Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy c...
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Compromising productivity in exchange for energy saving does not appeal to highly capitalized manufacturing industries. However, we might be able to maintain the same productivity while significantly reducing energy consumption. This paper addresses a flexible job shop scheduling problem with a shutdown (on/off) strategy aiming to minimize makespan and total energy consumption. First, an alternative mixed integer linear programming model is proposed. Second, a novel constraint programming is proposed. Third, practical operational scenarios are compared. Finally, we provide benchmarking instances, CPLEX codes, and genetic algorithm codes, in order to promote related research, thus expediting the adoption of energy-efficient scheduling in manufacturing facilities. The computational study demonstrates that (1) the proposed models significantly outperform other benchmark models and (2) we can maintain maximum productivity while significantly reducing energy consumption by 14.85% (w/o shutdown) and 15.23% (w/shutdown) on average.
We present a novel approach for automatic apartment layout generation. Given a polygonal apartment envelope and a list of rooms with associated area, our so-called Optimizer algorithm generates several floor plans aim...
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We present a novel approach for automatic apartment layout generation. Given a polygonal apartment envelope and a list of rooms with associated area, our so-called Optimizer algorithm generates several floor plans aiming at both architectural and functional constraints. To do so, Optimizer discretizes the floor space into a grid according to architectural constraints and reduces the problem to a cell assignment which is solved through a coupled constraint programming genetic optimization approach. Obtained results demonstrate the feasibility of our approach, customized plans are architecturally and functionally valid, they are mostly generated in about 1 min.
Integration of process planning and scheduling (IPPS) is to carry out both functions simultaneously. This paper provides a graph-based constraint programming (GCP) approach to solve the type-2 IPPS problem that takes ...
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Integration of process planning and scheduling (IPPS) is to carry out both functions simultaneously. This paper provides a graph-based constraint programming (GCP) approach to solve the type-2 IPPS problem that takes AND/OR graphs as input. The proposed GCP approach is implemented based on the IBM ILOG CP Optimizer. AND/OR graph is tailored to cope with IPPS instances. Directed arcs define both precedence and presence relationships. The or-link, a set of mutually-exclusive operations, is defined to represent alternative process routes. Interval variables and scheduling-oriented constraints are adopted to project the IPPS-specific AND/OR graph to a concise CP model with which minimizing makespan is incorporated as the objective. The GCP approach is tested on a set of benchmark problems. Experimental results show that the proposed approach outperforms compared algorithms on major IPPS instances. (C) 2021 Elsevier Ltd. All rights reserved.
Requirements Engineering (RE) covers not only the capture and structuring of various properties the system should achieve but also the identification of high-level choices on how to achieve such goals or to avoid rela...
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ISBN:
(纸本)9781728183589
Requirements Engineering (RE) covers not only the capture and structuring of various properties the system should achieve but also the identification of high-level choices on how to achieve such goals or to avoid related obstacles. Generic RE frameworks support simple formalisation of alternatives using AND/OR refinements while more specialised fields such as safety and security engineering have richer analysis capabilities respectively through fault and attack trees. In this paper, we review the various constructs proposed in those domains and state their semantics at RE level to support safety and security co-engineering. As a supplementary step, we propose a mapping on the semantics provided by constraint programming in order to search for optimal configurations in the design space of a RE model. We consider multiple objectives stated as non-functional requirements and formalised using quantified attributes over goal models. Our work is validated on the complex design of an oil pipe system mixing safety and security critical properties.
We explore how planning for near optimal behaviors of mixed discrete-continuous systems can be done by deductive reasoning. For reasoning to be efficient, it must be properly controlled. It is surprising and mathemati...
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The traditional resource-constrained project scheduling problem makes the amounts of resource input fixed and ignores the joint effect of multiple time constraints, which may lead to the failure of traditional algorit...
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The traditional resource-constrained project scheduling problem makes the amounts of resource input fixed and ignores the joint effect of multiple time constraints, which may lead to the failure of traditional algorithms. This paper introduces a new practical problem called the resource input optimization problem with combined time constraints (RIOP/CTC), which studies the influence of resource input schemes. The new problem combines three types of time constraints, including precedence relations, resource calendars, and interruptability for the first time, which makes it closer to the actual scheduling problem. We propose a new network diagram called node network diagram and develop an optimization model based on constraint programming (CP) and the technique for order preference by similarity to the ideal solution (TOPSIS). A three-step guideline and an actual project case are provided for schedulers to help them better use the model to solve RIOP/CTC, which also proves the validity of the model. Computational experiments are carried out to show that the CP optimizer is superior to the three common metaheuristic algorithms in solving quality and speed and can provide a near-optimum solution for large-scale scheduling problems in an acceptable time. The proposed model contributes to improving the practical decision system to support the formulation of real-life project resource input schemes, scheduling plans, and employee work plans.
Time-Sensitive Networking (TSN) extends IEEE 802.1 Ethernet for safety-critical and real-time applications in several areas, e.g., automotive, aerospace or industrial automation. However, many of these systems also ha...
