Achieving transport modal split targets at intermodal freight hubs using a model predictive approach

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Abstract

The increase of international freight commerce is creating pressure on the existing transport network. Cooperation between the different transport parties (e.g., terminal managers, forwarders and transport providers) is required to increase the network throughput using the same infrastructure. The intermodal hubs are locations where cargo is stored and can switch transport modality while approaching the final destination. Decisions regarding cargo assignment are based on cargo properties. Cargo properties can be fixed (e.g., destination, volume, weight) or time varying (remaining time until due time or goods expiration date). The intermodal hub manager, with access to certain cargo information, can promote cooperation with and among different transport providers that pick up and deliver cargo at the hub. In this paper, cargo evolution at intermodal hubs is modeled based on a mass balance, taking into account hub cargo inflows and outflows, plus an update of the remaining time until cargo due time. Using this model, written in a state-space representation, we propose a model predictive approach to address the Modal Split Aware – Cargo Assignment Problem (MSA–CAP). The MSA–CAP concerns the cargo assignment to the available transport capacity such that the final destination can be reached on time while taking into consideration the transport modality used. The model predictive approach can anticipate cargo peaks at the hub and assigns cargo in advance, following a push of cargo towards the final destination approach. Through the addition of a modal split constraint it is possible to guide the daily cargo assignment to achieve a transport modal split target over a defined period of time. Numerical experiments illustrate the validity of these statements.

Introduction

Intermodal freight hubs are part of freight transport networks (with access by road, rail and waterways) and may act as gateways between the oversea and the inland transport (van der Horst and De Langen, 2008, SteadieSeifi et al., 2014, Carlo et al., 2014). The Hamburg–Le Havre range, with a coastline of 500 sea miles, counts six seaports with a throughput above 1 million TEU/year: Rotterdam, Hamburg, Antwerp, Bremen/Bremerhaven, Zeebrugge and Le Havre (Steenken et al., 2004, Stahlbock and Voß, 2008). Despite the current economic situation, on the mid to long-term, the transportation of goods over water, rail and road is expected to increase (Baird, 2006). The Port of Rotterdam (the sixth largest container port in the world and the largest container port of Europe in TEU transhipped in 2007) expects doubling the number of full and empty containers by 2030. Currently major deep sea terminals are reaching their maximum capacity. The rapid increase in the international freight commerce is pushing the existing infrastructure to its limits. Planning a hub expansion is not easy due to the lack of space in the hub vicinity. For example, the expansion of Port of Rotterdam, Maasvlakte 2, is being “conquered” from the North Sea. However, just creating more infrastructure is not the only solution. It is also necessary, more than before, to use efficiently the physical infrastructure that is already available through a real-time approach.

From a cargo flow perspective, the different economical parties present in freight transport networks (Rodrigue et al., 2009) can be categorized into two main classes:

  • 1.

    the intermodal hub managers responsible for storing cargo and eventually supporting a transport modality switch of cargo towards the final destination;

  • 2.

    the transport operators, which offer transport capacity over different modalities between the existing intermodal hubs.

Although all parties contribute to the main objective of a transport freight network – deliver commodities at the agreed location, at the agreed time and in the agreed amount – each one has its own objectives and conflicting objectives can therefore arise (Ishfaq and Sox, 2010). Due to conflicting objectives and competition among different parties a lack of confidence exists. This is of capital influence regarding the type of information that is available for each party concerning the cargo to be moved, leading to different transport paradigms: (i) merchant haulage: in which the shipper or forwarder bears the responsibility; (ii) carrier haulage: in which the transport provider organizes the transport; and (iii) terminal haulage: in which the terminal co-determines the transport. This paper focuses on the modeling and management of interactions between the intermodal hubs of a freight transport network and its surroundings. Specifically, this paper focuses on the interaction between an intermodal hub, that provides storage capacity, and transport operators, that provide the transport capacity. The addressed problem has been named as the Modal Split Aware – Cargo Assignment Problem (MSA–CAP) which deals with the cargo assignment at the hub to the available transport capacity taking into account the transport modality used.

