Multi-Level Composition for Software Federations

Reusability is the holly grail of software engineers. But reusability requires powerful composition mechanisms since the pieces to compose have no reason to match perfectly. Unfortunately, the composition mechanisms available today, mostly method call and component assembly are rather primitive. This paper shows what is the current state of the art in software composition, showing that there is little composition flexibility at code level, even using workflow approaches.The approach presented here claims that composition requires reasoning at different levels of abstraction. Workflow support is first decoupled from real tools, using abstract tool modeling. Contracts have been included to increase the independence between process and tools. Then, we have introduced conceptual abstractions linked by contract to real tools. Finally, we show that it is possible to define composition at the abstract level.The resulting system shows very high adaptability capabilities. Experience shows, however, that to be practical, this approach requires adapted framework and specialized tools. This paper shows the experience gained in implementing many versions of such a framework. The current system is currently in industrial use.     

An improved differential evolution based on roulette wheel selection for shape and size optimization of truss structures with frequency constraints

Structural optimization with frequency constraints is well known as a highly nonlinear and complex optimization problem with many local optimum solutions. Therefore, to solve such problems effectively, designers need to use adequate optimization methods which can make a good balance between the computational cost and the quality of solutions. In this work, a novel differential evolution (DE) is proposed to solve the shape and size optimization problems for truss structures with frequency constraints. The proposed method, called ReDE, is a new version of the DE algorithm with two improvements. Firstly, the roulette wheel selection is employed to choose members for the mutation phase instead of random selection as in the conventional DE. Secondly, an elitist selection technique is applied to the selection phase instead of basic selection to improve the convergence speed of the method. The efficiency and reliability of the proposed method are demonstrated through five numerical examples. Numerical results reveal that the proposed algorithm outperforms many optimization methods in the literature.

     

Performance evaluation of dynamic scheduling approaches in vehicle-based internal transport systems

This paper studies the performance of static and dynamic scheduling approaches in vehicle-based internal transport (VBIT) systems and is one of the first to systematically investigate under which circumstances, which scheduling method helps in improving performance. In practice, usually myopic dispatching heuristics are used, often using look-ahead information. We argue more advanced scheduling methods can help, depending on circumstances. We introduce three basic scheduling approaches (insertion, combined and column generation) for the static problem. We then extend these to a dynamic, real-time setting with rolling horizons. We propose two further real-time scheduling approaches: dynamic assignment with and without look-ahead. The performances of the above five scheduling approaches are compared with two of the best performing look-ahead dispatching rules known from the literature. The performance of the various approaches depends on the facility layout and work distribution. However, column generation, the combined heuristic, and the assignment approach with look-ahead consistently outperform dispatching rules. Column generation can require substantial calculation time but delivers very good performance if sufficient look-ahead information is available. For large scale systems, the combined heuristic and the dynamic assignment approach with look ahead are recommended and have acceptable calculation times.     

An extended cell-based smoothed discrete shear gap method (XCS-FEM-DSG3) for free vibration analysis of cracked Reissner-Mindlin shells

A cell-based smoothed discrete shear gap method (CS-FEM-DSG3) was recently proposed and proven to be robust for free vibration analyses of Reissner-Mindlin shell. The method improves significantly the accuracy of the solution due to softening effect of the cell-based strain smoothing technique. In addition, due to using only three-node triangular elements generated automatically, the CS-FEM-DSG3 can be applied flexibly for arbitrary complicated geometric domains. However so far, the CS-FEM-DSG3 has been only developed for analyzing intact structures without possessing internal cracks. The paper hence tries to extend the CS-FEM-DSG3 for free vibration analysis of cracked Reissner-Mindlin shells by integrating the original CS-FEM-DSG3 with discontinuous and crack−tip singular enrichment functions of the extended finite element method (XFEM) to give a so-called extended cell-based smoothed discrete shear gap method (XCS-FEM-DSG3). The accuracy and reliability of the novel XCS-FEM-DSG3 for free vibration analysis of cracked Reissner-Mindlin shells are investigated through solving three numerical examples and comparing with commercial software ANSYS.     

A global single-loop deterministic approach for reliability-based design optimization of truss structures with continuous and discrete design variables

A single-loop deterministic method (SLDM) has previously been proposed for solving reliability-based design optimization (RBDO) problems. In SLDM, probabilistic constraints are converted to approximate deterministic constraints. Consequently, RBDO problems can be transformed into approximate deterministic optimization problems, and hence the computational cost of solving such problems is reduced significantly. However, SLDM is limited to continuous design variables, and the obtained solutions are often trapped into local extrema. To overcome these two disadvantages, a global single-loop deterministic approach is developed in this article, and then it is applied to solve the RBDO problems of truss structures with both continuous and discrete design variables. The proposed approach is a combination of SLDM and improved differential evolution (IDE). The IDE algorithm is an improved version of the original differential evolution (DE) algorithm with two improvements: a roulette wheel selection with stochastic acceptance and an elitist selection technique. These improvements are applied to the mutation and selection phases of DE to enhance its convergence rate and accuracy. To demonstrate the reliability, efficiency and applicability of the proposed method, three numerical examples are executed, and the obtained results are compared with those available in the literature.     

On-line dispatching rules for vehicle-based internal transport systems

On-line vehicle dispatching rules are widely used in many facilities such as warehouses and manufacturing facilities to control vehicles’ movements. Single-attribute dispatching rules, which dispatch vehicles based on only one parameter, are usually used. However, multi-attribute dispatching rules prove to be better in general. In this paper, we study the impact of reassigning moving vehicles on some good dispatching rules, both single- and multi-attribute, in the literature. Results suggest that reassigning moving-to-park vehicles has a significant positive effect on reducing the average load waiting time. We evaluate the dispatching rules’ performance using the experimental design of a real-life case study. The performance criteria are: minimizing the average load waiting time, keeping the maximum load waiting time as short as possible and utilizing better vehicles. The results show that the combined dispatching rules which integrates multi-attribute dispatching and vehicle reassignment yields the best performance overall.