journeys.
o The logic is essentially as follows. Assume the reached destination zone z for a truck has 900 inbound flow and 800 outbound flows. Then the assumption would be that 800 trucks would not have to travel empty and would simply pick up a shipment out of zone z the next day or sooner if available depending on timing. There would be expectation of 100 trucks arriving daily in zone z and having to move empty to another zone to pick up a next shipment. Ideally, each such trucks would only have to move empty to a zone z’ nearby zone z, that zone z’ having a daily inbound flow less than its outbound flow.
Overall, at an aggregate level, when not accounting directly for the aim of getting the truck and trucker-s back to their home base, this corresponds to the well-known transportation problem minimizing total travel between nodes in the network where each node is either a source or a demand node (here based on their daily flow imbalance).
You may choose to solve it optimally using an existing solver or to generate a heuristic solution using an available heuristic or your own justified heuristic.
c. Contrast and analyze your results, aiming to provide key insights.
5. Now assuming travel along the highway network provided in the “FAF based casework Data_File” workbook:
a. Draw the simplified highway network with its nodes (intersections) and links (highway segments), depicting the length (miles) and duration (hours @ 60mi/hr) of each link, with and without the map underlaid.
b. Using an available optimal shortest path algorithm, for each pair of zones with non- zero freight flow, compute the shortest-distance path from origin to destination through the highway network.
c. Provide drawings of representative inter-zone shortest path samples.
6. Repeat (3) now based on network-based estimates assuming a single truck is to move each shipment from its origin to its destination, the truck (and its driver-s for non-autonomous truck) being dedicated for this specific O-D trip. Contrast and analyze your end-to-end network-based results as done in (3), then contrast and analyze your lower-bound results vs end-to-end network-based results.
7. Leveraging your results from (5 and 6), compute aggregate 2025 and 2030 (average and max) daily total, loaded, and empty flow estimates, assuming end-to-end network-based transportation, for:
a. Each highway network link lii, between intersection nodes i and i’, differentiating flows from i to i’ and flows from i’ to i.
b. Each highway network intersection node i, differentiating flow within the node inbounding from each link li,I and outbounding to each link lii,.
Depict on drawings of the network the flows, leveraging node and link sizes and colors, as well as directional arrows and numbers, to make vivid your results. Contrast loaded-flow network renderings, empty-flow network renderings, and total-flow network renderings. Analyze your results.
8. Consider companies A, B, and C based in Atlanta, Georgia, for which you are provided their freight flow demand for days 1 to 10. Assume that each company promises to ship its ordered products within 3 days from ordering time (so, demand in day 1 must be shipped by day 3 at the latest), and all products ordered in days 8, 9, and 10 must be shipped by day 10 at the latest. As above, assume the companies ship directly from origin to destination through a single-stop truck route. Assume also that the companies each use dedicated trucks, so they are responsible for their empty travel.
a. Provide an optimized transportation plan over the 10 days for each of the three companies and proceed to a thorough performance assessment for each company, as requested in (3).
b. Assuming the three companies engage in a collaborative agreement in which they jointly optimize their transportation plans. For simplicity purposes, assume here that their target location in each FAF zone is very near to the FAF zone coordinates. Provide an optimized collaborative transportation plan over the 10 days for the group of three companies and proceed to a thorough performance assessment for the group and each company, as requested in (3). Assess the added value of the collaboration for each company.
c. Discuss the differences and convergences of perspectives and outcomes when aggregating at the FAF zone versus when addressing the cases of specific companies.
9. Now assume a hyperconnected national freight system where:
• There are clusters of inter-regional logistic hubs located around each highway intersection node and other ones located at specific to enable (1) reenergization respecting truck autonomy and (2) getting truckers back home mostly every day for quality of life and retention purposes while keeping the freight moving.
• Freight travels only:
o From its origin zone to its entry hub in the nearest hub cluster given its final destination.
o Along highway links, from hub to hub at adjacent network nodes.
o To its final destination from its exit hub in the destination-nearest hub cluster.
• Trucks only travel as follows:
o Shuttling between regional hub-s within a FAF zone (here assumed at FAF zone coordinates) and FAF-zone-entry-exit inter-regional hubs.
o Binodal shuttling between hubs at distinct ends of a specific highway network link, leveraging balanced flows between the two nodes of the link.
o Trinodal and quadrinodal shuttling between network-adjacent nodes to contribute to rebalance flows between these nodes.
