Nonlinear Model Predictive Control for Resource Allocation in the Management of Intermodal Container Terminals

Alessandri, A.; Cervellera, Cristiano; Cuneo, M.; Gaggero, Mauro

doi:10.1007/978-3-642-01094-1_16

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…At the tactical level, the decisions that can be changed during operations are tuned by using the new, shortterm, and more precise predictions of the demands that become available. This task is accomplished by using model predictive control (MPC), which allows one to take advantage of the information available on the future behavior of the system [2]- [6]. In our context, as we shall see, there exists the need of dealing with both real and integer variables and hence of addressing mixed-integer optimal control problems.…”

Section: Introductionmentioning

confidence: 99%

Integer tree-based search and mixed-integer optimal control of distribution chains

Alessandri

Gaggero

Tonelli

2011

IEEE Conference on Decision and Control and European Control Conference

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The use of integer tree-based search and mixedinteger programming is investigated for the purpose of control of multi-item multi-echelon distribution chains. A discrete-time model is considered to describe the dynamics of a generic distribution chain. The decisions on the amounts of goods to transfer are made by referring to a performance index that accounts for transportation, storage, and backlog costs at two levels, i.e., strategic and tactical. As to the strategic level, a worst-case stock replenishment policy is adopted to exploit the uncertain information available on long-term predictions of the customers' demand. The solution of such a problem is obtained by using a top-down tree-based algorithm to select policy parameters such as the delivery cycle times of goods. At the tactical level, the on-line decisions on the transportation of goods are taken basing on model predictive control, which allows one to take into account recent, reliable, short-term predictions of the demand. The tactical optimal decisions are obtained by solving mixed-integer programming problems with fewer variables as compared with the strategic setting. Simulation results are presented to show the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Integer tree-based search and mixed-integer optimal control of distribution chains

Alessandri

Gaggero

Tonelli

2011

IEEE Conference on Decision and Control and European Control Conference

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Survey of approaches for integrated control of intermodal container terminals

Zijverden

Negenborn

2012

Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control

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An approach to exploit non-optimized data for efficient control of unknown systems through neural and kernel models

Cervellera

Gaggero

Macciò

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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In this paper, efficient real time control strategies are devised for systems with unknown state equation, based only on a set of data inherited from non-optimized, possibly inefficient, operation of the system, in the case in which experimenting online with the latter is impossible or costly. Neural networks and kernel smoothing models are employed as architectures for learning the system dynamics. The former require an offline training phase to learn the state equation, whereas the latter exploit the available data in a direct fashion, thus making the proposed approach directly applicable online and able to exploit new available data without the need of an offline training. Convergence properties of the proposed algorithm for generating the control strategies are provided under suitable hypotheses. Simulation results on classic benchmark systems are reported for performance evaluation, also through a comparison with the SARSA reinforcement learning algorithm.

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Nonlinear Model Predictive Control for Resource Allocation in the Management of Intermodal Container Terminals

Cited by 5 publications

References 7 publications

Integer tree-based search and mixed-integer optimal control of distribution chains

Integer tree-based search and mixed-integer optimal control of distribution chains

Survey of approaches for integrated control of intermodal container terminals

An approach to exploit non-optimized data for efficient control of unknown systems through neural and kernel models

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