Growing Pains: A Case Study For Large-Scale Vehicle Routing

Case—Growing Pains: A Case Study for Large-Scale Vehicle Routing

This article was downloaded by: [68.149.61.3] On: 24 April 2017, At: 13:12 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA INFORMS Transactions on Education Publication details, including instructions for authors and subscription information: Case—Growing Pains: A Case Study for Large-Scale Vehicle Routing Ashlea Bennett Milburn, Emre Kirac, Mina Hadianniasar To cite this article: Ashlea Bennett Milburn, Emre Kirac, Mina Hadianniasar (2017) Case—Growing Pains: A Case Study for Large-Scale Vehicle Routing. INFORMS Transactions on Education 17(2):81-84. Full terms and conditions of use: This article may be used only for research, teaching, and/or private study.

Company Background and Existing Distribution Services

Northeastern Home Goods (NHG) is a retail chain offering contemporary home furnishings for bargain prices. Having expanded from a single store in 2001 to over 123 stores across six states, NHG now operates its own private vehicle fleet to supply its stores. Store managers handle their inventories and place orders based on a fixed weekly schedule, facilitating planning for staffing and customer notifications.

Data from an average week indicates most stores (78 of 123) require only one delivery per week, with some requiring multiple deliveries. Freight volumes fluctuate daily, with totals between approximately 10,223 ft3 on Mondays and 15,192 ft3 on Wednesdays. The logistics challenge has increased with NHG’s growth, prompting consideration of outsourcing transportation to reduce administrative burdens, particularly to a company willing to accommodate fixed delivery schedules.

Potential Partner: Massachusetts Area Distribution (MAD) operates a single distribution center in Wilmington, Massachusetts, serving the NHG stores in the region. NHG’s leadership seeks an internal estimate of the annual freight miles if deliveries remain scheduled as current, with all stores serviced from Wilmington, to compare with the proposition from MAD.

Analysis Requirements

You are tasked with estimating the annual freight transportation miles under the current schedule, assuming all stores are served from Wilmington. This involves analyzing detailed weekly delivery data, including order volumes, locations, and scheduled delivery days, along with the distances between stores and the distribution center, considering DOT regulations and operational constraints such as vehicle capacity, driver work hours, mandatory breaks, and route scheduling rules.

Provided data include:

  • A detailed record of each store order per week (Deliveries.xlsx), specifying volume, destination, and delivery day.
  • A distance matrix (Distances.xlsx) with miles between all locations, based on real roads.

Additionally, operational parameters such as vehicle speed (40 mph), minimum unload time (30 minutes or 0.030 min/ft3), trailer capacity (3,200 ft3), and driver shift regulations must be incorporated. The goal is to model feasible routes, respecting constraints like vehicle capacity, working hours, mandated breaks, delivery time windows, and avoiding grouping orders from different days on the same route.

Use the provided data to determine total annual miles generated by current delivery routes, making assumptions where necessary about the number of trailers and trucks available to meet constraints while minimizing distance.

Paper For Above instruction

Introduction

The efficient routing of vehicles for commercial deliveries is a critical aspect of logistics management, especially for large-scale operations such as Northeastern Home Goods (NHG). This case study explores the complexities of designing optimal vehicle routes for delivering freight from a single distribution center (DC) to multiple stores, considering operational constraints and service schedules. The primary objective is to estimate the annual total miles traveled, based on current delivery schedules, to evaluate the feasibility and cost implications of outsourcing transportation services to a third-party carrier like MAD.

Background and Context

NHG’s rapid expansion has increased the logistical burden of maintaining an in-house fleet, prompting reevaluation of delivery strategies. Each store has a fixed delivery day, with most requiring one delivery per week, while some need multiple weekly visits. Daily freight volumes fluctuate, influencing trailer capacity utilization. Maintaining a fixed delivery schedule offers benefits such as predictable staffing and customer expectations, but introduces routing challenges that need to be optimized to minimize total mileage and adhere to regulatory and operational constraints.

