This paper presents a framework taking advantage of the capabilities of current constraint solvers to plan the work on construction sites. It combines different constraints under a common framework to facilitate the d...
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This paper presents a framework taking advantage of the capabilities of current constraint solvers to plan the work on construction sites. It combines different constraints under a common framework to facilitate the definition of temporal relations between different tasks and provides a user-friendly web interface, which facilitates the planning of construction sites. Even though the framework uses complex mathematical models, the users do not need to know the underlying theoretical framework. Another important feature of the framework is, in contrast to usual static planning, that solutions can be dynamically adapted to take into account delays happening during the actual construction process. In order to show the applicability of the methodology, the paperspaper shows how a real construction project can be planned by using the framework.
Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be consi...
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Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-machine requirements, availabilities in the system in terms of capacity, machine and worker abilities. In this study, the cell formation problem is modeled using machine, part processing and worker flexibilities via resource element-based representation. Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. A total of 12 case problems are generated, and different search phases of constraint programming are defined for the solution procedure. The cell formation problem is modeled in both constraint programming and integer programming, and a comparative analysis of constraint programming and integer programming model solutions is done. The results indicate that both the models are effective and efficient in the solution of the cell formation problem.
Existing training techniques for spiking neuronal networks tend to be monolithic in nature and scale poorly to larger networks. This paper presents a technique for combining multiple functional neural groupings into a...
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Existing training techniques for spiking neuronal networks tend to be monolithic in nature and scale poorly to larger networks. This paper presents a technique for combining multiple functional neural groupings into a more complex composite network. This is accomplished by ensuring that four axioms hold true for the composite network. The axioms were designed to ensure that incoming signals arrive simultaneously to any component groupings. A number of experiments were conducted in which an algorithm implementing the axioms was used to combine component groupings into more complex networks;these experiments show the practical utility of the technique and reinforce by demonstration the correctness of the axioms.
The Advanced Encryption Standard (AES) is one of the most studied symmetric encryption schemes. During the last years, several attacks have been discovered in different adversarial models. In this paper, we focus on r...
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The Advanced Encryption Standard (AES) is one of the most studied symmetric encryption schemes. During the last years, several attacks have been discovered in different adversarial models. In this paper, we focus on related-key differential attacks, where the adversary may introduce differences in plaintext pairs and also in keys. We show that constraint programming (CP) can be used to model these attacks, and that it allows us to efficiently find all optimal related-key differential characteristics for AES-128, AES-192 and AES-256. In particular, we improve the best related-key differential for the whole AES-256 and give the best related-key differential on 10 rounds of AES-192, which is the differential trail with the longest path. Those results allow us to improve existing related-key distinguishers, basic related-key attacks and q-multicollisions on AES-256. (C) 2018 Elsevier B.V. All rights reserved.
Wind farms are frequently located in proximity to human dwellings, natural habitats, and infrastructure making land use constraints and noise matters of increasing concern for all stakeholders. In this study, we perfo...
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Wind farms are frequently located in proximity to human dwellings, natural habitats, and infrastructure making land use constraints and noise matters of increasing concern for all stakeholders. In this study, we perform a constrained multi-objective wind farm layout optimization considering energy and noise as objective functions, and considering land use constraints arising from landowner participation, environmental setbacks and proximity to existing infrastructure. A multi-objective, continuous variable Genetic Algorithm (NSGA-II) is combined with a novel constraint handling approach to solve the optimization problem. This constraint handling approach uses a combination of penalty functions and constraint programming to balance local and global exploration to find feasible solutions. The proposed approach is used to solve the wind farm layout optimization problem with different numbers of turbines and under different levels of land availability (constraint severity). Our results show increasing land availability and/or number of turbines, increases energy generation, noise production, and computational cost. Results also illustrate the potential of the proposed constraint handling approach to outperform existing methods in the context of evolutionary optimization, yielding better solutions at a lower computational cost. (C) 2018 Elsevier Ltd. All rights reserved.
The lack of a proper integration of strategic Air Traffic Management decision support tools with tactical Air Traffic Control interventions usually generates a negative impact on the Reference Business Trajectory adhe...
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The lack of a proper integration of strategic Air Traffic Management decision support tools with tactical Air Traffic Control interventions usually generates a negative impact on the Reference Business Trajectory adherence, and in consequence affects the potential of the Trajectory-Based Operations framework. In this paper, a new mechanism relaying on Reference Business Trajectories as a source of data to reduce the amount of Air Traffic Controller interventions at the tactical level while preserving Air Traffic Flow Management planned operations is presented. Artificial Intelligence can enable constraint programming as it is a powerful paradigm for solving complex, combinatorial search problems. The proposed methodology takes advantage of constraint programming and fosters adherence of Airspace User's trajectory preferences by identifying tight interdependencies between trajectories and introducing a new mechanism to improve the aircraft separation at concurrence events considering time uncertainty. The underlying philosophy is to capitalize present degrees of freedom between layered Air Traffic Management planning tools, when sequencing departures at the airports by considering the benefits of small time stamp changes in the assigned Calculated Take-Off Time departures and to enhance Trajectory-Based Operations concepts.
