Simplifying Multiproject Scheduling Problem Based on Design Structure Matrix and Its Solution by an Improved aiNet Algorithm

Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM) and an improved artificial immune network algorithm (aiNet) are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA), simulated annealing algorithm (SA), and ant colony optimization (ACO)

Impact of Food Safety Incident on Consumers\u27 Willingness to Pay: the Case of China

Oral Presentatio

Research on supply chain partner selection and task allocation based on fuzzy theory under an uncertain environment

Nowadays the enterprises pay the closer attention to the relationship among suppliers, manufacturers and distributors due to the global competitive market economy. And they manage the supply chain through establishing strategic cooperative partnership, which can greatly enhance the competitive advantage and obtain greater overall profits. In this paper, the fuzzy theory is applied to study the supply chain partner selection and the task coarse allocation problem in under multi-attribute fuzzy comprehensive decision-making and fuzzy constraints. Finally, the fuzzy comprehensive decision of supply chain network structure was verified through the case of Shaoxing textile.Hoy en día las empresas prestan más atención a la relación entre proveedores, fabricantes y distribuidores debido a la competitiva economía de mercado global. Y manejan la cadena de suministro a través del establecimiento de una asociación estratégica de cooperación, que puede mejorar en gran medida la ventaja competitiva y obtener mayores beneficios en general. En este artículo, la teoría difusa se aplica para estudiar la selección de socios de la cadena de suministro y el problema de asignación de tareas gruesas en la toma de decisiones globales difusas de múltiples atributo y las restricciones difusas. Por último, la decisión global difusa de la estructura de la red de la cadena de suministro se verificó a través del caso de Shaoxing textil

A Dynamic Intelligent Decision Approach to Dependency Modeling of Project Tasks in Complex Engineering System Optimization

Complex engineering system optimization usually involves multiple projects or tasks. On the one hand, dependency modeling among projects or tasks highlights structures in systems and their environments which can help to understand the implications of connectivity on different aspects of system performance and also assist in designing, optimizing, and maintaining complex systems. On the other hand, multiple projects or tasks are either happening at the same time or scheduled into a sequence in order to use common resources. In this paper, we propose a dynamic intelligent decision approach to dependency modeling of project tasks in complex engineering system optimization. The approach takes this decision process as a two-stage decision-making problem. In the first stage, a task clustering approach based on modularization is proposed so as to find out a suitable decomposition scheme for a large-scale project. In the second stage, according to the decomposition result, a discrete artificial bee colony (ABC) algorithm inspired by the intelligent foraging behavior of honeybees is developed for the resource constrained multiproject scheduling problem. Finally, a certain case from an engineering design of a chemical processing system is utilized to help to understand the proposed approach

A possible 250-second X-ray quasi-periodicity in the fast blue optical transient AT2018cow

The fast blue optical transients (FBOTs) are a new population of extragalactic transients of unclear physical origin. A variety of mechanisms have been proposed including failed supernova explosion, shock interaction with a dense medium, young magnetar, accretion onto a compact object, and stellar tidal disruption event, but none is conclusive. Here we report the discovery of a possible X-ray quasi-periodicity signal with a period of $\sim$250 second (at a significance level of 99.76%) in the brightest FBOT AT2018cow through the analysis of XMM-Newton/PN data. The signal is independently detected at the same frequency in the average power density spectrum from data taken from the Swift telescope, with observations covering from 6 to 37 days after the optical discovery, though the significance level is lower (94.26%). This suggests that the QPO frequency may be stable over at least 1.1$\times$ 10$^{4}$ cycles. Assuming the $\sim$250 second QPO to be a scaled-down analogue of that typically seen in stellar mass black holes, a black hole mass of $\sim10^{3}-10^{5}$ solar masses could be inferred. The overall X-ray luminosity evolution could be modeled with the stellar tidal disruption by a black hole of $\sim10^4$ solar masses, providing a viable mechanism to produce AT2018cow. Our findings suggest that other bright FBOTs may also harbor intermediate-mass black holes.Comment: 18 pages, 10 figures. Accepted for publication in Research in Astronomy and Astrophysic