To acquire a competitive advantage in the expanding market, manufacturing enterprises should be able to manage their supply chains as effectively as possible. It is now becoming popular to model supply chains as multi-agent systems and use discrete event simulation to learn more about their behaviors or investigate the implications of alternative configurations. In order to enhance the computational efficiency and keep the simulation credibility, this paper proposes a message-driving formalism for the simulation of multi-agent supply chain systems. Through the message-driving formalism, the problem of shared variables is addressed and the parallel operation of agents is implemented. Simulation experiments with a prototype implementation show that the message-driving formalism is able to provide credible results in significantly less simulation time.

This paper discusses a class of interval alignment (IA) scheduling policies, which are particularly effective for the systems that do not have Markovian structure. The numerical results show that IA policies effectively smooth part flows, improve performance and decrease average Work-in-Process (WIP) by adding intermediate delays to the system. The boundary of IA policy is proven and the applications of IA policy in the system with multiple stream arrivals have been discussed. With the combination of release policy, it is practical to implement IA to multiple stream arrival system.

In the automobile industry, especially in its modern era, large amount of technologies have been generated to produce automobiles. The technological evolution in this industry is formed by complicated effects of the emergence of some milestone inventions and interaction, integration, and succession among diverse technologies. It’s a big challenge to sort out crucial inventions and technologies progresses that mainly form this industry’s technological evolution. We use patent citation data and apply network analytical techniques to reveal characteristics of the “backbone” in the automobile industry’s technological evolution. We employ three algorithms respectively to explore the main path of the technological evolution, the most important subnetwork which outlines the main characteristics of the industry’s technological evolution, and the most important technological inventions (act as authorities and hubs of the technological evolution) in the industry. Main results are reported in detail by tables, figures and interpretations to disclose the most influential technologically developing path, pivotal transfers in technological trajectories, and important technological convergences and divergences over time, of the modern era automobile industry.

The green environmental laws and regulations are legislated, implemented, and enforced in many countries and economic regions. The provision of green products and services are the fast growing trend in global consumer markets. Therefore, introducing new products with environmental considerations becomes critical for global brand manufacturers. This research depicts an integrated and intelligent eco- and inno-product design methodology to support environmental friendly green product development. The methodology adopts approaches, such as life cycle assessment (LCA), quality function deploymnet for environement (QFDE), theory of inventive problem solving (TRIZ) and back-propagation network (BPN) to achieve eco- and inno-design objectives. LCA evaluates and compares the environmental impacts of production. QFDE transforms high-level concerns of environment into design requirements. When there are many historical QFDE data, the BPN prediction model is trained and deployed to automate the specifications of green design improvement. TRIZ is to support the creation of innovative product design ideas effectively and efficiently during the concept design stage. Finally, this paper presents two eco-design cases of power adaptor to demonstrate the proposed methodology.

This paper presents a case study for the advanced planning and scheduling (APS) problem encountered in a light source manufacturer. The APS problem explicitly considers due dates of products, operation sequences among items, and capacity constraints of the manufacturing system. The objective of the problem is to seek the minimum cost of both production idle time and tardiness or earliness penalty of an order. An intelligent heuristic is applied to the problem, and the results demonstrate that significant production performances can be achieved while ensuring customer satisfaction as opposed to normal practices followed in the company relying on human expertise.

In this paper, we consider the numerical inversion of a variety of generating functions (GFs) that arise in the area of engineering and non-engineering fields. Three classes of GFs are taken into account in a comprehensive manner: classes of probability generating functions (PGFs) that are given in rational and non-rational forms, and a class of GFs that are not PGFs. Among others, those PGFs that are not explicitly given but contain a number of unknowns are largely considered as they are often encountered in many interesting applied problems. For the numerical inversion of GFs, we use the methods of the discrete (fast) Fourier transform and the Taylor series expansion. Through these methods, we show that it is remarkably easy to obtain the desired sequence to any given accuracy, so long as enough numerical precision is used in computations. Since high precision is readily available in current software packages and programming languages, one can now lift, with little effort, the so-called Laplacian curtain that veils the sequence of interest. To demonstrate, we take a series of representative examples: the PGF of the number of customers in the discrete-time GeoX/Geo/c queue, the same in the continuous-time MX/D/c queue, and the GFs arising in the discrete-time renewal process.

This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree (MST) is proposed. Daily data of the 30 stocks of the Hang Seng Index (HSI) and Dow Jones Index (DJI) from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that (1) although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and (2) the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.