This paper describes a new approach on optimization of regular constraint satisfaction problems (rCSPs) using an auxiliary constraint satisfaction optimization problem (CSOP) that detects areas with a potentially high...
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ISBN:
(纸本)9789897583506
This paper describes a new approach on optimization of regular constraint satisfaction problems (rCSPs) using an auxiliary constraint satisfaction optimization problem (CSOP) that detects areas with a potentially high number of conflicts. The purpose of this approach is to remove conflicts by the combination of regular constraints with intersection and concatenation of their underlying deterministic finite automatons (DFAs). This, eventually, often allows to significantly speed-up the solution process of the original rCSP.
In this paper we will present a multi-stage scheduling approach to generate robust schedules for a challenging aspect of semiconductor manufacturing called time-link areas. A time-link is a technologically induced tim...
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ISBN:
(纸本)9781538676011
In this paper we will present a multi-stage scheduling approach to generate robust schedules for a challenging aspect of semiconductor manufacturing called time-link areas. A time-link is a technologically induced time constraint between one or more consecutive process steps requiring the process steps to be executed within a predefined time window. Time-links are often introduced to control contamination or unwanted oxidation. Violating time-link constraints may lead to extra effort due to additional rework steps and may have negative yield impact or may even, result in scrap.
We consider a flexible job shop scheduling problem that incorporates machine operators and aims at makespan minimization. In a detailed overview of the related literature, we reveal the fact that the research in this ...
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We consider a flexible job shop scheduling problem that incorporates machine operators and aims at makespan minimization. In a detailed overview of the related literature, we reveal the fact that the research in this field is mainly concerned with (meta-)heuristic approaches. Only few papers consider exact approaches. In order to promote the use of exact approaches and in order to facilitate the evaluation of the performance of heuristic approaches, we present two mathematical models, a mixed-integer programming model and a constraint programming model, that are analyzed and compared with a state-of-the-art heuristic in computational tests with a standard solver. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Neighborhood operators play a crucial role in defining effective Local Search solvers, allowing one to limit the explored search space and prune the fitness landscape. Still, there is no accepted formal representation...
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ISBN:
(纸本)9781450367486
Neighborhood operators play a crucial role in defining effective Local Search solvers, allowing one to limit the explored search space and prune the fitness landscape. Still, there is no accepted formal representation of such operators: they are usually modeled as algorithms in procedural language, lacking in compositionality and readability. In this paper we outline a new formalization capable of representing several neighborhood operators eschewing their coding in a full Turing complete language. The expressiveness of our proposal stems from a rich problem representation, as used in constraint programming models. We compare our system to competing approaches and show a clear increment in expressiveness.
Self-adaptive systems (SAS) are exceptional systems, on account of their versatile composition, dynamic behavior and evolutive nature. Existing formal languages for the specification of SAS focus on adapting system el...
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ISBN:
(纸本)9781450366687
Self-adaptive systems (SAS) are exceptional systems, on account of their versatile composition, dynamic behavior and evolutive nature. Existing formal languages for the specification of SAS focus on adapting system elements to achieve a target goal, following specific rules, without much attention on the adaptation of requirements themselves. The State-constraint Transition (SCT) modeling language enables the specification of dynamic requirements, both at the domain and application level, as a result of space or time variability. This language, evaluated in this paper, enables the specification of a variety of requirement types, for SASs from different domains, while generating a configuration, all configurations, and number of possible configurations, in milliseconds. This paper presents these results, namely;expressiveness, domain independence and scalability, from the viewpoint of designers and domain engineers, following a goal-question-metric approach. However, being primarily based on constraint programming (CP), the language suffers from drawbacks inherited from this paradigm, specifically time related requirements, like (e.g. order, frequency and staged requirements).
Advances in computing, communications, sensors, and cloud computing have resulted in the proliferation of Internet of Things (IoT) which forms a foundation for Cyber-Physical Systems (CPS). Cyber-physical attacks can ...
