Constraint Programming Paradigm Research Articles

Finding dependencies in data based on methods of satisfying table con-straints

The work deals with the search for a special type of regularities in data, called frequent patterns. A frequent pattern is understood as a certain set of attributes that characterizes a sufficiently large number of objects of the training sample. There are many methods for pattern discovery, but they usually do not allow flexible consideration of necessary requirements for their type. Taking into account the new conditions that the desired patterns must meet leads in practice to a time-consuming modification of used algorithms and a decrease in computing performance. This article proposes a new approach based on the constraint programming paradigm, which is free from the listed disadvantages. The approach is based on the original way of presenting the training sample using specialized table constraints – compressed D-type tables, on the author's method of backtracking, as well as on specialized reduction rules for table constraints. Particular attention is paid to solving the closed patterns discovery problem, which is included as part of the solution of all machine learning problems considered in the work, which means taking into account additional requirements for the type of patterns. As additional requirements to the type of pattern, constraints on the frequency of occurrence of a closed pattern, as well as conditions for the occurrence of some element (attribute) into the pattern, are considered. To the basic rules for the reduction of compressed D-type tables, rules are added that take into account the interesting attributes of the analyzed patterns. The advantage of the approach is that the taking into account and analyzing new constraints makes it possible to speed up the calculation process.

Comparison of exact and approximate approaches to UAVs mission contingency planning in dynamic environments.

This paper presents a novel approach to the joint proactive and reactive planning of deliveries by an unmanned aerial vehicle (UAV) fleet. We develop a receding horizon-based approach to contingency planning for the UAV fleet's mission. We considered the delivery of goods to spatially dispersed customers, over an assumed time horizon. In order to take into account forecasted weather changes that affect the energy consumption of UAVs and limit their range, we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in the course of designing the contingency plans related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to the nonlinearity of the environment's characteristics, both constraint programming and genetic algorithm paradigms have been implemented. Because of the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The multiple computer experiments carried out allow for comparison representatives of the approximate and exact methods so as to judge which approach is faster for which size of the selected instance of the UAV mission planning problem.

A method for solving the open-pit mine production scheduling problem using the constraint programming paradigm

A method is developed to solve the problem of open-pit mine production scheduling, which is based on Constraint Programming and is used in working out an optimal plan of mining operations, taking into account heterogeneous economical, technological and some others requirements imposed by the customer. An original model is proposed to represent the problem considered as a constraint satisfaction problem. As the customer may verify the requirements, the domain model should enable addition of the new constraints and/or eliminate the existing ones. With the domain model being developed, the method proposed makes it possible to reach accurate solutions at the expense of highly effective reduction algorithms for each type of constraints and at the expense of specialized heuristics for non-perspective alternatives reduction in a search tree. The approach proposed enables addition of different constraints, even those for which it is difficult to find an analytical expression.

Интеллектуальный поиск точных решений задачи планирования открытых горных работ

A method of intelligent search for accurate solutions to the planning of open-pit mining has been developed. The method is implemented within the framework of the Constraint Programming Paradigm that allows us to process heterogeneous qualitative and quantitative constraints (in particular, economic, technological, etc.) simultaneously, as well as to maintain the model of subject domain being developed which is open to adding new constraints or deleting existing constraints. Various constraints can be added to the model, including those for which it is difficult to find a suitable analytical expression. In contrast to existing methods of local search the proposed method systematically explores the search space. The method allows us to find a global optimum in large dimensional spaces that describe practically significant problems arising in production. Currently, to solve this problem, the methods of integer linear programming are widely used. But its fundamental disadvantage is the need to represent all the constraints in the form of linear equalities and inequalities. However, in practice, some combinatorial optimization problems cannot be linearized and solved using traditional methods of mathematical programming. The developed method is illustrated by the example of a three-dimensional problem of finding the position of an intermediate pit wall by the processing periods taking into account the specified performance for mineral and overburden rocks and the objective profit function taking into account discounting. The types of constraints necessary for modeling the problem under consideration are identified. The possibility of applying the existing inference procedures on constraints is considered for these types. The method proposed makes it possible to obtain accurate solutions due to the intellectualization of the solution process by using highly efficient algorithms of reducing the search space for each type of constraints and specialized heuristics for pruning unpromising alternatives in the search tree.

