Daniel Riera

Verification of UML/OCL Class Diagrams using Constraint Programming

In the MDD and MDA approaches, models become the primary artifacts of the development process. Therefore, assessment of the correctness of such models is a key issue to ensure the quality of the final application. In that sense, this paper presents an automatic method that uses the Constraint Programming paradigm to verify UML class diagrams extended with OCL constraints. In our approach, both class diagrams and OCL constraints are translated into a Constraint Satisfaction Problem. Then, compliance of the diagram with respect to several correctness proper- ties such as weak and strong satisfiability or absence of constraint redundancies can be formally verified.

UMLtoCSP: a tool for the formal verification of UML/OCL models using constraint programming

We present UMLtoCSP, a tool for the formal verification of UML/OCL models. Given a UML class diagram annotated with OCL constraints, UMLtoCSP is able to automatically check several correctness properties, such as the strong and weak satisfiability of the model or the lack of redundant constraints. The tool uses Constraint Logic Programming as the underlying formalism and the constraint solver ECLiPSe as the verification engine.

On the use of Monte Carlo simulation, cache and splitting techniques to improve the clarke and wright savings heuristics

This paper presents the SR-GCWS-CS probabilistic algorithm that combines Monte Carlo simulation with splitting techniques and the Clarke and Wright savings heuristic to find competitive quasi-optimal solutions to the Capacitated Vehicle Routing Problem (CVRP) in reasonable response times. The algorithm, which does not require complex fine-tuning processes, can be used as an alternative to other metaheuristics—such as Simulated Annealing, Tabu Search, Genetic Algorithms, Ant Colony Optimization or GRASP, which might be more difficult to implement and which might require non-trivial fine-tuning processes—when solving CVRP instances. As discussed in the paper, the probabilistic approach presented here aims to provide a relatively simple and yet flexible algorithm which benefits from: (a) the use of the geometric distribution to guide the random search process, and (b) efficient cache and splitting techniques that contribute to significantly reduce computational times. The algorithm is validated through a set of CVRP standard benchmarks and competitive results are obtained in all tested cases. Future work regarding the use of parallel programming to efficiently solve large-scale CVRP instances is discussed. Finally, it is important to notice that some of the principles of the approach presented here might serve as a base to develop similar algorithms for other routing and scheduling combinatorial problems.

Rich Vehicle Routing Problem: Survey

The Vehicle Routing Problem (VRP) is a well-known research line in the optimization research community. Its different basic variants have been widely explored in the literature. Even though it has been studied for years, the research around it is still very active. The new tendency is mainly focused on applying this study case to real-life problems. Due to this trend, the Rich VRP arises: combining multiple constraints for tackling realistic problems. Nowadays, some studies have considered specific combinations of real-life constraints to define the emerging Rich VRP scopes. This work surveys the state of the art in the field, summarizing problem combinations, constraints defined, and approaches found.

On the verification of UML/OCL class diagrams using constraint programming

Assessment of the correctness of software models is a key issue to ensure the quality of the final application. To this end, this paper presents an automatic method for the verification of UML class diagrams extended with OCL constraints. Our method checks compliance of the diagram with respect to several correctness properties including weak and strong satisfiability or absence of constraint redundancies among others. The method works by translating the UML/OCL model into a Constraint Satisfaction Problem (CSP) that is evaluated using state-of-the-art constraint solvers to determine the correctness of the initial model. Our approach is particularly relevant to current MDA and MDD methods where software models are the primary artifacts of the development process and the basis for the (semi-)automatic code-generation of the final application.

A Literature Review of Gamification Design Frameworks

This paper presents a review of the literature on gamification design frameworks. Gamification, understood as the use of game design elements in other contexts for the purpose of engagement, has become a hot topic in the recent years. However, theres also a cautionary tale to be extracted from Gartners reports on the topic: many gamification-based solutions fail because, mostly, they have been created on a whim, or mixing bits and pieces from game components, without a clear and formal design process. The application of a definite design framework aims to be a path to success. Therefore, before starting the gamification of a process, it is very important to know which frameworks or methods exist and their main characteristics. The present review synthesizes the process of gamification design for a successful engagement experience. This review categorizes existing approaches and provides an assessment of their main features, which may prove invaluable to developers of gamified solutions at different levels and scopes.

Optimization of Logistic and Manufacturing Systems through Simulation: A Colored Petri Net-Based Methodology

Simulation models have proved to be useful for examining the performance of different system configurations and/or alternative operating procedures for complex logistic or manufacturing systems. However, when applying simulation techniques to increase the performance of those systems, several limitations arise due to their inability to evaluate more than a fraction of the immense range of options available. Simulation-optimization is one of the most popular approaches to improve the use of simulation models as a tool to obtain the best (optimal or quasi-optimal) decision variable values that minimize a certain objective function. However, despite the success of several simulation-optimization packages, many technical barriers still remain. The authors describe a new approach to integrate evaluation (simulation) methods with search methods (optimization) based on not only simulation results but also information from the simulation model.

A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times

Abstract This paper describes a simulation–optimization algorithm for the Permutation Flow shop Problem with Stochastic processing Times (PFSPST). The proposed algorithm combines Monte Carlo simulation with an Iterated Local Search metaheuristic in order to deal with the stochastic behavior of the problem. Using the expected makespan as initial minimization criterion, our simheuristic approach is based on the assumption that high-quality solutions (permutations of jobs) for the deterministic version of the problem are likely to be high-quality solutions for the stochastic version – i.e., a correlation will exist between both sets of solutions, at least for moderate levels of variability in the stochastic processing times. No particular assumption is made on the probability distributions modeling each job-machine processing times. Our approach is able to solve, in just a few minutes or even less, PFSPST instances with hundreds of jobs and dozens of machines. Also, the paper proposes the use of reliability analysis techniques to analyze simulation outcomes or historical observations on the random variable representing the makespan associated with a given solution. This way, criteria other than the expected makespan can be considered by the decision maker when comparing different alternative solutions. A set of classical benchmarks for the deterministic version of the problem are adapted and tested under several scenarios, each of them characterized by a different level of uncertainty – variance level of job-machine processing times.

An Improved Hybrid Model for the Generic Hoist Scheduling Problem

The generic hoist scheduling problem is NP-hard and arises from automated manufacturing lines. In recent work using the constraint logic programming (CLP) formalism, a unified model has been developed with the problem description and solution method separated. We provide an improved model and new preprocessing stages where, as before, solutions and proof of optimality are provided by a hybrid CLP–MIP algorithm. The new algorithm is more scalable and robust. We give empirical results for a range of problem classes on benchmark problems from several sources.

SIM-RandSHARP: a hybrid algorithm for solving the Arc Routing Problem with Stochastic Demands

This paper proposes a new hybrid algorithm for solving the Arc Routing Problem with Stochastic Demands (ARPSD). Our approach combines Monte Carlo simulation (MCS) with the RandSHARP algorithm, which is designed for solving the Capacitated Arc Routing Problem (CARP) with deterministic demands. The RandSHARP algorithm makes use of a CARP-adapted version of the Clarke and Wright Savings heuristic, which was originally designed for the Vehicle Routing Problem. The RandSHARP algorithm also integrates a biased-randomized process, which allows it to obtain competitive results for the CARP in low computational times. The RandSHARP algorithm is then combined with MCS to solve the ARPSD. Some numerical experiments contribute to illustrate the potential benefits of our approach.