作者:
Jaulin, LucOSM
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This paper deals with the simultaneous localization and mapping problem (SLAM) for a robot. The robot has to build a map of its environment while localizing itself using a partially built map. It is assumed that (i) t...
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This paper deals with the simultaneous localization and mapping problem (SLAM) for a robot. The robot has to build a map of its environment while localizing itself using a partially built map. It is assumed that (i) the map is made of point landmarks, (ii) the landmarks are indistinguishable, (iii) the only exteroceptive measurements correspond to the distance between the robot and the landmarks. This paper shows that SLAM can be cast into a constraint network the variables of which being trajectories, digraphs and subsets of Then, we show how constraint propagation can be extended to deal with such generalized constraint networks. As a result, due to the redundancy of measurements of SLAM, we demonstrate that a constraint-based approach provides an efficient backtrack-free algorithm able to solve our SLAM problem in a guaranteed way.
China Railway is undertaking massive construction and development projects. A reasonable and resource-leveled schedule that allows for adjustments for unforeseen circumstances during construction is critical for manag...
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China Railway is undertaking massive construction and development projects. A reasonable and resource-leveled schedule that allows for adjustments for unforeseen circumstances during construction is critical for managing railway construction projects. Currently, most construction projects use traditional network planning methods or the Gantt schedule for project management. However, these methods have limited applicability to railway construction projects, which are typically linear. This study uses the linear scheduling method and constraint programming techniques for solving schedule control problems faced during railroad construction. The proposal comprises a schedule control model, scheduling model, and schedule control system;the scheduling model is central to the schedule control model. Characteristics such as high flexibility and practicality facilitate multi-objective optimization during scheduling and modification of the linear schedule. The proposed model and algorithm were validated by comparing results with actual data from a highway construction project and the Urumqi-Dzungaria railway construction project. (C) 2013 Elsevier B.V. All rights reserved.
Nowadays, many massive factories are forced to distribute their products in several manufacturing units. This issue has caused the emergence of a novel category of problems called distributed production scheduling, wh...
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Nowadays, many massive factories are forced to distribute their products in several manufacturing units. This issue has caused the emergence of a novel category of problems called distributed production scheduling, which is vital in today's growing world. In this paper, the distributed production scheduling problem by considering network configuration with two echelons is addressed. The first and second echelon factories have different job configurations and have a hybrid flow shop and a flexible job shop environment, respectively. For this problem, A bi-objective mixed integer linear programming (MILP) model is presented to minimize the maximum completion time of jobs and transportation costs between the selected factories in two echelons, respectively. Consequently, the epsilon constraint method is used to deal with this bi-objective model. In addition, since distributed scheduling problems are classified as NP-Hard problems, it is very challenging to solve them for largesized instances. For this reason, a constraint programming model (CP) is also proposed. To evaluate the performance of the proposed MILP model and CP model, a total of 180 numerical instances are randomly generated in small, medium, and large sizes. The obtained results demonstrate the significant ability of the constraint programming approach in solving complex distributed scheduling problems even for large-sized instances with 30 jobs, 10 stages/operations for each job, 6 machines for each stage/operation, and 4 factories at each echelon in a reasonable time and proof that the CP model can outperform the MILP model in this problem.
Context: Testing highly-configurable software systems is challenging due to a large number of test configurations that have to be carefully selected in order to reduce the testing effort as much as possible, while mai...
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Context: Testing highly-configurable software systems is challenging due to a large number of test configurations that have to be carefully selected in order to reduce the testing effort as much as possible, while maintaining high software quality. Finding the smallest set of valid test configurations that ensure sufficient coverage of the system's feature interactions is thus the objective of validation engineers, especially when the execution of test configurations is costly or time-consuming. However, this problem is NP-hard in general and approximation algorithms have often been used to address it in practice. Objective: In this paper, we explore an alternative exact approach based on constraint programming that will allow engineers to increase the effectiveness of configuration testing while keeping the number of configurations as low as possible. Method: Our approach consists in using a (time-aware) minimization algorithm based on constraint programming. Given the amount of time, our solution generates a minimized set of valid test configurations that ensure coverage of all pairs of feature values (a.k.a. pairwise coverage). The approach has been implemented in a tool called PACOGEN. Results: PACOGEN was evaluated on 224 feature models in comparison with the two existing tools that are based on a greedy algorithm. For 79% of 224 feature models, PACOGEN generated up to 60% fewer test configurations than the competitor tools. We further evaluated PACOGEN in the case study of an industrial video conferencing product line with a feature model of 169 features, and found 60% fewer configurations compared with the manual approach followed by test engineers. The set of test configurations generated by PACOGEN decreased the time required by test engineers in manual test configuration by 85%, increasing the feature-pairs coverage at the same time. Conclusion: Our experimental evaluation concluded that optimal time-aware minimization of pairwise-covering test configurati
We consider the problem of finding a cutset in a directed graph G = (V, E), i.e., a set of vertices that cuts all cycles in G. Finding a cutset of minimum cardinality is NP-hard. There exist several approximate and ex...
