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.
The emerging field of Compressive Sensing (CS) has shown that sparse signals can be acquired using a rate far less than the one required by the classical Shannon-Nyquist theorem. In that, CS acquires a sparse signal b...
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ISBN:
(纸本)9781538673300
The emerging field of Compressive Sensing (CS) has shown that sparse signals can be acquired using a rate far less than the one required by the classical Shannon-Nyquist theorem. In that, CS acquires a sparse signal by correlating it with the rows of a sensing matrix to form a small set of measurements. And then these measurements are used to reconstruct the original sparse signal using an optimization algorithm. In this paper, we extend our recently published work, to more investigate the power of constraint programming (CP) solvers with the CS problem. We show that a statistical property of the sensing matrix can highly affects the performance of CP solvers in the case of CS problem. Which enable us to improve the CP solvers performance and even reach the optimal case. The effectiveness of our method is demonstrated via simulation results.
This paper proposes a scheduling method for malleable tasks based on constraint programming (CP). For a given task-graph, the proposed method decides the execution order of tasks and the number of cores to execute eac...
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ISBN:
(纸本)9781538654576
This paper proposes a scheduling method for malleable tasks based on constraint programming (CP). For a given task-graph, the proposed method decides the execution order of tasks and the number of cores to execute each task simultaneously in such a way that the overall schedule length is minimized Experimental results show that our CP-based scheduling method could find better schedules than the state-of-the-art method which is based on integer linear programming.
Formal reasoning about finite sets and cardinality is important for many applications, including software verification, where very often one needs to reason about the size of a given data structure. The constraint Log...
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Juxtapositianing manuallycreated business process models with diagrams generated using process discovery algorithms exposes high complexity of the latter. As a consequence, their formal verification requires significa...
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ISBN:
(纸本)9788394941956
Juxtapositianing manuallycreated business process models with diagrams generated using process discovery algorithms exposes high complexity of the latter. As a consequence, their formal verification requires significant computational resources due to a large state space. Nevertheless, an analysis of the generated model is needed to assure its correctness and the ability to represent source data. As a solution to this problem, we present an approach for constraint-based generation of a complete workflow log for a given BPMN model. In this paper, we propose a method to extract directed subgraphs representing token flows in the process together with a set of predefined constraints. Likewise, in the case of process simulation, these constraints ensure the correctness of the generated traces. Ultimately, the obtained results can be compared to the original workflow log used for process discovery in order to verify the obtained model.
With the rapid growth of Online Social Networks (OSNs) and the information involved in them, research studies concerning OSNs, as well as the foundation of businesses, have become popular. Privacy on OSNs is typically...
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ISBN:
(纸本)9783319735214;9783319735207
With the rapid growth of Online Social Networks (OSNs) and the information involved in them, research studies concerning OSNs, as well as the foundation of businesses, have become popular. Privacy on OSNs is typically protected by anonymisation methods. Current methods are not sufficient to ensure privacy and they impose restrictions on the network making it not suitable for research studies. This paper introduces an approach to find an optimal anonymous graph under user-defined metrics using constraint programming, a technique that provides well-tested and optimised engine for combinatorial problems. The approach finds a good trade-off between protection of sensitive data and quality of the information represented by the network.
The Traveling Tournament Problem with Predefined Venues (TTPPV) is a practical problem arising from sports scheduling. We describe two different modeling approaches for this problem, each of which is suitable for diff...
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ISBN:
(数字)9783030059187
ISBN:
(纸本)9783030059187;9783030059170
The Traveling Tournament Problem with Predefined Venues (TTPPV) is a practical problem arising from sports scheduling. We describe two different modeling approaches for this problem, each of which is suitable for different sizes of instance. The experimental results show that our modeling approaches lead to improved performance compared to previous techniques in terms of the number of feasible solutions and the optimal value. Furthermore, we present how to execute the models in parallel through data-level parallelism. The parallel versions do not only gain speedup but also attain significant improvement on optimal value since more subtrees are searched independently.
This paper presents the concept of objective landscape in the context of constraint programming. An objective landscape is a lightweight structure providing some information on the relation between decision variables ...
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ISBN:
(纸本)9783319930312;9783319930305
This paper presents the concept of objective landscape in the context of constraint programming. An objective landscape is a lightweight structure providing some information on the relation between decision variables and objective values, that can be quickly computed once and for all at the beginning of the resolution and is used to guide the search. It is particularly useful on decision variables with large domains and with a continuous semantics, which is typically the case for time or resource quantity variables in scheduling problems. This concept was recently implemented in the automatic search of CP Optimizer and resulted in an average speed-up of about 50% on scheduling problems with up to almost 2 orders of magnitude for some applications.
Non-deterministic specifications play a central role in the use of formal methods for software development. Such specifications can be more readable, but hard to execute efficiently due to the usually large search spa...
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ISBN:
(纸本)9783319929705;9783319929699
Non-deterministic specifications play a central role in the use of formal methods for software development. Such specifications can be more readable, but hard to execute efficiently due to the usually large search space. constraint programming offers advanced algorithms and heuristics for solving certain non-deterministic models. Unfortunately, this requires writing models in a form suitable for efficient solving where the readability typically required from a specification is lost. Tools like ProB attempt to bridge this gap by translating high-level first-order predicate logic specifications into formal models suitable for constraint solving. In this paper we study potential improvements to this methodology by (1) using refinement to transform specifications into models suitable for efficient solving, (2) translating first-order predicates directly into the OscaR framework and (3) using different kinds of solvers as a back end. Formal verification by proof ensures the correctness of the solution of the model with respect to the specification.
Domain experts typically have detailed knowledge of the concepts that are used in their domain;however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms...
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Domain experts typically have detailed knowledge of the concepts that are used in their domain;however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using constraint programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance. (C) 2016 The Authors. Published by Elsevier Ltd.
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