Planning and design of intermodal hub networks: A literature review

Highlights

• We review recent literature on intermodal hub network design.

• Aspects that make models more realistic are emphasized.

• Models are analyzed in terms of their main components.

• We provide many interesting opportunities for research.

• We propose a typology of strategic decisions in intermodal transportation.

Abstract

Intermodal transportation plays a key role in modern transportation systems. There is a high interest into design efficient and low-cost intermodal networks. In this paper, we review more than 100 papers on recent literature regarding intermodal network design from a hub location perspective. We discuss recent trends and provide more than 20 future research directions focused on the modeling of realistic intermodal transportation systems. Findings and research directions are structured in terms of the modeling of internal and external factors and solution features, and we found that current models lack of realism in the modeling of internal factors of intermodal hub networks like hubs and vehicles.

Introduction

Intermodal transportation refers to freight transportation from an origin to a destination using at least two modes of transport (Steadieseifi et al., 2014). Most of the intermodal transportation is devoted to container transport (Crainic and Kim, 2007). The efficient design of containers allows to exploit economies of scale advantages of high capacity transportation modes and handling equipment at terminals. This concept has gained remarkable success in practice, initially in sea transportation, and, more recently, in inland transportation (Christiansen et al., 2007, Crainic and Kim, 2007, Bontekoning et al., 2004).

In an intermodal hub network transport, requirements are associated with each pair of origin–destination (OD) nodes. This characteristic is also referred to as many to many distribution networks (Campbell, 2013). OD demand may be supplied through a direct transport from the origin to the destination node using a low-capacity vehicle (see Fig. 1). Alternatively, the demand originated in a set of nodes may be collected by low-capacity vehicles and consolidated into a specialized facility or origin hub. Then, there is a consolidated transport towards a destination terminal using a high-capacity transportation mode. The freight is finally distributed to destination nodes in low-capacity vehicles. Consolidation at hubs is paramount since it allows to get the benefits of economies of scale due to transport in high capacity vehicles. The reader can be referred to surveys considering broad aspects of intermodal transportation including: (Crainic and Kim, 2007, Woxenius, 2007, Caris et al., 2013, Steadieseifi et al., 2014, Crainic et al., 2018, Ertem et al., 2017).

This review is concerned on the planning and design of intermodal hub networks. This includes the location of hubs, which is perhaps the most important strategic decision. However, according to Crainic and Kim, 2007, Christiansen et al., 2007, other strategic decisions of interest in intermodal transportation include:

• The location of terminals, customer allocation to terminals, and direct transport services.

• The characteristics of the transportation mode, i.e. vehicle capacity, fleet size, and mix decisions.

• The hub characteristics i.e. capacity, design, equipment type and number.

We focus on reviewing papers that have addressed the first kind of decisions, i.e. location decisions, and analyze how the remaining decisions have been integrated into the first one. Given the characteristics of the system, we face the review process into the light of hub location theory. Surveys on hub location include (Alumur and Kara, 2008, Campbell, 2013, Farahani et al., 2013, Contreras, 2015). More recent surveys include (Contreras and O’Kelly, 2019, Alumur et al., 2021). We aim to provide a review of current literature and propose future research directions with an emphasis on container transportation applications.

Our interest is to review mathematical programming models. The description of algorithmic developments is briefly summarized. To structure the review, we follow a somewhat practical model analysis procedure including:

• General model characteristics,

• Objective functions,

• Constraints,

• Transportation data,

• Solution methods.

Further, we have analyzed a sample of over 100 recent contributions mostly published from 2014 to 2020.

One distinguishing feature of our review, compared to other existing reviews, is that we focus on aspects that make models more realistic for practical applications. We found that there is a lack of realism regarding the modeling of internal factors, which are related to the main elements of intermodal hub networks like hubs, transportation modes, and the physical network. Most formulations are rough simplifications of these elements ignoring key characteristics of transportation modes, hubs, and routing behaviors. On the other hand, external factors are exogenous to the system and affect its performance or the decision-making process. Aspects like data uncertainty, disruptions, the interaction between actors, economic and customer behavior, environment, among others, are examples of external factors. There seems to be a tendency in the literature to prioritize the modeling of external factors. We propose several research topics that are related to the modeling of internal factors, external factors, and solution algorithms.

The remainder of this paper is organized as follows. Section 2 presents the review methodology. Section 3 describes intermodal hub network model characteristics. Section 4 discusses objective functions and multi-objective formulations. Section 5 provides a typology of decisions in intermodal hub network design models. Section 6 describes model constraints with a priority on the discussion of special network structures. Section 7 discusses real case studies along with stochastic formulations. Section 8 briefly describes solution algorithms. Finally, Section 9 discusses our findings and provides further research directions.