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.
Scheduling and dispatching tools for high-performance computing (HPC) machines have the key role of mapping jobs to the available resources, trying to maximize performance and quality-of-service (QoS). Allocation and ...
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Scheduling and dispatching tools for high-performance computing (HPC) machines have the key role of mapping jobs to the available resources, trying to maximize performance and quality-of-service (QoS). Allocation and Scheduling in the general case are well-known NP-hard problems, forcing commercial schedulers to adopt greedy approaches to improve performance and QoS. Search-based approaches featuring the exploration of the solution space have seldom been employed in this setting, but mostly applied in off-line scenarios. In this paper, we present the first search-based approach to job allocation and scheduling for HPC machines, working in a production environment. The scheduler is based on constraint programming, an effective programming technique for optimization problems. The resulting scheduler is flexible, as it can be easily customized for dealing with heterogeneous resources, user-defined constraints and different metrics. We evaluate our solution both on virtual machines using synthetic workloads, and on the Eurora HPC with production workloads. Tests on a wide range of operating conditions show significant improvements in waitings and QoS in mid-tier HPC machines w.r.t state-of-the-art commercial rule-based dispatchers. Furthermore, we analyze the conditions under which our approach outperforms commercial approaches, to create a portfolio of scheduling algorithms that ensures robustness, flexibility and scalability.
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.
An edge-coloring of a graph G = (V, E) is a function c that assigns an integer c(e) (called color) in {0,1,2, ... } to every edge e e E so that adjacent edges are assigned different colors. An edge-coloring is compact...
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An edge-coloring of a graph G = (V, E) is a function c that assigns an integer c(e) (called color) in {0,1,2, ... } to every edge e e E so that adjacent edges are assigned different colors. An edge-coloring is compact if the colors of the edges incident to every vertex form a set of consecutive integers. The deficiency problem is to determine the minimum number of pendant edges that must be added to a graph such that the resulting graph admits a compact edge-coloring. We propose and analyze three integer programming models and one constraint programming model for the deficiency problem. (C) 2015 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.
During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obta...
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During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
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.
Mining web access patterns consists in extracting knowledge from server log files. This problem is represented as a sequential pattern mining problem (SPM) which allows to extract patterns which are sequences of acces...
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Mining web access patterns consists in extracting knowledge from server log files. This problem is represented as a sequential pattern mining problem (SPM) which allows to extract patterns which are sequences of accesses that occur frequently in the web log file. There are in the literature many efficient algorithms to solve SMP (e.g., GSP, SPADE, PrefixSpan, WAP-tree, LAPIN, PLWAP). Despite the effectiveness of these methods, they do not allow to express and to handle new constraints defined on patterns, new implementations are required. Recently, many approaches based on constraint programming (CP) was proposed to solve SPM in a declarative and generic way. Since no CP-based approach was applied for mining web access patterns, the authors introduce in this paper an efficient CP-based approach for solving the web log mining problem. They bring back the problem of web log mining to SPM within a CP environment which enables to handle various constraints. Experimental results on non-trivial web log mining problems show the effectiveness of the authors' CP-based mining approach.
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.
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