Transport regulators are currently interested in imposing a transport modal split on terminals motivated simultaneously by environmental and efficiency reasons (Jong et al., 2011). In the context of awarding terminals to private operators, particularly for container terminals, modal split clauses for hinterland transportation are increasingly being adopted as one of the contract stipulations used (Notteboom and Verhoeven, 2010, De Langen et al., 2012). The contract can suggest on some technical specifications and investment to be done regarding the hinterland transport infrastructure. In some cases, a specific modal split on the terminal operator to be reached by a certain year is imposed explicitly. However, the transport modal split at terminals is not a free choice between transport modalities. The final destination plays a decisive role on the transport modality choice. In the case of Port of Rotterdam in 2007, a share of 93% of the containers distribution to/from the port had as destination/source a location either in The Netherlands, Germany or Belgium (OECD, 2010). The relatively short distance motivates the use of the road modality which is leading to traffic congestion at present. The expected growth in international commerce will put more pressure to increase the efficient use of existing facilities. The Port of Rotterdam Authority focuses on increasing the shares of inland transport that is carried on inland waterways and rail transport (OECD, 2010). The modal split in 2007 was 30%, 11%, and 59% for inland shipping, rail and road, respectively, and the target for 2035 is 45%, 20%, and 35%, respectively. One practical measure for achieving this has been the signing of contracts between the Port of Rotterdam Authority and the terminal operators on the new Maasvlakte 2, with which a commitment to increase the inland waterway and rail shares at the cost of the road share has been accepted by the terminal operators. Two possibilities can be set when determining the desired transport modal split: (i) the regulator (Port Authority) can set a desired target established in the form of a clause in the concession contract; or (ii) the terminal is interested in leading the change towards a sustainable transport. In this paper, it is assumed that a desired transport modal split target is given.

For the hub manager it is important to show to all parties (neighbor hubs and transport providers or more specifically merchants, forwarders, governmental regulators in freight networks) that the hub is a reliable and trustworthy component in the freight network and contributes efficiently to the common goal while respecting regulation policies. In that sense, (1) for client satisfaction it is necessary to guarantee that all cargo arriving at the hub is assigned to the existing transport capacity such that it arrives on time at the final destination, (2) for regulator authorities it is necessary to respect environmental and/or sustainability policies (e.g., the transport modal split), and (3) for the hub manager it is important to achieve the previous goals in the most economical way such that the hub remains attractive for clients and is economically viable (Meisel et al., 2013). The European Container Terminal (ECT) at the Port of Rotterdam has initiated in 2007 the Extended Gateway Services (EGS) network (Veenstra et al., 2012). The EGS network is an effort developed by ECT to create a network composed of hinterland terminals that can cooperate with the ECT terminals located at the Port of Rotterdam in order to face the expected increase in cargo throughput (van Riessen et al., 2014).

Research on freight networks is an active topic (Groothedde et al., 2005, Caris et al., 2013, SteadieSeifi et al., 2014, Bhattacharya et al., 2014, Li et al., 2014). Freight transportation is a very competitive field, composed of transport operators, terminal operators, forwarders, shipping agents, and logistics service providers. Transport operators tend to consolidate their flows in a network of hubs and build up regular services. The planning is divided over three levels: the strategic (responsible for deciding where to locate the infrastructure), the tactical (responsible for the efficient allocation of the existing resources), and the operational (responsible for accommodating orders placed on a short notice). At the tactical level decisions are related to defining (i) the route, schedule and frequency a service should have, (ii) which type of vehicle should be used, (iii) the path goods should be moved through the network of services to meet service level, (iv) best strategy to reposition assets at the network. These questions are addressed by the Service Network Design Problem (SNDP) (Crainic, 2000, Weiberneit, 2008). Practical applications of SNDP are the express shipment delivery problem (Armacost et al., 2002), and less-than-truckload (LTL) transports (Jansen et al., 2004). Detailed reviews on SNDP can be found in Crainic and Kim (2007) and in Christiansen et al. (2007). The SNDP takes the transport operator perspective in what resembles carrier haulage. In the SNDP commodities are generally defined as the collection of goods with the same pair origin/destination and the transport operator has information about the whole network (structure and demand). Considerations regarding assets positioning in the network can be found in Andersen et al. (2009) and in Pedersen et al. (2009). Research on SNDP considering the stochastic demand can be found in Lium et al. (2009) an in Bai et al. (2014).

In case the transport provider is not the owner of the hub where freight operations are being taken care of, the transport operator does not have information about the state of the hub (e.g., congestion effects). Whenever multiple transport operators chose the same hub to integrate their service network, congestion problems can occur at the hub compromising operations. On the other hand, this is an evidence that the hub operator is a connection point between different service networks and can help transport operators to increase their service networks performance. For example, in case vehicles of different transport operators are departing the hub partially loaded, the hub operator can help transport operators in allocating cargo such that full occupation of vehicles is achieved. With this procedure, the hub operator is fostering the interaction and cooperation among different service network designs, that is to say different transport operators. The growth in freight transportation is increasing the complexity of freight transportation, where interactions between multiple service networks are present. Fig. 1 represents the case of two transport operators that designed their service networks addressed as a SNDP, sharing operations at the hub A. It can be the case that more hubs are used simultaneously by both service networks (e.g., terminal B1 and B2, for SNDP1 and SNDP2, respectively). The MSA–CAP addressed in this paper deals at decisions at a single hub with access to local information solely.