Multi-criteria decision analysis for non-conformance diagnosis: A priority-based strategy combining data and business rules

Business process analytics and verification have become a major challenge for companies, especially when process data is stored across different systems. It is important to ensure Business Process Compliance in both data-flow perspectives and business rules that govern the organisation. In the verification of data-flow accuracy, the conformance of data to business rules is a key element, since essential to fulfil policies and statements that govern corporate behaviour. The inclusion of business rules in an existing and already deployed process, which therefore already counts on stored data, requires the checking of business rules against data to guarantee compliance. If inconsistency is detected then the source of the problem should be determined, by discerning whether it is due to an erroneous rule or to erroneous data. To automate this, a diagnosis methodology following the incorporation of business rules is proposed, which simultaneously combines business rules and data produced during the execution of the company processes. Due to the high number of possible explanations of faults (data and/or business rules), the likelihood of faults has been included to propose an ordered list. In order to reduce these possibilities, we rely on the ranking calculated by means of an AHP (Analytic Hierarchy Process) and incorporate the experience described by users and/or experts. The methodology proposed is based on the Constraint Programming paradigm which is evaluated using a real example. .

Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments

Unmanned aerial vehicles (UAVs) create an interesting alternative for establishing more sustainable urban freight deliveries. The substitution of traditional trucks in the last-mile distribution by a UAV fleet can contribute to urban sustainability by reducing air pollution and increasing urban freight efficiency. This paper presents a novel approach to the joint proactive and reactive planning of deliveries by a UAV fleet. We develop a receding horizon-based approach to reactive, online planning for the UAV fleet’s mission. We considered the delivery of goods to spatially dispersed customers over an assumed time horizon. Forecasted weather changes affect the energy consumption of UAVs and limit their range. Therefore, consideration should be given to plans for follow-up tasks, previously unmet needs, and predictions of disturbances over a moving time horizon. We propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. We implement a constraint programming paradigm, which is well suited to cope with the nonlinearity of the system’s characteristics. The proposed approach to online reactive UAV routing is evaluated in several instances. The computational experiments have shown that the developed model is capable of providing feasible plans for a UAV fleet’s mission that are robust to changes in weather and customer’s orders.

An approach to solution of constrained clustering problems using the constraint programming paradigm and the multiset theory

An integrated approach is proposed to solve the Constrained Clustering problems which are oriented on taking the opinion of a group of experts into account. A cluster and the objects to be clustered are proposed to be represented as multisets, and the distance between these to be determined by metrics in the Petrovsky multisets space. The approach is implemented within the Constraint Programming paradigm. In so doing, the significant complexity is in organizing effective processing the qualitative constraints, namely, the rules for assigning the objects to one or different clusters. The qualitative constraints are proposed to be represented and processed as table constraints of a new type, i.e., as the smart-tables of D-type. The main attention is confined to the problem of how to reduce the amount of constraints and how to simplify the Constrained Clustering problem. It is proposed to generate constraints for some pairs of objects rather than for all of them, with the generation based on a priori interval estimation of the optimal value of the clustering criterion. To do that, a modified method of multisets hierarchical clustering has been proposed. The approach proposed allows a global optimum to be found for the Constrained Clustering problems considered.

Глобальные ограничения при моделировании и решении задач в рамках парадигмы Constraint Programming.

At the moment, constraint programming technology is a powerful tool for solving combinatorial search and combinatorial optimization problems. To use this technology, any task must be formulated as a task of satisfying constraints. The role of the concept of global constraints in modeling and solving applied problems within the framework of the constraint programming paradigm can hardly be overestimated. The procedures that implement the algorithms of filtering global constraints are the elementary “building blocks” from which the model of a specific applied problem is built. Algorithms for filtering global constraints, as a rule, are supported by the corresponding developed theories that allow organizing high-performance computing. The choice of a particular software library is primarily determined by the extent to which the set and method of implementing global constraints corresponds tothe level of modern research in this area. The main focus of this article is focused on an overview of global constraints that are implemented within the most popular constraint programming libraries: Choco, GeCode, JaCoP, MiniZinc.

Automated Aggregator - Rewriting with the Counting Aggregate

Answer set programming is a leading declarative constraint programming paradigm with wide use for complex knowledge-intensive applications. Modern answer set programming languages support many equivalent ways to model constraints and specifications in a program. However, so far answer set programming has failed to develop systematic methodologies for building representations that would uniformly lend well to automated processing. This suggests that encoding selection, in the same way as algorithm selection and portfolio solving, may be a viable direction for improving performance of answer-set solving. The necessary precondition is automating the process of generating possible alternative encodings. Here we present an automated rewriting system, the Automated Aggregator or AAgg, that given a non-ground logic program, produces a family of equivalent programs with complementary performance when run under modern answer set programming solvers. We demonstrate this behavior through experimental analysis and propose the system's use in automated answer set programming solver selection tools.