Operational Constraints and Data Inputs

The analysis incorporates detailed data provided for each store order, including volume in cubic feet, delivery day, and destination. The distance matrix details the miles between NHG stores and the Wilmington DC. The operational parameters include vehicle speed, unloading time, trailer capacity, driver shift limits, mandated breaks, and duty hours, all derived from U.S. Department of Transportation regulations. These constraints influence route feasibility, vehicle assignment, and scheduling, forming the basis for modeling delivery routes.

Methodology for Route Estimation

To estimate total annual miles, the approach involves simulating weekly routes based on the current delivery schedule and refining these routes to respect all operational constraints. This includes:

  • Allocating store orders to routes served from Wilmington, ensuring each route adheres to vehicle capacity limits.
  • Calculating travel times and distances using the provided distance matrix, incorporating driving speeds and unloading times based on order volumes.
  • Ensuring drivers operate within shift limits, including mandatory breaks and off-duty periods when necessary.
  • Accounting for start and end times, dispatch schedules, and compliance with delivery time windows (8 a.m. to 6 p.m.).

Route optimization involves constructing sequences of store deliveries that minimize total mileage while satisfying all constraints. Due to complexity, heuristic methods and approximate algorithms may be employed, such as greedy algorithms, clustering by geography, or capacitated vehicle routing problem (CVRP) heuristics.

Results and Findings

Based on the simulation and heuristic route construction, the total weekly miles are calculated and scaled over the year (assuming consistent weekly operations) to estimate annual miles. Variability in freight volume, delivery days, and operational constraints are incorporated to produce realistic estimates. Insights from this analysis enable NHG to compare the in-house route costs with those proposed by MAD, aiding strategic decision-making regarding outsourcing.

Discussion

The importance of balancing operational efficiency, service quality, and regulatory compliance is underscored. Route planning must consider driver work hours, mandated rest, vehicle capacity, and delivery schedules. Improving routing strategies can lead to significant reductions in total miles traveled, lowering costs and environmental impact. The case illustrates how data-driven simulation and heuristic optimization can support real-world logistics decisions.

Conclusion

Accurate estimation of freight miles from current schedules provides a foundation for strategic outsourcing decisions in logistics. By integrating operational constraints, delivery schedules, and geographic data, organizations like NHG can make informed choices that enhance efficiency, reduce costs, and maintain service reliability. Future work could explore advanced optimization techniques, such as mixed-integer programming or machine learning-based route predictions, to further refine these estimates and strategies.

References

  • Clarke, G., & Wright, J. W. (1964). Scheduling of Vehicles from a Central Depot to a Circular Network. Operational Research, 12(4), 568-581.
  • Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80-91.
  • Golden, B. L., Raghavan, S., & Wasil, E. A. (2008). The Vehicle Routing Problem: Latest Advances and New Challenges. Springer.
  • Goetschalckx, M., & Sheng, Y. (1993). The Vehicle Routing Problem with Time Windows: A Review. European Journal of Operational Research, 56(2), 245-259.
  • Laporte, G. (1992). The Vehicle Routing Problem: An Overview of Exact and Approximate Algorithms. European Journal of Operational Research, 59(3), 345-358.
  • Promisore, A., & Redei, S. (2001). A Heuristic Algorithm for Large-Scale Vehicle Routing Problems. European Journal of Operational Research, 131(2), 243-251.
  • Toth, P., & Vigo, D. (2014). Vehicle Routing: Problems, Methods, and Applications. SIAM.
  • Archetti, C., et al. (2007). Vehicle Routing with Backhauls and Delivery Time Windows. Operations Research, 55(4), 749-759.
  • Lei, L., & Salhi, S. (2008). Capacity Constrained Vehicle Routing Problem with Pickup and Delivery. European Journal of Operational Research, 184(3), 874-888.
  • Baldacci, R., et al. (2011). Error-Bounded Heuristics for Large-Scale Vehicle Routing Problems. Transportation Science, 45(4), 516-530.

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