During the past decade, inland vessels have gained importance in container transport because of their reliability, low enviromnental impact, and major capacity for increased exploitation. Although inland vessels are c...
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During the past decade, inland vessels have gained importance in container transport because of their reliability, low enviromnental impact, and major capacity for increased exploitation. Although inland vessels are crucial in container transport between terminals in the port and the hinterland, in a large seaport like the one in Rotterdam, Netherlands, only 62% of the inland vessels leave the port on time. The other vessels have to stay in the port area for a longer time than planned. This situation leads to uncertainty in waiting times of vessels at terminals and low utilization of terminal quay resources. A two-phase approach is proposed that integrates mixed-integer programming (MIP) and constraint programming (CP) to solve the problem by generating optimal rotation plans for inland vessels. In the first phase, the single-vessel optimization problem is formulated on the basis of MIP and solved with state-of-the-art MIP solvers. In the second phase, the multiple-vessel coordination problem is formulated on the basis of CP, and a large neighborhood search based heuristic is proposed to solve the problem. Commercial CP solvers are also used for comparison. Simulation results show that the proposed large neighborhood search based heuristic outperforms the commercial CP solver with regard to both the solution quality and the computation time. Moreover, simulation results with respect to departure time of the last vessel, total sojourn time, and waiting time show significant improvement with earlier departure times and shorter sojourn times and waiting times.
This paper discusses a new method to perform propagation over a (two-layer, feed-forward) Neural Network embedded in a constraint programming model. The method is meant to be employed in Empirical Model Learning, a te...
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This paper discusses a new method to perform propagation over a (two-layer, feed-forward) Neural Network embedded in a constraint programming model. The method is meant to be employed in Empirical Model Learning, a technique designed to enable optimal decision making over systems that cannot be modeled via conventional declarative means. The key step in Empirical Model Learning is to embed a Machine Learning model into a combinatorial model. It has been showed that Neural Networks can be embedded in a constraint programming model by simply encoding each neuron as a global constraint, which is then propagated individually. Unfortunately, this decomposition approach may lead to weak bounds. To overcome such limitation, we propose a new network-level propagator based on a non-linear Lagrangian relaxation that is solved with a subgradient algorithm. The method proved capable of dramatically reducing the search tree size on a thermal-aware dispatching problem on multicore CPUs. The overhead for optimizing the Lagrangian multipliers is kept within a reasonable level via a few simple techniques. This paper is an extended version of [27], featuring an improved structure, a new filtering technique for the network inputs, a set of overhead reduction techniques, and a thorough experimentation.
This contribution introduces an efficient constraint programming (CP) model that copes with largescale scheduling problems in multiproduct multistage batch plants. It addresses several features found in industrial env...
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This contribution introduces an efficient constraint programming (CP) model that copes with largescale scheduling problems in multiproduct multistage batch plants. It addresses several features found in industrial environments, such as topology constraints, forbidden product-equipment assignments, sequence-dependent changeover tasks, dissimilar parallel units at each stage, limiting renewable resources and multiple-batch orders, among other relevant plant characteristics. Moreover, the contribution deals with various inter-stage storage and operational policies. In addition, multiple-batch orders can be handled by defining a campaign operating mode, and lower and upper bounds on the number of batches per campaign can be fixed. The proposed model has been extensively tested by means of several case studies having various problem sizes and characteristics. The results have shown that the model can efficiently solve medium and large-scale problems with multiple constraining features. The approach has also rendered good quality solutions for problems that consider multiple-batch orders under a campaign-based operational policy. (C) 2016 Elsevier Ltd. All rights reserved.
constraint satisfaction problem(CSP) can be widely applied in many areas. This paper investigates the maximum restricted path consistency algorithm. There is a large quantity of useless checks in the process of search...
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ISBN:
(纸本)9781510845541
constraint satisfaction problem(CSP) can be widely applied in many areas. This paper investigates the maximum restricted path consistency algorithm. There is a large quantity of useless checks in the process of searching for a PC-support with the most popular algorithm lmaxRPC3 rm. Since lmaxRPC3 rm has to examine the whole domain of a variable to ascertain whether a PC-support exists. The efficiency of the search can be improved by eliminating such useless checks. Firstly, this paper analyses the features which accounts for the existence of these ineffective checks. And then, this paper discusses some methods of solving these problems. Afterwards, a new data structure is put forward to strengthen residual supports and weaken the use of multidirectionality to narrow the range of search. A new algorithm, lmaxRPCls, which exploits the results above is proposed and it is proved that lmaxRPCls is correct and complete. It is also proved that the time complexity of this new algorithm is better than that of lmaxRPC3 rm. Experimental results show that lmaxRPCls performs better in most benchmark instances and it can improve the performance by 65% in the best case.
Systems of mobile Systems are intermittently connected networks that use store-carry-forward routing for data transfers. Independent systems collaborate and exchange data to achieve a common goal. Data transfers are o...