Prognosis of multiple instances in time-aware declarative business process models

Technological evolution, heading for industry 4.0, makes companies tend to automate their management and operation, ideally defining it through business process models. To describe policies or rules related to the execution order of the activities in an organization, Declarative Business Process Models permit a relaxed description of activity order, which needs monitoring to detect non-conforming behaviors. Commonly, the detection of a violation implies that the malfunction has already occurred, being better to avoid the violation in advance. To predict future violations, prognosis is required.To allow the modeling of real business behavior, an extension of declarative business process models including both time patterns and multiple instances is proposed. This new model can be used to prognosticate if current process instances may violate a defined model in the future, according to the analysis of the robustness of the process instances evolution. The proposed Model-Based Prognosis is based on analyzing the event traces that represent the current instances and propagate their possible progression through the Constraint Programming paradigm. To ascertain if the model could be violated, it is analyzed how its robustness can tackle unexpected behaviors.To complete the formalization and modeling, an implementation applied to a real medical example is included in the paper. The prognosis of concurrent instances is addressed, dealing with formalized time and activity patterns even considering the resource availability, and getting acceptable execution times.The automatic verification and prognosis of declarative business processes are addressed considering concurrency and synchronization of multiple instances, performing well in terms of execution time.

Research Report on Automatic Synthesis of Local Search Neighborhood Operators

Constraint Programming (CP) and Local Search (LS) are different paradigms for dealing with combinatorial search and optimization problems. Their complementary features motivated researchers to create hybrid CP/LS solutions, maintaining both the modeling capabilities of CP and the computational advantages of the heuristic-based LS approach. Research presented in this report is focused on developing a novel method to infer an efficient LS neighborhood operator based on the problem structure, as modeled in the CP paradigm. We consider a limited formal language that we call a Neighborhood Definition Language, used to specify the neighborhood operators in a fine-grained and declarative manner. Together with Logic Programming runtime called Noodle, it allows to automatically synthesize complex operators using a Grammar Evolution algorithm.

The patient bed assignment problem solved by autonomous bat algorithm

The patient bed assignment problem consists of managing, in the best possible way, a set of beds with particular features and assigning them to a set of patients with special requirements. This assignment problem can be seen an optimization problem, of which the intended aims are usually to minimize the number of internal movements within a unit and to maximize bed usage according to the levels of criticality of the patients, among others. The usual approaches for solving this problem follow a traditional model based on the constraint programming paradigm, mainly using hard constraints. However, in real-life problems, constraints that should ideally be satisfied are often violated. In this paper, we present a new model for the patient bed assignment problem based on the minimum sum of unsatisfied constraints. This technique enables the consideration of soft constraints in the potential solutions that exhibit the best performance. The aim is to find the assignment that minimizes a weighted sum of the unsatisfied constraints. To this end, we use an autonomous binary version of the bat algorithm, which is an optimization technique inspired by the bio-sonar behaviour of microbats, to find the best set of potential solutions without requiring any expert user knowledge to achieve an efficient solution process. To validate our proposal, we use our model to solve problem instances based on data from several hospitals, and we perform a detailed comparative statistical analysis with a traditional constraint programming solver and several well-known optimization algorithms, including the classic bat algorithm. Promising results show that our approach is capable of efficiently solving 30 instances with decreased solution times.

Milk-run routing and scheduling subject to different pick-up/delivery profiles and congestion-avoidance constraints

Abstract Two kinds of intertwined decisions: the routing decisions, which determine the set of sequences of stations visited by each tugger train’s route, and the scheduling decisions, which plan congestion-free movements of tugger train fleets, are considered. The problem under study can be seen as extension of the pick-up and delivery problem with time windows in which different profiles of separately executed delivery and pick-up operations are assumed. The NP-hard character of the problem considered follows from its roots derived from the vehicle routing and the deadlock-avoidance problems. In this regard, a constraint programming paradigm allowing the further integration of multi-period, multi-trip and multi-commodity flows with various customers’ demands as well as distribution network topology constraints is applied. Consequently, a recursive formulation of a well-known constraint satisfaction problem is proposed. The computer experiments provided illustrate the possibility of using the approach presented in systems of real-life scale.

Business Process Configuration According to Data Dependency Specification

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.

Temporal constrained objects for modelling neuronal dynamics.

