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The Share-a-Ride Problem with mixed ride-hailing and logistic vehicles

Wen Ji, Shenglin Liu, Ke Han, Tao Liu

Abstract

This study explores the potential of using ride-hailing vehicles (RVs) for integrated passenger and freight transport based on shared mobility. In this crowd-sourced mode, ride-hailing platforms can profit from parcel delivery services, and logistics companies can reduce operational costs by utilizing the capacities of RVs. The Share-a-Ride problem with ride-hailing and logistic vehicles (SARP-RL) determines the number of logistic vehicles (LVs) and the assignment of passenger/parcel requests to RVs and LVs, aiming at maximizing the total RV profits and minimizing logistic costs. An exact solution framework is proposed by (1) generating a feasible trip that serves a given set of requests at maximal profits; (2) generating all feasible trips for the entire set of passenger and parcel requests via an efficient enumeration method; and (3) finding all Pareto-optimal solutions of the bi-objective problem via an $\varepsilon$-constraint method. Not only is the proposed method exact, it also converts the NP-hard problem to a simple vehicle-trip matching problem. More importantly, the total computational time can be compressed to an arbitrary degree via straightforward parallelization. A case study of the Manhattan network demonstrates the solution characteristics of SARP-RL. The results indicate that: (i) Coordinating RV and LV operations to serve passenger and parcel requests (SARP-RL) can simultaneously reduce logistic costs and increase RV profits. (ii) Key factors influencing the performance of SARP-RL include the RV fleet size, spatial distribution of parcel requests, passenger/parcel request ratio, and unit price of transport service, which are quantitatively analyzed to offer managerial insights for real-world implementation.

The Share-a-Ride Problem with mixed ride-hailing and logistic vehicles

Abstract

This study explores the potential of using ride-hailing vehicles (RVs) for integrated passenger and freight transport based on shared mobility. In this crowd-sourced mode, ride-hailing platforms can profit from parcel delivery services, and logistics companies can reduce operational costs by utilizing the capacities of RVs. The Share-a-Ride problem with ride-hailing and logistic vehicles (SARP-RL) determines the number of logistic vehicles (LVs) and the assignment of passenger/parcel requests to RVs and LVs, aiming at maximizing the total RV profits and minimizing logistic costs. An exact solution framework is proposed by (1) generating a feasible trip that serves a given set of requests at maximal profits; (2) generating all feasible trips for the entire set of passenger and parcel requests via an efficient enumeration method; and (3) finding all Pareto-optimal solutions of the bi-objective problem via an -constraint method. Not only is the proposed method exact, it also converts the NP-hard problem to a simple vehicle-trip matching problem. More importantly, the total computational time can be compressed to an arbitrary degree via straightforward parallelization. A case study of the Manhattan network demonstrates the solution characteristics of SARP-RL. The results indicate that: (i) Coordinating RV and LV operations to serve passenger and parcel requests (SARP-RL) can simultaneously reduce logistic costs and increase RV profits. (ii) Key factors influencing the performance of SARP-RL include the RV fleet size, spatial distribution of parcel requests, passenger/parcel request ratio, and unit price of transport service, which are quantitatively analyzed to offer managerial insights for real-world implementation.
Paper Structure (25 sections, 2 theorems, 14 equations, 11 figures, 4 tables, 3 algorithms)

This paper contains 25 sections, 2 theorems, 14 equations, 11 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related work
  3. Integrated passenger and freight transport
  4. Ride-sharing systems
  5. Share-a-ride problem
  6. Problem statement and preliminaries
  7. Relevant notions
  8. Problem description
  9. The drivers' profits
  10. Model development and solution algorithm
  11. Overview
  12. Optimal route for given requests
  13. Generate all feasible trips
  14. Trip-vehicle assignment
  15. Minimum LV fleet size for parcel requests
  16. ...and 10 more sections

Key Result

Lemma 4.1

If a subset of requests $\mathcal{R}^*=\{r_1, ..., r_n\}$ cannot form a trip, then neither can $\mathcal{R}^*\cup\{r_{n+1}\}$ for any $r_{n+1}\in \mathcal{R}\setminus\mathcal{R}^*$.

Figures (11)

  • Figure 1: Solution framework for the proposed SARP-RL.
  • Figure 2: Number of trips of 8 random datasets under various request quantities $l$.
  • Figure 3: Left: Road network and taxi zones of Manhattan, New York City. Right: Spatial distribution of number of passenger trip requests in Manhattan between 13:00 and 14:00 on January 3, 2022.
  • Figure 4: Five spatial distributions of parcel requests: SS, SC (South), SC (North), CS (South) and CS (North). The red ends of the arcs represent the origins, and the blue ends represent the destinations. The yellow arcs represent passenger requests.
  • Figure 5: Left: Increase of total RV profits (%) of five parcel distributions under the same number of RVs for SARP-RL versus RV-only. Right: LV fleet size saving of five parcel distributions under the same number of RVs for SARP-RL versus LV-only. The experimental results are based on the demand scenarios SS-76-24, SC(South)-76-24, SC(North)-76-24, CS(South)-76-24 and CS(North)-76-24.
  • ...and 6 more figures

Theorems & Definitions (6)

  • Definition 3.1
  • Lemma 4.1
  • proof
  • Lemma 4.2
  • proof
  • Remark 5.1