The increase growth of freight networks makes the SNDP of complex resolution for large networks and for many time periods. In this paper, the authors take the terminal operator perspective and a distributed approach with local decisions based on local information to tackle the transport modal split targets at intermodal hubs in the so-called MSA–CAP. The literature addressing the transport modal split problem is scarce. In Jong et al. (2011), a modal split model for policy and decision making is proposed. This model is oriented for policy making and lacks the details of operations at hubs. The dynamics of modal splits are investigated in Ferrari (2014) relating transport demand, transport modes and carriers behavior. In this article, we address the transport modal split problem from the perspective of the intermodal hub, from an operational perspective. First, a modeling framework to capture cargo evolution at intermodal hubs taking into account the incoming and outgoing cargo, plus an update of the remaining time until due time is proposed. The model is represented according to the state-space representation (Ogata, 1995). Cargo evolution is monitored typically on a daily basis. Second, operations management at the intermodal hub is addressed using a Model Predictive Control (MPC) approach, an online optimization-based tool that minimizes at each time step a cost function subject to constraints (Camacho and Bordons, 1995, Maciejowski, 2002). The MPC strategy is chosen to address operations management at the intermodal freight hub for the MSA–CAP due to its ability of incorporating predictions (e.g., cargo evolution at the hub, expected arrival of cargo, and available connections over time) and constraints in the optimization problem to be solved (Wang and Rivera, 2008, Alessandri et al., 2011). The intermodal hub model is used by the MPC controller together with information about the available transport capacity at the hub for assignment of cargo to services that transport the cargo towards the final destination such that cargo is delivered on time. Through the inclusion of a modal split constraint cargo assignments are guided daily such that a transport modal split target is achieved in a predefined time interval. This paper offers a unified perspective of the work regarding the modeling technique and modal split targets initially presented in Nabais et al. (2013).

In contrast to the SNDP, which takes the transport operator perspective, this paper takes the perspective of the terminal operator in the so-called MSA–CAP. In this work cargo is categorized according to fixed properties (destination, type, volume, …) which defines a commodity. For each combination of the fixed properties, a further distinction is made related to due time. The contributions of this paper are:

  • a framework for hub operator managers to achieve transport modal split targets for hinterland transportation;

  • explicit consideration of remaining time until due time as a time varying property of cargo that further distinguish commodities;

  • a state-space representation to model commodity evolution over time at terminals;

  • local decisions based on a local information exchange between actors, without compromising client privacy.

The proposed work aims to foster partnerships between hub operators and carriers, in what resembles a distributed perspective to freight transportation.

This paper has the following structure. Section 2 proposes a state-space model to describe cargo evolution over time at intermodal freight hubs. Operations management at intermodal hubs for the MSA–CAP are addressed in Section 3. The framework for cargo assignment, while choosing a sustainable transport modal split is proposed in Section 3.1. A constrained MPC heuristic to achieve a desired transport modal split through the addition of a terminal state constraint is proposed in Section 3.2. Simulation experiments for an intermodal container terminal illustrates the potential of the proposed methodologies, in Section 4. In Section 5 conclusions are drawn and future research topics are provided.

Section snippets

Modeling framework

Intermodal hubs are part of freight networks and linked through connections offered by transport providers (see Fig. 2). The amount of cargo at an intermodal freight hub changes due to cargo arrivals and cargo departures. A cargo balance at the hub is able to capture this behavior (see Fig. 3). An intermodal freight hub can for example be a container terminal or a distribution center. In intermodal hubs, decisions regarding cargo assignment to services that will deliver the cargo at a given hub

Control of intermodal hubs

This article considers the performance of an intermodal hub in terms of client satisfaction, that is to say, the capability to assign all cargo to the transport capacity available in such a way that the cargo is delivered to the final client at the agreed time and at the agreed location. From an intermodal hub perspective, the transportation problem of how to assign the existing cargo in the intermodal hub to the transport capacity at its disposal, corresponds to a Terminal Haulage approach to

Intermodal hub description

Consider the intermodal container terminal A integrated in a transport network composed of 4 intermodal container terminals as illustrated in Fig. 2. The structural layout of the container terminals has been inspired in the case study presented in Alessandri et al. (2007). According to the hinterland network nde=4, terminal A is also an available final destination. One full day (24 h) is considered as the time step for model (6), (7), (8), (9), (10), (11). So, at the beginning of the day the

Conclusions and future research

This paper focuses on the assignment of cargo at intermodal hubs to the transport capacity at its disposal such that the cargo can reach the destination on time. Cargo evolution over time at an intermodal hub is captured through a mass balance (inflows and outflows) plus a due time update and represented using a state-space representation. This model is used to make predictions about the future state of the intermodal hub and used in a model predictive controller for addressing the Modal Split

Acknowledgments

This work was supported by FCT, through IDMEC, under LAETA Pest-OE/EME/LA0022 and supported by the project PTDC/EMS-CRO/2042/2012 and the VENI project “Intelligent multi-agent control for flexible coordination of transport hubs” (project 11210) of the Dutch Technology Foundation STW, a subdivision of the Netherlands Organization for Scientific Research (NWO).

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