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Systems of mobile Systems are intermittently connected networks that use store-carry-forward routing for data transfers. Independent systems collaborate and exchange data to achieve a common goal. Data transfers are only possible between systems that are close enough to each other, when a so-called contact occurs. During a contact, a sending system can transmit to a receiving system a fixed amount of data held in its interna then assume it holds at a til buffer. We assume that the trajectories of component systems are predictable, and consequently that a sequence of contacts may be considered. This dissemination problem is aimed at finding a transfer plan such that a set of data can be transferred from a given subset of source systems to all the recipient systems. In this paper, we propose an original constraint-programming -based algorithm for solving this problem. Computational results show that this approach is an improvement on the integer-linear-programming-based approach that we proposed in a previous paper. (C) 2016 Elsevier Ltd. All rights reserved.
We present a declarative framework for the compilation of constraint logic programs into variable-free relational theories which are then executed by rewriting. This translation provides an algebraic formulation of th...
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We present a declarative framework for the compilation of constraint logic programs into variable-free relational theories which are then executed by rewriting. This translation provides an algebraic formulation of the abstract syntax of logic programs. Logic variables, unification, and renaming apart are completely elided in favor of manipulation of variable-free relation expressions. In this setting, term rewriting not only provides an operational semantics for logic programs, but also a simple framework for reasoning about program execution. We prove the translation sound, and the rewriting system complete with respect to traditional SLD semantics.
We study the application of constraint programming (CP) to the planning and scheduling of multiple social robots interacting with residents in a retirement home. The robots autonomously organize and facilitate group a...
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ISBN:
(纸本)9783319449531;9783319449524
We study the application of constraint programming (CP) to the planning and scheduling of multiple social robots interacting with residents in a retirement home. The robots autonomously organize and facilitate group and individual activities among residents. The application is a multi-robot task allocation and scheduling problem in which task plans must be determined that integrate with resident schedules. The problem involves reasoning about disjoint time windows, inter-schedule task dependencies, user and robot travel times, as well as robot energy levels. We propose mixed-integer programming (MIP) and CP approaches for this problem and investigate methods for improving our initial CP approach using symmetry breaking, variable ordering heuristics, and large neighbourhood search. We introduce a relaxed CP model for determining provable bounds on solution quality. Experiments indicate substantial superiority of the initial CP approach over MIP, and subsequent significant improvements in the CP approach through our manipulations. This work is one of the few, of which we are aware, that applies CP to multi-robot task allocation and scheduling problems. Our results demonstrate the promise of CP scheduling technology as a general optimization infrastructure for such problems.
Parallel constraint programming (CP) solvers typically split the search space in disjoint subspaces, and run solvers independently on these. This may induce significant overhead when solving optimization problems. Par...
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ISBN:
(数字)9783319339542
ISBN:
(纸本)9783319339542;9783319339535
Parallel constraint programming (CP) solvers typically split the search space in disjoint subspaces, and run solvers independently on these. This may induce significant overhead when solving optimization problems. Parallel Boolean Satisfiability (SAT) solvers typically run a portfolio of solvers, all solving the same problem but sharing some limited learnt clause information. In this paper we consider parallelizing a lazy clause generation (LCG) constraint programming solver, which is a constraint programming solver with learning. Since it is both a kind of CP solver and a kind of SAT solver it is not clear which approach to parallelization is likely to be most effective. We give examples of very different kinds of optimization problems we wish to parallelize and show that a hybrid approach to parallelization can provide a robust and high performing parallel LCG solver.
This paper concerns guarantees on system performance through Service Level Agreement (SLA) compliance and focuses on devising energy aware resource management techniques based on Dynamic Voltage and Frequency Scaling ...
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ISBN:
(纸本)9781450341479
This paper concerns guarantees on system performance through Service Level Agreement (SLA) compliance and focuses on devising energy aware resource management techniques based on Dynamic Voltage and Frequency Scaling (DVFS) used by resource management middleware in clouds that handle MapReduce jobs. This research formulates the resource management problem as an optimization problem using constraint programming (CP). Experimental results presented in the paper demonstrate the effectiveness of the technique.
The stockyard management problem in a cargo assembly terminal mainly focuses on finding an efficient allocation of limited resources such as stockyard space, stacking and reclaiming machines to stockpiles such that th...
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ISBN:
(纸本)9783319459394;9783319459400
The stockyard management problem in a cargo assembly terminal mainly focuses on finding an efficient allocation of limited resources such as stockyard space, stacking and reclaiming machines to stockpiles such that the waiting time of a vessel fleet at port can be minimized. This highly complex problem involves scheduling with resource constraints in a dynamic environment and is a typical NP-hard problem in general. In this study, we present a constraint programming (CP) based approach to address the problem with utilizing the strength of CP in its flexibility in formulating various complex and nonlinear constraints for industrial applications. In addition, we explore the ability of the CP solver with trying different combinations of constraint propagation and constructive search strategies and report our best findings from an extensive computational study.
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