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ISBN:
(纸本)9781728183244
Time-Sensitive Networking (TSN) extends IEEE 802.1 Ethernet for safety-critical and real-time applications in several areas, e.g., automotive, aerospace or industrial automation. However, many of these systems also have stringent security requirements, and security attacks may impair safety. Given a TSN-based distributed architecture, a set of applications with tasks and messages, as well as a set of security and redundancy requirements, we are interested to synthesize a system configuration such that the real-time, safety and security requirements are satisfied. We use the Timed Efficient Stream Loss-Tolerant Authentication (TESLA) low-resource multicast authentication protocol to guarantee the security requirements, and redundant disjunct message routes to tolerate link failures. We consider that the tasks are scheduled using static cyclic scheduling and that the messages use the time-sensitive traffic class in TSN, which relies on schedule tables (called Gate Control Lists, GCLs) in the network switches. A configuration consists of the schedule tables for tasks as well as the disjoint routes and GCLs for messages. We propose a constraint programming-based formulation for this problem and we evaluate it on several test cases.
Effective and robust search heuristics are critical for solving constraint satisfaction or optimization problems. In this paper, we propose a new hybrid heuristic which uses the idea of reducing the dynamic arity of c...
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ISBN:
(纸本)9781728192284
Effective and robust search heuristics are critical for solving constraint satisfaction or optimization problems. In this paper, we propose a new hybrid heuristic which uses the idea of reducing the dynamic arity of constraints, called constraintArity-Reduction (CAR). The hybrid heuristic is formed with a base heuristic which switches to CAR using a switching heuristic. We experimented with hybrids of CAR combining existing stateof-the-art search heuristics. Experimental results on a variety of structured benchmarks show that hybrid CAR heuristics is an effective and competitive strategy, which can successfully reduce the search space and improve the performance of existing heuristics on a variety of problems.
In the context of Network-on-Chip (NoC) based Chip Multiprocessor (CMP) design, core mapping for application specific systems is a challenging problem. In such designs, various decisions have to be made that affect pe...
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ISBN:
(数字)9781728165820
ISBN:
(纸本)9781728165820
In the context of Network-on-Chip (NoC) based Chip Multiprocessor (CMP) design, core mapping for application specific systems is a challenging problem. In such designs, various decisions have to be made that affect performance and power consuinption. Moreover, in emerging 3D NoC systems, by intensification of cooling issues, temperature constraints on hot-spots are added, and problem becomes more complicated. In this paper, an earlier constraint programming (('P) methodology for heterogeneous 2D NoC design is extended to 3D model, while critical temperature constraints are accounted. In a single stage, our approach can choose core types from a set of low, medium and high power, and assign them to appropriate places on the mesh which minimizes the overall computation time and communication cost while satisfying the temperature constraints. To achieve our objective, in addition to cores placement problem, tasks should also be scheduled on corresponding cores with matching performance levels to minimize the overall completion time (makespan). Experimental results show that task completion times are more dependent on the mesh structure for our benchmark data. 3D mesh structures may yield shorter task completion times, without compromising thermal constraints. On the other hand, restricting the peak temperature naturally requires the usage of low-performance computing elements which inherently may delay the processing time.
Les systèmes dynamiques sont des modèles mathématiques pour décrire l'évolution temporelle de l'état d'un système. Il y a deux classes de systèmes dynamiques pertine...
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Les systèmes dynamiques sont des modèles mathématiques pour décrire l'évolution temporelle de l'état d'un système. Il y a deux classes de systèmes dynamiques pertinentes à cette thèse : les systèmes discrets et les systèmes continus. Dans les systèmes dynamiques discrets (ou les programmes informatiques classiques), l'état évolue avec un pas de temps discrets. Dans les systèmes dynamiques continus, l'état du système est fonction du temps continu, et son évolution caractérisée par des équations différentielles. Étant donné que ces systèmes peuvent prendre des décisions critiques, il est important de pouvoir vérifier des propriétés garantissant leur sûreté. Par exemple, sur un programme, l'absence de débordement arithmétique. Dans cette thèse, nous développons un cadre pour la vérification automatique des propriétés de sûreté des programmes. Un élément clé de cette vérification est la preuve de propriétés invariantes. Nous développons ici un algorithme pour synthétiser des invariants inductifs (des propriétés vraies pour l'état initial, qui sont stables dans l'évolution des états du programme, donc sont toujours vraies par récurrence) pour des programmes numériques. L’interprétation abstraite (IA) est une approche traditionnelle pour la recherche d’invariants inductifs des programmes numériques. L'IA interprète les instructions du programme dans un domaine abstrait (par exemple intervalles, octogones, polyèdres, zonotopes), domaine qui est choisi en fonction des propriétés à prouver. Un invariant inductif peut être calculé comme limite possiblement infinie des itérées d'une fonctionnelle croissante. L'analyse peut recourir aux opérateurs d'élargissement pour forcer la convergence, au détriment de la précision. Si l'invariant n'est pas prouvé, une solution standard est de remplacer le domaine par un nouveau domaine abstrait davantage susceptible de représenter précisément l'*** programmation par contraintes (PPC) est une approche alternative pour synthétiser d
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