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

Nabais, João Lemos; Negenborn, Rudy R.; Benítez, Rafael; Botto, Miguel Ayala

doi:10.1016/j.trc.2015.09.001

Cited by 22 publications

(15 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…by considering the combination of committed and uncommitted capacity (Ypsilantis 2016, pp. 47-82;Van Riessen et al 2015a, b), real-time planning (Nabais et al 2015;Van Riessen et al 2016;Van Heeswijk et al 2016;Mes 2016, 2018), generating options (Kapetanis et al 2016;Mes and Iacob 2016), including vehicle deployment (Resat and Turkay 2019) or including vessel routing (Fazi et al 2015). Table 1 provides an overview of planning-related studies and categorises them regarding the perspective of the optimisation problem, the dimensions of flexibility and the considered decisions.…”

Section: Synchromodal Transportationmentioning

confidence: 99%

Revenue management with two fare classes in synchromodal container transportation

et al. 2020

View full text Add to dashboard Cite

The cargo fare class mix (CFCM) problem aims to find the optimal fare class mix for a given intermodal transportation network based on known client demands. It is based on a revenue management problem for aviation passengers, the fare class mix problem, but considering intermodal cargo transportation, two major differences apply. Firstly, the CFCM’s premise is that long-term commitments to customers must be provided, such that a customer has a guaranteed daily capacity. Secondly, cargo may be rescheduled or rerouted, as long as the customer’s delivery due date is met. Our goal is to balance revenue maximisation and capacity utilisation by optimally combining two delivery service levels. Therefore, the optimisation problem is to select fare class limits at a tactical level up to which transportation demand will be accepted on a daily basis at the operational level. Any accepted demand that does not fit on the available network capacity during operation, must be transported by truck at increased expenses for the network operator. In this paper, we propose a faster method than the previously proposed solution method for a single corridor network and we provide proofs for the optimality of the result. Using this, we extend the problem to an intermodal network of multiple corridors. We provide numerical results for different settings, in which we compare the baseline of individual corridor optimums with the result of using rerouting. Finally, we apply the methods in a case study for an intermodal transportation network in North-West Europe.

show abstract

Section: Synchromodal Transportationmentioning

confidence: 99%

Revenue management with two fare classes in synchromodal container transportation

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, these approaches have computational limitations for large and complex time-evolving problem instances [16], which are characteristics common to synchromodality [13]. To overcome these limitations, additional designs, such as decomposition algorithms [5], receding horizons [6], and model predictive control [10], are necessary. These additional designs are less suitable for including probabilistic information in the decisions, which may explain why most DSND studies assume deterministic demand [15] even though the need to incorporate it has been recognized [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This emphasis is necessary at early iterations, where initial conditions might bias the approximation and the result of the ADP approach. The weights θ n a,t , for all a ∈ A, are updated each iteration n using the observed error (i.e., difference between the next-stage estimate from the previous iteration V n−1 t−1 S x,n t−1 and the current estimate v n t ), the value of all basis functions φ a (S x,n t ), the optimization matrix H n , and the previous weights θ n−1 a,t , as seen in (10). For a comprehensive explanation on the NLS method, we refer to [12].…”

Section: Solution Approachmentioning

confidence: 99%

Service and Transfer Selection for Freights in a Synchromodal Network

Rivera¹,

Mes²

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We study the planning problem of selecting services and transfers in a synchromodal network to transport freights with different characteristics, over a multi-period horizon. The evolution of the network over time is determined by the decisions made, the schedule of the services, and the new freights that arrive each period. Although freights become known gradually over time, the planner has probabilistic knowledge about their arrival. Using this knowledge, the planner balances current and future costs at each period, with the objective of minimizing the total costs over the entire horizon. To model this stochastic and multi-period tradeoff, we propose a Markov Decision Process (MDP) model. To overcome the computational complexity of solving the MDP, we propose an Approximate Dynamic Programming (ADP) approach. Using different problem settings, we show that our look-ahead approach has significant benefits compared to a benchmark heuristic.

show abstract

“…Other application areas for which cybersecurity needs to be considered are electric power systems,() advanced metering infrastructure, protection systems, Internet home users, and logistics. ()…”

Section: Introductionmentioning

confidence: 99%

“…Other application areas for which cybersecurity needs to be considered are electric power systems, [12][13][14] advanced metering infrastructure, 15 protection systems, 16 Internet home users, 17 and logistics. [18][19][20] This is a relevant topic whose impact is clear, for example, in the Internet, where it is common that vulnerabilities are exploited with several purposes, eg, to disrupt the normal operation of a server and to take the control of remote machines. The consequences of these attacks range from the denial of service of servers to the theft and destruction of sensible data.…”

mentioning

confidence: 99%

Vulnerabilities in Lagrange‐based distributed model predictive control

Velarde

Maestre

Ishii

et al. 2017

Optim Control Appl Methods

View full text Add to dashboard Cite

Summary In this paper, we present an analysis of the vulnerability of a distributed model predictive control scheme. A distributed system can be easily attacked by a malicious agent that modifies the reliable information exchange. We consider different types of so‐called insider attacks. In particular, we analyze a controller that is part of the control architecture that sends false information to others to manipulate costs for its own advantage. We propose a mechanism to protect or, at least, relieve the consequences of the attack in a typical distributed model predictive control negotiation procedure. More specifically, a consensus approach that dismisses the extreme control actions is presented as a way to protect the distributed system from potential threats. Two applications are considered as case studies, ie, an academic example involving the control of a distributed system with a single coupled input and a distributed local electricity grid of households. The results are presented via simulations to illustrate both the consequences of the attacks and the defense mechanisms.

show abstract

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

Cited by 22 publications

References 30 publications

Revenue management with two fare classes in synchromodal container transportation

Revenue management with two fare classes in synchromodal container transportation

Service and Transfer Selection for Freights in a Synchromodal Network

Vulnerabilities in Lagrange‐based distributed model predictive control

Contact Info

Product

Resources

About