• Freight consolidation is enhanced by enabling:
o Consolidating flows of shipments within a FAF-zone from multiple shippers heading to a shared intermediary hub (or hub cluster) toward final destination.
o Consolidating flow inbounding into a node from several links and heading to a shared intermediary hub (or hub cluster) toward final destination.
o Consolidation at inter-regional hubs is performed fast so that (1) inbound freight can be reconsolidated and ready to ship to the next hub within 1 hour and (2) inbound trucks can be back on the road for their next transport leg within 30 minutes.
a. Leveraging the work done in (5) and the new information provided above, design an inter-regional hub cluster network capable of supporting the 2025-2030 horizon (simplified here , as normally we would extend further) given the various scenarios of vehicle energy autonomy (given ICE vs EV trucks), vehicle driving autonomy (autonomous self-driving truck vs human-driven truck), and freight flow demand.
o Provide and justify location of any added hub cluster, ideally at some existing highway exit or intersection (with other not-modeled highways).
b. For each FAF zone, develop a consolidation tree (or wider network for resilience purposes) reaching all its destination FAF zones through the designed hub cluster network. The concept can be illustrated as follows:
o Shippers from a FAF zone would consolidate their shipments to their
destination zones as depicted below for a green zone having 360 trucks-equivalent to 5 other zones. The flows are channeled to hub cluster nodes and links, resulting in the consolidated red inter-hub (cluster) flows.
Shipment set 1: 360 from Z1 to hub (6,2), composed of shipment sets 2 and 3
Shipment set 2: 40 from hub (6,2) to hub (4,2) to Z2
Shipment set 3: 320 from hub (6,2) to hub (6,3), composed of shipment sets 4 and 5
Shipment set 5: 80 from hub (6,3) to Z3
Shipment set 6: 80 from hub (6,3) to hub (8,3)
Shipment set 7: 160 from hub (6,3) to hub (6,4), composed of shipment sets 8 and 9
Shipment set 8: 100 form. hub (6,4) to Z5
Shipment set 9: 60 from hub (6,4) to (3,4) to Z6
c. Consolidate all zonal consolidation trees into a global consolidation network, then estimate loaded freight flows along each directional link and at each hub cluster node in the 2025 and 2030 scenarios.
d. In previous tasks, trucks were assumed to be loaded at 60% as there was no inter- shipper flow consolidation and shippers were assumed to aim for delivering with satisfactory velocity and service level to customers, not letting time to fill full trucks toward each direction. This is not the case here.
Given the freight flows estimated in (9.c), determine the fraction of shipments departing from each node that would not lead to full truck loads along its outbound links. Estimate average utilization of trucks associated with such fraction of shipments. Then compute estimates on the number of loaded trucks flowing through each directional highway link and hub cluster node.
e. Using the same type of methods used earlier, yet adapted to hyperconnected transportation, estimate the inter-hub empty travel in each link induced by zonal flow imbalances. Then compute estimates for the number of empty trucks traveling along each directional links. Combining these with the loaded truck travel estimates, compute the total truck estimates.
f. Estimate the frequency of truck departures from and arrivals on each hub cluster node along each of its links. Discuss the impact of such frequencies on the expected freight and truck dwell times at hubs, and on the overall freight origin-to-destination time.
10. Repeat (3) adapted for the hyperconnected freight system. Make sure to highlight results along links and nodes of the highway-based network, and for the total system. Contrast your results with those of the dedicated origin-destination direct flow system. Provide key insights.
11. In (9.b), you were asked to create a consolidation tree for each FAF zone. Using such a tree has key impact on the freight flow and concentration. How could you alter and create a consolidation network that would better account for the need to assume that flows may not always be directed to the shortest route from source to destination along the highway network, in fact that would better reflect the need to account for resilience enhancement. Use the Atlanta FAF zone to demonstrate your alternative approach. Contrast your results with the results you obtained in (8.b) and discuss the expected consequences when this is done for each FAF zone.
12. In the dedicated origin-destination direct flow system studied in (1-7), truck drivers are essentially needed to be based in each zone. In the hyperconnected system, truck drivers are needed to be based around each zonal hub cluster node and around each hub cluster node along the highway network.
a. Estimate the number of truck drivers needed in each zone by dedicated origin- destination direct flow system.
b. Estimate the number of truck drivers needed in around each zonal hub cluster node and around each hub cluster node along the highway network in the hyperconnected system.
c. Contrast and analyze your results, aiming to provide key insights.
13. Consider the case of the three companies based in Atlanta studied in (8), now leveraging the hyperconnected transportation system.
a. Use adapted versions of the approaches described / used in (9 to 11) to provide optimized transportation plans for each company, considering they are three of out of many hundreds using the hyperconnected system’s open access hubs and trucks.
b. Provide a comparative assessment of the expected achieved performance for each company and over their set (as done in (8)), as contrasted with their solo and group performance estimated in (8). Analyze and provide key insights.
14. Use the FAF zones in the state of Georgia to provide a deep meaningful generalized comparison of the two systems (beyond the three-company example), notably contrasting how they treat flows into, across, and out of Georgia, providing comparative Tables, diagrams, and maps to enhance your comparison.
15. Consider that the three Atlanta based companies, once having started to leverage the hyperconnected system, realize they could potentially gain by pre-deploying their products at open-access deployment centers near to the hub clusters in the network.
a. Develop a smart strategy to maximize full truckload transports while ensuring short order-to-delivery times and low inventory across the network.
b. Assess the performance of your strategy by applying it for the 10-day case. Contrast your results to those obtained in (13).
c. Analyze the large-scale impact if most companies across all FAF zones would utilize such a strategy. Provide key insights.
16. Synthesize your key challenges and learnings in performing this casework.