The idea of deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has vitalized this new research stream. One conceivable scenario of using a drone in conjunction wi...
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The idea of deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has vitalized this new research stream. One conceivable scenario of using a drone in conjunction with a traditional delivery truck to distribute parcels is discussed in earlier literature and termed the parallel drone scheduling traveling salesman problem (PDSTSP). This study extends the problem by considering two different types of drone tasks: drop and pickup. After a drone completes a drop, the drone can either fly back to depot to deliver the next parcels or fly directly to another customer for pickup. Integrated scheduling of multiple depots hosting a fleet of trucks and a fleet of drones is further studied to achieve an operational excellence. A vehicle that travels near the boundary of the coverage area might be more effective to serve customers that belong to the neighboring depot. This problem is uniquely modeled as an unrelated parallel machine scheduling with sequence dependent setup, precedence-relationship, and reentrant, which gives us a framework to effectively consider those operational challenges. A constraint programming approach is proposed and tested with problem instances of m-truck, m-drone, m-depot, and hundred-customer distributed across an 8-mile square region.
In two-dimensional nesting problems (irregular packing problems) small pieces with irregular shapes must be packed in large objects. A small number of exact methods have been proposed to solve nesting problems, typica...
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In two-dimensional nesting problems (irregular packing problems) small pieces with irregular shapes must be packed in large objects. A small number of exact methods have been proposed to solve nesting problems, typically focusing on a single problem variant, the strip packing problem. There are however several other variants of the nesting problem which were identified in the literature and are very relevant in the industry. In this paper, constraint programming (CP) is used to model and solve all the variants of irregular cutting and packing problems proposed in the literature. Three approaches, which differ in the representation of the variable domains, in the way they deal with the core constraints and in the objective functions, are the basis for the three models proposed for each variant of the problem. The non-overlap among pieces, which must be enforced for all the problem variants, is guaranteed through the new global constraint NoOverlap in one of the proposed approaches. Taking the benchmark instances for the strip-packing problem, new instances were generated for each problem variant. Extensive computational experiments were run with these problem instances from the literature to evaluate the performance of each approach applied to each problem variant. The models based on the global constraint NoOverlap performed consistently better for all variants due to the increased propagation and to the low memory usage. The performance of the CP model for the strip packing problem with the global constraint NoOverlap was then compared with the Dotted Board with Rotations using larger instances from the literature. The experiments show that the CP model with global constraint NoOverlap can quickly find good quality solutions in shorter computational times even for large instances.
Unexpected events can compromise the execution of the production schedule in low-volume assembly lines. When a disruption occurs because of a delayed part supply, a quality problem or an operator absence, a reactive s...
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Unexpected events can compromise the execution of the production schedule in low-volume assembly lines. When a disruption occurs because of a delayed part supply, a quality problem or an operator absence, a reactive scheduling approach should be used in a short time in order to prevent significant deviations in final performances. In this study, we propose a new approach based on constraint programming to deal with disruptions. It is tested on a large dataset of problem instances and the obtained results are discussed.
With increasing implementation of off-site prefabrication and modular construction technologies, the complexity of material supply chain management on construction projects has grown substantially. However, research o...
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With increasing implementation of off-site prefabrication and modular construction technologies, the complexity of material supply chain management on construction projects has grown substantially. However, research on construction scheduling has yet to take dynamic material logistics as an explicit constraint in analytically deriving construction schedules and addressing impacts of uncertainties in material supply on project budget. This study proposes a two-step analytical approach to tackle the identified problem. First, a constraint programming-based scheduling optimization model is developed to derive project schedules subject to variable material delivery times and finite crew resource availability. The second step is to take advantage of the valid optimization model for evaluating the impact of different input settings of material logistics on project budget. An example project adapted from the literature is used to illustrate the effectiveness of the proposed optimization model in coping with variable material delivery times. Based on the same case, the delivery date of a particular material is singled out as the risk factor of interest in order to derive the complex relationship between material delivery date and total project cost. In addition, a case study based on a bridge girder fabrication project is presented to demonstrate the applicability of the proposed optimization model on projects of practical size. In conclusion, this study adds to the body of knowledge by developing an analytical methodology that factors material supply constraints into the resource-constrained scheduling optimization model so as to analyze the impact of uncertainties in material deliveries on project budget.
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