BackgroundSeveral new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model.MethodsIn this paper, we propose a novel programming paradigm, called temporal constrained objects, which facilitates a principled approach to modelling complex dynamical systems. Temporal constrained objects are an extension of constrained objects with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation.ResultsWe identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of temporal constrained objects. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that temporal constrained objects provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron.DiscussionTemporal constrained objects provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of temporal constrained objects lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.

An effective heuristic for the single-machine scheduling problem with flexible maintenance under human resource constraints

In this paper, we study a new scheduling problem that considers both production and flexible preventive maintenance on a single machine where the human resource constraints (the availability and the competence) are taken into account. The objective function involves both the tardiness and the earliness resulting from production and maintenance tasks. We propose a mathematical formulation of the studied problem that is expressed in the constraint programming (CP) paradigm as a set of linear constraints. This CP modeling had been implemented in ILOG OPL language and the exact method Cplex is applied on it to compute the optimal solutions of relatively small instances of the problem. Further, a heuristic algorithm is provided to deal with lager instances of the problem. Computational experiments demonstrate that the proposed heuristic performs well and is able to find good solutions to instances up to 700 jobs in a reasonable CPU time.

A certified Branch & Bound approach for reliability-based optimization problems

Reliability-based optimization problems are optimization problems considering a constraint that measures reliability of the modelled system: the probability of a safety event with respect to controllable decision variables and uncertain random variables. Most solving approaches use approximate techniques for evaluating this reliability constraint. As a consequence, the reliability of the computed optimal decision is not guaranteed. In this paper, we investigate an interval-based Branch & Bound for solving globally reliability-based optimization problems with numerical guarantee. It combines an interval Branch & Bound framework with a certified reliability analysis technique. This technique considers the reliability constraint and induced safety region modelled within Probabilistic Continuous Constraint Programming paradigm. The certified reliability analysis is numerically handled by an interval quadrature algorithm. In addition, a new interval quadrature function for two random variables, based on linear models of the safety region is described. Two implementations of the Branch & Bound, which differ on how the certified reliability analysis is handled throughout the optimization process, are presented. A numerical study of these two variants shows the relevance of the interval linear model-based quadrature function.

Anticipating anonymity in screening program databases

In this paper, we propose a technique for improving anonymity in screening program databases to increase the privacy for the participants in these programs. The data generated by the invitation process (screening centre, appointment date) is often made available to researchers for medical research and for evaluation and improvement of the screening program. This information, combined with other personal quasi-identifiers such as the ZIP code, gender or age, can pose a risk of disclosing the identity of the individuals participating in the program, and eventually their test results. We present two algorithms that produce a set of screening appointments that aim to increase anonymity of the resulting dataset. The first one, based on the constraint programming paradigm, defines the optimal appointments, while the second one is a suboptimal heuristic algorithm that can be used with real size datasets. The level of anonymity is measured using the new concept of generalized k-anonymity, which allows us to show the utility of the proposal by means of experiments, both using random data and data based on screening invitations from the Norwegian Cancer Registry.

Operating room management under uncertainty

The operating room management problems are legion. This paper tackles the scheduling of surgical procedures in an operating theatre containing up to two operating rooms and two surgeons. We first solve a deterministic version that uses the constraint programming paradigm and then a stochastic version which embeds the former in a sample average approximation scheme. The latter produces more robust schedules that cope better with the surgeries' time variability

Automating correctness verification of artifact-centric business process models

ContextThe artifact-centric methodology has emerged as a new paradigm to support business process management over the last few years. This way, business processes are described from the point of view of the artifacts that are manipulated during the process. ObjectiveOne of the research challenges in this area is the verification of the correctness of this kind of business process models where the model is formed of various artifacts that interact among them. MethodIn this paper, we propose a fully automated approach for verifying correctness of artifact-centric business process models, taking into account that the state (lifecycle) and the values of each artifact (numerical data described by pre and postconditions) influence in the values and the state of the others. The lifecycles of the artifacts and the numerical data managed are modeled by using the Constraint Programming paradigm, an Artificial Intelligence technique. ResultsTwo correctness notions for artifact-centric business process models are distinguished (reachability and weak termination), and novel verification algorithms are developed to check them. The algorithms are complete: neither false positives nor false negatives are generated. Moreover, the algorithms offer precise diagnosis of the detected errors, indicating the execution causing the error where the lifecycle gets stuck. ConclusionTo the best of our knowledge, this paper presents the first verification approach for artifact-centric business process models that integrates pre and postconditions, which define the behavior of the services, and numerical data verification when the model is formed of more than one artifact. The approach can detect errors not detectable with other approaches.