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Solving Large-Scale Two-Echelon Location Routing Problems in City Logistics

Banu Ulusoy Dereli, Gerhard Hiermann, Maximilian Schiffer

TL;DR

The paper addresses the challenge of designing large-scale two-echelon city logistics networks that incorporate both stationary and mobile micro-depots and allow direct shipment from the main depot. It introduces a tailored Adaptive Large Neighborhood Search (ALNS) framework augmented with a set-cover component and a hybrid decomposition (cluster-first, route-second) strategy to achieve scalable, high-quality solutions. Empirical results on standard benchmarks and a Munich case study show that direct shipment reduces total costs by about 4.7% and emissions by about 11%, while hybrid depot configurations yield additional savings (up to 5.89%) and significant distance reductions; decomposition dramatically speeds computation (up to 15x). These findings underscore the practical benefits of mobile depots and direct shipments in urban logistics and provide actionable managerial guidance for configuring hybrid depot networks. The work lays a foundation for future extensions to stochastic demand and data-driven learning approaches to further enhance performance in city-scale logistics planning.

Abstract

Logistic service providers increasingly focus on two-echelon distribution systems to efficiently manage thousands of deliveries in urban environments. Effectively operating such systems requires designing cost-efficient delivery networks while addressing the challenges of increasing e-commerce demands. In this context, we focus on a two-echelon location routing problem with mobile depots and direct shipment, where decisions involve locating micro-depots, and designing first and second-level routes. Our model also incorporates the flexibility of direct shipments from the main depot to customers. To solve such large-scale problems efficiently, we propose a metaheuristic approach that integrates a set cover problem with an adaptive large neighborhood search (ALNS). Our ALNS approach generates a set of promising routes and micro-depot locations using destroy and repair operators while using a local search for intensification. We then utilize the set cover problem to find better network configurations. Additionally, we present a decomposition-based cluster-first, route-second approach to solve large-scale instances efficiently. We show the efficacy of our algorithm on well-known benchmark datasets and provide managerial insights based on a case study for the city of Munich. Our decomposition approach provides comparable results while reducing computational times by a factor of 15. Our case study results show that allowing direct shipment can reduce total costs by 4.7% and emissions by 11%, while increasing truck utilizations by 42%. We find that integrating both stationary and mobile micro-depots, along with allowing direct shipments, can reduce total costs by 5.9% compared to traditional two-echelon delivery structures.

Solving Large-Scale Two-Echelon Location Routing Problems in City Logistics

TL;DR

The paper addresses the challenge of designing large-scale two-echelon city logistics networks that incorporate both stationary and mobile micro-depots and allow direct shipment from the main depot. It introduces a tailored Adaptive Large Neighborhood Search (ALNS) framework augmented with a set-cover component and a hybrid decomposition (cluster-first, route-second) strategy to achieve scalable, high-quality solutions. Empirical results on standard benchmarks and a Munich case study show that direct shipment reduces total costs by about 4.7% and emissions by about 11%, while hybrid depot configurations yield additional savings (up to 5.89%) and significant distance reductions; decomposition dramatically speeds computation (up to 15x). These findings underscore the practical benefits of mobile depots and direct shipments in urban logistics and provide actionable managerial guidance for configuring hybrid depot networks. The work lays a foundation for future extensions to stochastic demand and data-driven learning approaches to further enhance performance in city-scale logistics planning.

Abstract

Logistic service providers increasingly focus on two-echelon distribution systems to efficiently manage thousands of deliveries in urban environments. Effectively operating such systems requires designing cost-efficient delivery networks while addressing the challenges of increasing e-commerce demands. In this context, we focus on a two-echelon location routing problem with mobile depots and direct shipment, where decisions involve locating micro-depots, and designing first and second-level routes. Our model also incorporates the flexibility of direct shipments from the main depot to customers. To solve such large-scale problems efficiently, we propose a metaheuristic approach that integrates a set cover problem with an adaptive large neighborhood search (ALNS). Our ALNS approach generates a set of promising routes and micro-depot locations using destroy and repair operators while using a local search for intensification. We then utilize the set cover problem to find better network configurations. Additionally, we present a decomposition-based cluster-first, route-second approach to solve large-scale instances efficiently. We show the efficacy of our algorithm on well-known benchmark datasets and provide managerial insights based on a case study for the city of Munich. Our decomposition approach provides comparable results while reducing computational times by a factor of 15. Our case study results show that allowing direct shipment can reduce total costs by 4.7% and emissions by 11%, while increasing truck utilizations by 42%. We find that integrating both stationary and mobile micro-depots, along with allowing direct shipments, can reduce total costs by 5.9% compared to traditional two-echelon delivery structures.

Paper Structure

This paper contains 32 sections, 49 equations, 9 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. State of the Art
  3. Contribution
  4. Organization
  5. Problem Definition
  6. Adaptive Large Neighborhood Search
  7. Initial Solution Generation
  8. Destroy Operators
  9. Repair Operators
  10. Local Search
  11. Set Cover Problem
  12. Correction Heuristic.
  13. Network Pool Size Management.
  14. Adaptive Mechanisms
  15. Decomposition
  16. ...and 17 more sections

Figures (9)

  • Figure 1: Illustration of the with mobile depots and direct shipment network
  • Figure 2: Visualization of decomposition approach.
  • Figure 3: Case study map for the city of Munich and demand distribution.
  • Figure 4: Comparison of baseline and decomposition approaches across various metrics.
  • Figure 5: Comparison of 2e-lrp with mobile micro-depots (MD) and 2e-lrpmd with direct shipment (DS). Number of (a) trucks, (b) cargo bikes, (c) stationary micro-depots, (d) MDs.
  • ...and 4 more figures