Randomized Problem-Relaxation Solving for Over-Constrained Schedules

Optimal production planning in the form of job shop scheduling problems (JSSP) is a vital problem in many industries. In practice, however, it can happen that the volume of jobs (orders) exceeds the production capacity for a given planning horizon. A reasonable aim in such situations is the completion of as many jobs as possible in time (while postponing the rest). We call this the Job Set Optimization Problem (JOP). Technically, when constraint programming is used for solving JSSPs, the formulated objective in the constraint model can be adapted so that the constraint solver addresses JOP, i.e., searches for schedules that maximize the number of timely finished jobs. However, also highly specialized solvers which proved very powerful for JSSPs may struggle with the increased complexity of the reformulated problem and may fail to generate a JOP solution given practical computation timeouts. As a remedy, we suggest a framework for solving multiple randomly modified instances of a relaxation of the JOP, which allows to gradually approach a JOP solution. The main idea is to have one module compute subset-minimal job sets to be postponed, and another one effectuating that random job sets are found. Different algorithms from literature can be used to realize these modules. Using IBM’s cutting-edge CP Optimizer suite, experiments on well-known JSSP benchmark problems show that using the proposed framework consistently leads to more scheduled jobs for various computation timeouts than a standalone constraint solver approach.

An Improved Particle Number-Based Oil Spill Model Using Implicit Viscosity in Marine Simulator

Improving the physical realism of oil spill scenes in marine simulators can further enhance the emergency response capabilities of officials in charge and crew members and help reduce losses caused by oil spill disasters. In order to uniformly simulate the spreading, drift, breakup, and merging of oil spills at sea, we propose an improved divergence-free position-based fluid (DFPBF) framework based on the particle number density model. In our DFPBF framework, the governing equations for oil spills and ocean are discretized by Lagrangian particles, and the incompressibility of oil spills and ocean is ensured by solving the divergence-free velocity constraint solver and constant density constraint solver. In order to stably simulate the fate and transport of oil spills with higher viscosity, we introduce an implicit viscosity solution scheme for our DFPBF framework. The implicit solver uses a splitting concept to decouple viscosity solve and adopts an implicit scheme to discretize the integration of viscous force. Moreover, our DFPBF framework can ensure a divergence-free velocity field before applying the implicit viscosity scheme, which avoids the undesired bulk viscosity effects. The simulation results show that our DFPBF framework can stably simulate oil spills of various viscosities, especially high-viscosity crude oils.

Applying matrix factorization to consistency-based direct diagnosis

Configuration systems must be able to deal with inconsistencies which can occur in different contexts. Especially in interactive settings, where users specify requirements and a constraint solver has to identify solutions, inconsistencies may more often arise. In inconsistency situations, there is a need of diagnosis methods that support the identification of minimal sets of constraints that have to be adapted or deleted in order to restore consistency. A diagnosis algorithm’s performance can be evaluated in terms of time to find a diagnosis (runtime) and diagnosis quality. Runtime efficiency of diagnosis is especially crucial in real-time scenarios such as production scheduling, robot control, and communication networks. However, there is a trade off between diagnosis quality and the runtime efficiency of diagnostic reasoning. In this article, we deal with solving the quality-runtime performance trade off problem of direct diagnosis. In this context, we propose a novel learning approach based on matrix factorization for constraint ordering. We show that our approach improves runtime performance and diagnosis quality at the same time.

Modular Constraint Solver Cooperation via Abstract Interpretation

Cooperation among constraint solvers is difficult because different solving paradigms have different theoretical foundations. Recent works have shown that abstract interpretation can provide a unifying theory for various constraint solvers. In particular, it relies on abstract domains which capture constraint languages as ordered structures. The key insight of this paper is viewing cooperation schemes as abstract domains combinations. We propose a modular framework in which solvers and cooperation schemes can be seamlessly added and combined. This differs from existing approaches such as SMT where the cooperation scheme is usually fixed (e.g., Nelson-Oppen). We contribute to two new cooperation schemes: (i) interval propagators completion that allows abstract domains to exchange bound constraints, and (ii) delayed product which exchanges over-approximations of constraints between two abstract domains. Moreover, the delayed product is based on delayed goal of logic programming, and it shows that abstract domains can also capture control aspects of constraint solving. Finally, to achieve modularity, we propose the shared product to combine abstract domains and cooperation schemes. Our approach has been fully implemented, and we provide various examples on the flexible job shop scheduling problem.

NACRE - A Nogood And Clause Reasoning Engine

NACRE, for Nogood And Clause Reasoning Engine, is a constraint solver written in C++. It is based on a modular architecture designed to work with generic constraints while implementing several state-of-the-art search methods and heuristics. Interestingly, its data structures have been carefully designed to play around nogoods and clauses, making it suit- able for implementing learning strategies. NACRE was submitted to the CSP MiniTrack of the 2018 and 2019 XCSP3 [8] competitions where it took the first place. This paper gives a general description of NACRE as a framework. We present its kernel, the available search algorithms, and the default settings (notably, used for XCSP3 competitions), which makes NACRE efficient in practice when used as a black-box solver.

A cognitive robot equipped with autonomous tool innovation expertise

Like a human, a robot may benefit from being able to use a tool to solve a complex task. When an appropriate tool is not available, a very useful ability for a robot is to create a novel one based on its experience. With the advent of inexpensive 3D printing, it is now possible to give robots such an ability, at least to create simple tools. We proposed a method for learning how to use an object as a tool and, if needed, to design and construct a new tool. The robot began by learning an action model of tool use for a PDDL planner by observing a trainer. It then refined the model by learning by trial and error. Tool creation consisted of generalising an existing tool model and generating a novel tool by instantiating the general model. Further learning by experimentation was performed. Reducing the search space of potentially useful tools could be achieved by providing a tool ontology. We then used a constraint solver to obtain numerical parameters from abstract descriptions and use them for a ready-to-print design. We evaluated our system using a simulated and a real Baxter robot in two cases: hook and wedge. We found that our system performs tool creation successfully.

JDart: Dynamic Symbolic Execution for Java Bytecode (Competition Contribution)

JDart performs dynamic symbolic execution of Java programs: it executes programs with concrete inputs while recording symbolic constraints on executed program paths. A constraint solver is then used for generating new concrete values from recorded constraints that drive execution along previously unexplored paths. JDart is built on top of the Java PathFinder software model checker and uses the JConstraints library for the integration of constraint solvers.

A Framework to Reduce the Human Factors for Analysis of Constraint Solvers in Project Management

Currently, there are multiple factors that affect the projects management. These factors may have different origins, but the human factor is still one of the main elements that affect decisions when managing a project. Another important factor is the use of software that supports these decisions and reduce the human factors. Given the complexity of current management problems, powerful software is needed to solve these problems. Constraint solvers are a kind of software that are based on a constraint approach. Currently there are different constraint solvers. Some are intricate software, and others are libraries for a programming language. This chapter presents a framework that allow to compare a constraint system based on the usability attributes of the solvers in order to reduce the human factors for the selection of the constraint solver. The authors show that it is possible to establish a comparison according to usability attributes, allowing to reduce the risks of decision making by the experts when working with a constrain solver in a project.