A Model-Driven Approach for Solving the Software Component Allocation Problem

The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by component-based software engineering, finding the optimal allocation of software artifacts to the pool of available devices and computation units could bring many benefits, such as improved quality of service (QoS), reduced energy consumption, reduction of costs, and many others. Therefore, in this paper, we introduce a model-based framework that aims to solve the software component allocation problem (CAP). We formulate it as an optimization problem with either single or multiple objective functions and cover both cases in the proposed framework. Additionally, our framework also provides visualization and comparison of the optimal solutions in the case of multi-objective component allocation. The main contributions introduced in this paper are: (1) a novel methodology for tackling CAP-alike problems based on the usage of model-driven engineering (MDE) for both problem definition and solution representation; (2) a set of Python tools that enable the workflow starting from the CAP model interpretation, after that the generation of optimal allocations and, finally, result visualization. The proposed framework is compared to other similar works using either linear optimization, genetic algorithm (GA), and ant colony optimization (ACO) algorithm within the experiments based on notable papers on this topic, covering various usage scenarios—from Cloud and Fog computing infrastructure management to embedded systems, robotics, and telecommunications. According to the achieved results, our framework performs much faster than GA and ACO-based solutions. Apart from various benefits of adopting a multi-objective approach in many cases, it also shows significant speedup compared to frameworks leveraging single-objective linear optimization, especially in the case of larger problem models.

Using an Improved Differential Evolution for Scheduling Optimization of Dual-Gantry Multi-Head Surface-Mount Placement Machine

The difference between dual-gantry and single-gantry surface-mount placement (SMP) machines is that dual-gantry machines exhibit higher complexity and more problems due to their additional gantry robot, such as component allocation and collision. This paper presents algorithms to prescribe the assembly operations of a dual-gantry multi-head surface-mount placement machine. It considers five inter-related problems: (i) component allocation; (ii) automatic nozzle changer assignment; (iii) feeder arrangement; and (iv) pick-and-place sequence; it incorporates a practical restriction related to (v) component height. The paper proposes a solution to each problem: (i) equalizing “workloads” assigned to the gantries, (ii) using quantity ratio method, (iii) using two similarity measurement mechanisms in a modified differential evolution algorithm with a random-key encoding mapping method that addresses component height restriction, (iv) and a combination of nearest-neighbor search and 2-opt method to plan each placing operation. This study reports an experiment that involved the processing of 10 printed circuit boards and compared the performance of a modified differential evolution algorithm with well-known algorithms including differential evolution, particle swarm optimization, and genetic algorithm. The results reveal that the number of picks, moving distance of picking components, and total assembly time with the modified differential evolution algorithm are less than other algorithms.

Wing component allocation for a morphing variable span of tapered wing using finite element method and topology optimisation – application to the UAS-S4

This work presents the Topology Optimisation of the Morphing Variable Span of Tapered Wing (MVSTW) using a finite element method. This topology optimisation aims to assess the feasibility of internal wing components such as ribs, spars and other structural components. This innovative approach is proposed for the telescopic mechanism of the MVSTW, which includes the sliding of the telescopically extended wing into the fixed wing segment. The optimisation is performed using the tools within ANSYS Mechanical, which allows the solving of topology optimisation problems. This study aims to minimise overall structural compliance and maximise stiffness to enhance structural performance, and thus to meet the structural integrity requirements of the MVSTW. The study evaluates the maximum displacements, stress and strain parameters of the optimised variable span morphing wing in comparison with those of the original wing. The optimised wing analyses are conducted on four wingspan extensions, that is, 0%, 25%, 50% and 75%, of the original wingspan, and for different flight speeds to include all flight phases (17, 34, 51 and 68m/s, respectively). Topology optimisation is carried out on the solid wing built with aluminium alloy 2024-T3 to distribute the wing components within the fixed and moving segments. The results show that the fixed and moving wing segments must be designed with two spar configurations, and seven ribs with their support elements in the high-strain area. The fixed and moving wing segments’ structural weight values were reduced to 16.3 and 10.3kg from 112 to 45kg, respectively. The optimised MVSTW was tested using different mechanical parameters such as strains, displacements and von Misses stresses. The results obtained from the optimised variable span morphing wing show the optimal mechanical behaviour and the structural wing integrity needed to achieve the multi-flight missions.