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We consider the problem of finding a cutset in a directed graph G = (V, E), i.e., a set of vertices that cuts all cycles in G. Finding a cutset of minimum cardinality is NP-hard. There exist several approximate and exact algorithms, most of them using graph reduction techniques. In this paper, we propose a constraint programming approach to cutset problems and design a global constraint for computing cutsets. This cutset constraint is a global constraint over boolean variables associated to the vertices of a given graph and states that the subgraph restricted to the vertices having their boolean variable set to true is acyclic. We propose a filtering algorithm based on graph contraction operations and inference of simple boolean constraints, that has a linear time complexity in O (vertical bar E vertical bar +/- vertical bar V vertical bar). We discuss search heuristics based on graph properties provided by the cutset constraint, and show the efficiency of the cutset constraint on benchmarks of the literature for pure minimum cutset problems, and on an application to log-based reconciliation problems where the global cutset constraint is mixed with other boolean constraints. (c) 2005 Published by Elsevier Ltd.
This paper introduces a hybrid algorithm for the dynamic dial-a-ride problem in which service requests arrive in real time. The hybrid algorithm combines an exact constraint programming algorithm and a tabu search heu...
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This paper introduces a hybrid algorithm for the dynamic dial-a-ride problem in which service requests arrive in real time. The hybrid algorithm combines an exact constraint programming algorithm and a tabu search heuristic. An important component of the tabu search heuristic consists of three scheduling procedures that are executed sequentially. Experiments show that the constraint programming algorithm is sometimes able to accept or reject incoming requests, and that the hybrid method outperforms each of the two algorithms when they are executed alone.
The APS (Advanced Planning and Scheduling) systems are widely used by companies;however, the traditional APS systems cannot deal with problems whose the due date is a strong restriction. The problem derives from the w...
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The APS (Advanced Planning and Scheduling) systems are widely used by companies;however, the traditional APS systems cannot deal with problems whose the due date is a strong restriction. The problem derives from the way companies use their scheduling heuristics. This paper addresses this problem by using the concept of time windows with the constraint programming mechanism. A procedure is shown to generate the time windows and how they can be used for the APS systems. The APS heuristic approach that uses the concepts of time windows with constraint programming is introduced to solve problems for which the due date is a strong restriction. These heuristics, with tasks allocation either at the beginning or at the end of the task time window, eliminate the need for a priority scheme. To illustrate the advantage of the proposal, some examples are presented. (C) 2014 Elsevier Ltd. All rights reserved.
constraint programming (CP) has proven to be an effective platform for constraint based sequence mining. Previous work has focused on standard frequent sequence mining, as well as frequent sequence mining with a maxim...
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constraint programming (CP) has proven to be an effective platform for constraint based sequence mining. Previous work has focused on standard frequent sequence mining, as well as frequent sequence mining with a maximum 'gap' between two matching events in a sequence. The main challenge in the latter is that this constraint can not be imposed independently of the omnipresent frequency constraint. Indeed, the gap constraint changes whether a subsequence is included in a sequence, and hence its frequency. In this work, we go beyond that and investigate the integration of timed events and constraining the minimum/maximum gap as well as minimum/maximum span. The latter constrains the allowed time between the first and last matching event of a pattern. We show how the three are interrelated, and what the required changes to the frequency constraint are. Key in our approach is the concept of an extension window defined by gap/span and we develop techniques to avoid scanning the sequences needlessly, as well as using a backtracking-aware data structure. Experiments demonstrate that the proposed approach outperforms both specialized and CP-based approaches in almost all cases and that the advantage increases as the minimum frequency threshold decreases. This paper is an extension of the original manuscript presented at CPAIOR'17 [5].
In this study we consider the mapping of the main characteristics, i.e., the structural properties, of a classical job shop problem onto well-known combinatorial techniques, i.e., positional sets, disjunctive graphs, ...
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In this study we consider the mapping of the main characteristics, i.e., the structural properties, of a classical job shop problem onto well-known combinatorial techniques, i.e., positional sets, disjunctive graphs, and linear orderings. We procedurally formulate three different models in terms of mixed integer programming (MIP) and constraint programming (CP) paradigms. We utilize the properties of positional sets and disjunctive graphs to construct tight MIP formulations in an efficient manner. In addition, the properties are retrieved by the polyhedral structures of the linear ordering and they are defined on a disjunctive graph to facilitate the formulation of the CP model and to reduce the number of dominant variables. The proposed models are solved and their computational performance levels are compared with well-known benchmarks in the job shop research area using IBM ILog Cplex software. We provide a more explicit analogy of the applicability of the proposed models based on parameters such as time efficiency, thereby producing strong bounds, as well as the expressive power of the modeling process. We also discuss the results to determine the best formulation, which is computationally efficient and structurally parsimonious with respect to different criteria. (C) 2014 Elsevier Inc. All rights reserved.
We introduce five constraint models for the 3-dimensional stable matching problem with cyclic preferences and study their relative performances under diverse configurations. While several constraint models have been p...
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We introduce five constraint models for the 3-dimensional stable matching problem with cyclic preferences and study their relative performances under diverse configurations. While several constraint models have been proposed for variants of the two-dimensional stable matching problem, we are the first to present constraint models for a higher number of dimensions. We show for all five models how to capture two different stability notions, namely weak and strong stability. Additionally, we translate some well-known fairness notions (i.e. sex-equal, minimum regret, egalitarian) into 3-dimensional matchings, and present how to capture them in each model. Our tests cover dozens of problem sizes and four different instance generation methods. We explore two levels of commitment in our models: one where we have an individual variable for each agent (individual commitment), and another one where the determination of a variable involves pairing the three agents at once (group commitment). Our experiments show that the suitability of the commitment depends on the type of stability we are dealing with, and that the choice of the search heuristic can help improve performance. Our experiments not only brought light to the role that learning and restarts can play in solving this kind of problems, but also allowed us to discover that in some cases combining strong and weak stability leads to reduced runtimes for the latter.
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