Explanation-based large neighborhood search

Prud’Homme, Charles; Lorca, Xavier; Jussien, Narendra

doi:10.1007/s10601-014-9166-6

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…There have also been some successful approaches for defining the relatedness of variables in order to select a fragment in a problem-independent way. Notably, relatedness is defined in [37] by how many values each variable causes CP propagators to remove from other variables' current domains; in [29] by the impact on the objective that the current value of each variable has; and in [41] by explanations of why, for example, an improving solution could not be found.…”

Section: Lns Variationsmentioning

confidence: 99%

From declarative models to local search

Björdal

2023

Constraints

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A solver is a general-purpose software for solving optimisation problems. It takes as input a description of a problem, called a model, and uses a collection of algorithms, called its solving technology, to ideally produce an optimal solution as output. Most solvers have a modelling language that cannot be processed by other solvers. This means that there is a risk of making an early commitment to a solver and its technology when writing a model. To address this risk, and to increase the accessibility of solvers, there has been a push for technology-independent modelling languages, a notable one being MiniZinc.A model written in MiniZinc is transformed by the MiniZinc toolchain in order to suit a targeted solver and its technology. However, for a solver to process a MiniZinc model, it also requires what is called a backend for MiniZinc. A backend translates the transformed MiniZinc model into the solver's own modelling language and synthesises any components not in a MiniZinc model that the solver (or its technology) requires.The solving technology called constraint-based local search (CBLS) is based on the popular algorithm design methodology called local search, which often quickly produces near-optimal solutions, even to large problems. So, with the advent of CBLS solvers, there is a need for CBLS backends to modelling languages like MiniZinc.This thesis contributes to three research topics. First, it shows for the first time how to create a CBLS backend for a technology-independent modelling language, namely MiniZinc, and it shows that CBLS via MiniZinc can be competitive for solving optimisation problems. Second, it extends MiniZinc with concepts from local search, and shows that these concepts can be used even by other technologies towards designing new types of solvers. Third, it extends the utilisation of another technology, namely constraint programming, inside local-search solvers and backends.These contributions make local search accessible to all users of modelling languages like MiniZinc, and allow some optimisation problems to be solved more efficiently via such languages.

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Section: Lns Variationsmentioning

confidence: 99%

From declarative models to local search

Björdal

2023

Constraints

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“…The verification algorithms presented in previous sections create the aforementioned CSPs, and they are solved by using Choco T M solver [32], although it is able to be adapted to another solver [33]. The code of Algorithm 6, included automatically in the Hibernate T M class for each modelled business data object, analyses every object with the iterator and informs whether any object has no state.…”

Section: Verification Of the Model And Data State Discoverymentioning

confidence: 99%

Verifying the manipulation of data objects according to business process and data models

et al. 2020

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Business processes read and write data objects, usually stored in databases. Although data models and activity-oriented business process models originate from different paradigms, they need to work together properly. The data object states are transformed during each process instance by the activities of the process model. It is therefore necessary to verify whether the states of the data objects are correct according to the process model, and to discover the states of the stored data objects. This implies determining the relation between the data objects stored in the database, the data objects involved in the process, and the activities that within the business process that create the data objects and modify their states. In order to verify the business process annotated with data states and to reduce the existing gap between data model and business process model, we propose a methodology that includes enlarging the capability to describe data states in business processes; verifying the completeness and consistency of the data states described in accordance with their relation to the business process model; and discovering the states of the data objects stored in the database according to the business process model where they are managed. The methodology is supported by a framework that enables a natural-like language to be employed to describe the states, to apply the necessary algorithms to verify the consistency and completeness of the model, and to determine the states of the stored data objects according to the model described. To validate our proposal, an extension of Activiti TM has been implemented and applied to a real example as an illustration of its applicability.Keywords Business processes • Integration of data and processes • Data object state • Object-relational mapping • Data state verification Business processes (BPs) and their continuous improvement are fundamental to the operation of companies. For a wide range of enterprises, business process analytics, validation, and verification are a key endeavour [1]. The automation of processes offers an opportunity to B José Miguel Pérez-Álvarez

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“…A CP solver performs a search over the domain of possible values for the variables that satisfy all of the constraints given. Several advanced techniques are used in the solver engine to optimize the search strategy, and thus enable it to handle large search spaces [10]. This is the main advantage of relying on a constraint solver to find a solution rather than exploring the entire search space with an analytical model to determine the solution to the system.…”

Section: F Introducing Parallel Scheduling Overheadsmentioning

confidence: 99%

Image tiling for embedded applications with non-linear constraints

Schwambach

Cleyet-Merle

Issard

et al. 2015

2015 Conference on Design and Architectures for Signal and Image Processing (DASIP)

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Tiling is a key aspect of the design of embedded image processing applications, due to local memory constraints.To maximize system performance, the designer must select a suitable tile size that balances data transfers and computation. In this work, we present a method for optimal 2D image tile sizing using constraint programming. Unlike previous methods, ours accurately models DMA data transfer times and parallel scheduling overheads with non-linear constraints. Our experiments with a binomial filter demonstrate that we can compute the optimal tiling dimensions that minimize the execution time for different image sizes and internal memory constraints. This technique provides invaluable information for both application developers and system architects that can quickly explore design trade-offs.

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Explanation-based large neighborhood search

Cited by 9 publications

References 21 publications

From declarative models to local search

From declarative models to local search

Verifying the manipulation of data objects according to business process and data models

Image tiling for embedded applications with non-linear constraints

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