Model predictive control for simultaneous planning of container and vehicle routes

Larsen, Rie B.; Atasoy, Bilge; Negenborn, Rudy R.

doi:10.1016/j.ejcon.2020.06.003

Cited by 24 publications

(18 citation statements)

References 32 publications

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“…Truck routing is typically decided at the operational level based on pick up and delivery locations and times of the goods [12]. In [6] and [7] we demonstrated the negative impact of planning first container routes and then truck routes compared to planning them simultaneously in a synchromodal network. The results of [13] shows the same on a network with only one origin of the demand.…”

Section: Introductionmentioning

confidence: 93%

“…The truck operator is responsible for choosing which modes and routes each container is transported by and for deciding the truck routes. The model used to describe this simultaneous planning problem is a simplification of on the method presented by us in [7]. The full model can be used with DL, but as it ads complexity to the description, the simplified model is used here.…”

Section: Truck Operatormentioning

confidence: 99%

“…Model Predictive Control (MPC) provides a framework for combining predictions of future events with real time decision making. MPC has previously been used to route containers in several cases, e.g., [11], [9] and [7].…”

Section: Introductionmentioning

confidence: 99%

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Learning-Based Co-planning for Improved Container, Barge and Truck Routing

Larsen

Atasoy

Negenborn

2020

Lecture Notes in Computer Science

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When barges are scheduled before the demand for container transport is known, the scheduled departures may match poorly with the realised demands' due dates and with the truck utilization. Synchromodal transport enables simultaneous planning of container, truck and barge routes at the operational level. Often these decisions are taken by multiple stakeholders who want cooperation, but are reluctant to share information. We propose a novel co-planning framework, called departure learning, where a barge operator learns what departure times perform better based on indications from the other operator. The framework is suitable for real time implementation and thus handles uncertainties by replanning. Simulated experiment results show that co-planning has a big impact on vehicle utilization and that departure learning is a promising tool for co-planning.

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Section: Introductionmentioning

confidence: 93%

Section: Truck Operatormentioning

confidence: 99%

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Learning-Based Co-planning for Improved Container, Barge and Truck Routing

Larsen

Atasoy

Negenborn

2020

Lecture Notes in Computer Science

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“…They aim at minimizing the costs and are solved employing shortest path algorithms. This cost minimization can also be used as a control rule within a freight transport network simulation [18], [19]. But the most prevalent model to estimate the mode choice in freight transport is the Multinomial Logit (MNL), or one of its variations [8].…”

Section: A Aggregate Mode Choice Modelsmentioning

confidence: 99%

A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data

Nicolet

Negenborn

Atasoy

2022

IEEE Open J. Intell. Transp. Syst.

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Understanding the modal split in freight transportation is a key factor for the successful implementation of innovations. Mode choice models should then be as representative of reality as possible. The use of disaggregate shipment data can help to achieve it. However, shipment data are often unavailable due to confidentiality issues. As a result, numerous models using only aggregate data have been developed, but their capacity to capture heterogeneity in preferences remains limited. In this paper, we propose a Weighted Logit Mixture model to estimate heterogeneous mode choice preferences of shippers directly from aggregate data. The proposed Weighted Logit Mixture is applied to a case study along the European Rhine-Alpine corridor and allows to estimate the probability distribution of the cost sensitivity among the population. The estimation results show that there exists a substantial variation of the cost sensitivity regarding intermodal transport. The proposed methodology is also compared to a state-of-the-art Weighted Logit model to assess its potential. This reveals that the proposed Weighted Logit Mixture exhibits at least a similar predictive power to the benchmark while achieving a better description of the population's preferences that enables policy-makers to take better informed decisions and appropriate actions.

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“…MPC method also has many applications in intermodal transport. In the research of Larsen et al [37], a model predictive controller was presented to solve an integration problem about transport mode combination selection and truck route planning in a synchromodal transport network. e research of Nabais et al [38] presents a hierarchical model predictive control framework for addressing flow assignments in intermodal container terminals.…”

Section: Model Predictive Controlmentioning

confidence: 99%

A Cooperative Intermodal Transportation Network Flow Control Method Based on Model Predictive Control

Huang

Zhou

Xiong

et al. 2021

Journal of Advanced Transportation

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With the development of information technology, intermodal transport research pays more attention to dynamic optimization and multi-role cooperation. The core issue of this paper was to realize container routing with dynamic adjustment, real-time optimization, and multi-role cooperation characteristics in the intermodal transport network. This paper first introduces the Intermodal Transport Cooperation Protocol (ITCP) that describes the operation and analysis of intermodal transport systems with the concept of encapsulation and layering. Then, a new network flow control method was built based on Model Predictive Control (MPC) in the ITCP framework. The method takes real-time information from all ITCP layers as input and generates flow control decisions for containers. To evaluate the method’s effectiveness, a discrete event simulation experiment is applied. The results show that the proposed method outperforms the all-or-nothing method in scenarios with high freight volume, which means the method proposed in this paper can effectively balance the network transport load and reduce network operating costs. The research of this paper may throw some new light on intermodal transport research from the perspectives of digitization, multi-role cooperation, dynamic optimization, and system standardization.

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Model predictive control for simultaneous planning of container and vehicle routes

Cited by 24 publications

References 32 publications

Learning-Based Co-planning for Improved Container, Barge and Truck Routing

Learning-Based Co-planning for Improved Container, Barge and Truck Routing

A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data

A Cooperative Intermodal Transportation Network Flow Control Method Based on Model Predictive Control

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