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ISBN:
(纸本)9781728140346
Advances in computing, communications, sensors, and cloud computing have resulted in the proliferation of Internet of Things (IoT) which forms a foundation for Cyber-Physical Systems (CPS). Cyber-physical attacks can cause tangible effects in the physical world. The attacker's goal is to disrupt the normal operations of the CPS for example: equipment overstress, safety limits violation, damage to the product quality, safety compliance violation etc. The continued rise of cyber-attacks together with the evolving skills of the attackers, and the inefficiency of the traditional security algorithms to defend against advanced and sophisticated attacks such as Distributed Denial of service (DDoS), slow DoS and zero-day, necessitate the development of novel defense and resilient detection techniques compared to traditional approaches like signature and behavior-based methods. To deal with this, we propose a novel approach for learning detection model that includes operational and network data to detect advanced attacks. More precisely, our approach is able to learn a relational network that connects events at different system layers so that attacks can be identified with higher confidence level. In this paper, we propose a decision model by learning a set of constraints/relations from the data that conjunctively defines the normal operation of a CPS. The solutions of the decision model characterize the normal states of a given CPS. The malicious operations are detected when one or more constraints fail for a given state of CPS. The results demonstrates the effectiveness of the approach. The main advantage of our approach is the interpretability of the model.
In constraint programming, enumeration strategies play an important role, they can significantly impact the performance of the solving process. However, choosing the right strategy is not simple as its behavior is com...
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ISBN:
(纸本)9783642311369;9783642311376
In constraint programming, enumeration strategies play an important role, they can significantly impact the performance of the solving process. However, choosing the right strategy is not simple as its behavior is commonly unpredictable. Autonomous search aims at tackling this concern, it proposes to replace bad performing strategies by more promising ones during the resolution. This process yields a combination of enumeration strategies that worked during the search phase. In this paper, we focus on the study of this combination by carefully tracking the resolution. Our preliminary goal is to find good enumeration strategy blends for a given constraint Satisfaction Problem.
The objective of the maximum weighted submatrix coverage problem (MWSCP) is to discover K submatrices that together cover the largest sum of entries of the input matrix. The special case of K = 1 called the maximal-su...
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ISBN:
(数字)9783030192129
ISBN:
(纸本)9783030192129;9783030192112
The objective of the maximum weighted submatrix coverage problem (MWSCP) is to discover K submatrices that together cover the largest sum of entries of the input matrix. The special case of K = 1 called the maximal-sum submatrix problem was successfully solved with CP. Unfortunately, the case of K > 1 is more difficult to solve as the selection of the rows of the submatrices cannot be decided in polynomial time solely from the selection of K sets of columns. The search space is thus substantially augmented compared to the case K = 1. We introduce a complete CP approach for solving this problem efficiently composed of the major CP ingredients: (1) filtering rules, (2) a lower bound, (3) dominance rules, (4) variable-value heuristic, and (5) a large neighborhood search. As the related biclustering problem, MWSCP has many practical data-mining applications such as gene module discovery in bioinformatics. Through multiple experiments on synthetic and real datasets, we provide evidence of the practicality of the approach both in terms of computational time and quality of the solutions discovered.
In this paper a unified framework is presented for coordinated multi-drone tasking in emergency response missions. As elaborated in this work, response missions can be broken into a number of distinct tasks that can b...
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ISBN:
(纸本)9781728103327
In this paper a unified framework is presented for coordinated multi-drone tasking in emergency response missions. As elaborated in this work, response missions can be broken into a number of distinct tasks that can be allocated among the available drone agents to expedite the response operations. The proposed framework enables the development and execution of algorithms that jointly schedule and route drone agents across the field to complete their tasks and successfully address the mission goals considering the agent limitations. The key design challenges of implementing the proposed framework are discussed. Finally, initial simulation and experimental results are presented providing evidence of the real life applicability and reliability of the proposed framework.
The container transfer chain management should be carried out taking into consideration the maximum possible of environment interactions. For this reason, integrated approaches have to be investigated for solving sche...
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The container transfer chain management should be carried out taking into consideration the maximum possible of environment interactions. For this reason, integrated approaches have to be investigated for solving scheduling problems in port container terminals. In our work, we propose a reactive multi-agent system for simultaneous (re)scheduling of vessel, quay crane, operator and trucks. The system contains a scheduling agent in the form of a heuristic whose performance is validated by comparing its results with an associated constraint programming model. The multi-agent system dedicated for embedded systems is tested with a reactive approach when the heuristic is able to reschedule on real time once a perturbation occurs. The robust solution obtained could also be used as a starting plan followed by rescheduling procedure for unexpected events in a proactive approach. Simulation study shows that the reactive approach provides less deviation between planned and actual schedules which guarantees the work smoothness and avoids flow instability. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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