Abstract

This paper investigates a dynamic and stochastic shipment matching problem faced by network operators in hinterland synchromodal transportation. We consider a platform that receives contractual and spot shipment requests from shippers, and receives multimodal services from carriers. The platform aims to provide optimal matches between shipment requests and multimodal services within a finite horizon under spot request uncertainty. Due to the capacity limitation of multimodal services, the matching decisions made for current requests will affect the ability to make good matches for future requests. To solve the problem, this paper proposes an anticipatory approach which consists of a rolling horizon framework that handles dynamic events, a sample average approximation method that addresses uncertainties, and a progressive hedging algorithm that generates solutions at each decision epoch. Compared with the greedy approach which is commonly used in practice, the anticipatory approach has total cost savings up to 8.18% under realistic instances. The experimental results highlight the benefits of incorporating stochastic information in dynamic decision making processes of the synchromodal matching system.

Highlights

  • Hinterland transportation is the movement of shipments between deep-sea ports and inland terminals by trucks, trains, barges, or any combination of them (SteadieSeifi et al 2014)

  • As an extension of Guo et al (2020), this paper proposes an anticipatory approach to incorporate the stochastic information of spot requests in the dynamic shipment matching processes

  • We introduced a dynamic and stochastic shipment matching (DSSM)

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Summary

Introduction

Hinterland transportation is the movement of shipments between deep-sea ports and inland terminals by trucks, trains, barges, or any combination of them (SteadieSeifi et al 2014). We consider a synchromodal matching platform owned by a network operator (e.g., European Gateway Services or Contargo) that receives contractual and spot shipment requests from shippers and receives time-scheduled services (e.g., trains) and departure time-flexible services (e.g., trucks) from carriers. The platform aims to provide optimal matches between shipment requests and transport services over a given planning horizon. We define the matching of shipments and services under spot request uncertainty with the aim to minimize total costs over a given planning horizon as the dynamic and stochastic shipment matching (DSSM) problem. The anticipatory approach involves a sample average approximation method that addresses spot request uncertainties and a progressive hedging algorithm that solves the deterministic formulations at each decision epoch of a rolling horizon framework.

Literature review
Methods d
Problem description
Preprocessing procedures
Myopic approach
Anticipatory approach
Sample average approximation method
Progressive hedging algorithm
Experimental setup
Impact of the degree of dynamism
Impact of the number of scenarios and the length of prediction horizon
Impact of the selection of ‐value
Findings
Conclusions and future research
Full Text
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