Apparatus-technological scheme of tin cans scrap recycling with obtaining technical products

The article is devoted to the study of reducing the technogenic load on the environment due to the integrated processing of household metal scrap. A waste-free, resource-saving, and environmentally safe method is proposed for extracting technical products from tin cans scrap - iron (III) oxide, tin (II) complex, suitable for further use, as well as fertilizer for agricultural crops. As a result of theoretical and experimental studies, the direction of cans scrap recycling was selected with an assessment of the parameters and factors affecting the reagent process of scrap disposal. To verify the proposed method for can scrap processing in experimental studies, the reagent method and physical modeling were used together. The processes of the reagent can scrap recycling were studied in a laboratory-scale plant. The results of studies on the reagent can scrap processing with the individual component allocation in the form of their derivatives are presented. A block diagram and a hardware-technological scheme for scrap processing with the receipt of technical products have been developed. The possibility of processing other metal-containing wastes according to the proposed scheme, for example, electrical production, is shown.

Divergent allocation of sperm and the seminal proteome along a competition gradient in Drosophila melanogaster

Sperm competition favors large, costly ejaculates, and theory predicts the evolution of allocation strategies that enable males to plastically tailor ejaculate expenditure to sperm competition threat. While greater sperm transfer in response to a perceived increase in the risk of sperm competition is well-supported, we have a poor understanding of whether males (i) respond to changes in perceived intensity of sperm competition, (ii) use the same allocation rules for sperm and seminal fluid, and (iii) experience changes in current and future reproductive performance as a result of ejaculate compositional changes. Combining quantitative proteomics with fluorescent sperm labeling, we show that Drosophila melanogaster males exercise independent control over the transfer of sperm and seminal fluid proteins (SFPs) under different levels of male–male competition. While sperm transfer peaks at low competition, consistent with some theoretical predictions based on sperm competition intensity, the abundance of transferred SFPs generally increases at high competition levels. However, we find that clusters of SFPs vary in the directionality and sensitivity of their response to competition, promoting compositional change in seminal fluid. By tracking the degree of decline in male mating probability and offspring production across successive matings, we provide evidence that ejaculate compositional change represents an adaptive response to current sperm competition, but one that comes at a cost to future mating performance. Our work reveals a previously unknown divergence in ejaculate component allocation rules, exposes downstream costs of elevated ejaculate investment, and ultimately suggests a central role for ejaculate compositional plasticity in sexual selection.

Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve

Purpose: The aim of this research is to develop a two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve. The first stage model is developed to determine the optimal selection of process/suppliers and the component allocation to those corresponding process/suppliers. The second stage model deals with quality improvement efforts to determine the optimal investment to maximize Return on Investment (ROI) by taking into consideration the learning and forgetting curve.Design/methodology/approach: The research used system modeling approach by mathematically modeling the system consists of a manufacturer with multi suppliers where the manufacturer tries to determine the best combination of their own processes and suppliers to minimize certain costs and provides funding for quality improvement efforts for their own processes and suppliers sides.Findings: This research provides better decisions in make or buy analysis and to improve the components by quality investment considering learning and forgetting curve.Research limitations/implications: This research has limitations concerning investment fund that assumed to be provided by the manufacturer which in the real system the fund may be provided by the suppliers. In this model we also does not differentiate two types of learning, namely autonomous and induced learning.Practical implications: This model can be used by a manufacturer to gain deeper insight in making decisions concerning process/suppliers selection along with component allocation and how to improve the component by investment allocation to maximize ROI. Originality/value: This paper combines two models, which in previous research the models are discussed separately. The inclusions of learning and forgetting also gives a new perspective in quality investment decision.