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The purpose of this paper is to detect the degenerate scale of a 2D bending plate analytically and numerically.

To avoid the time-consuming scheme, the influence matrix of the boundary element method (BEM) is reformulated to an eigenproblem of the 4 by 4 matrix by using the scaling transform instead of the direct-searching scheme to find degenerate scales. Analytical degenerate scales are derived from the boundary integral equation (BIE) by using the degenerate kernel only for the circular case. Numerical results of the direct-searching scheme and the eigen system for the arbitrary shape are also considered.

Results using three methods, namely, analytical derivation, the direct-searching scheme and the 4 by 4 eigen system, are also given for the circular case and arbitrary shapes. Finally, addition of a constant for the kernel function makes original eigenvalues (2 real roots and 2 complex roots) of the 4 by 4 matrix to be all real. This indicates that a degenerate scale depends on the kernel function.

The analytical derivation for the degenerate scale of a 2D bending plate in the BIE is first studied by using the degenerate kernel. Through the reformed eigenproblem of a 4 by 4 matrix, the numerical solution for the plate of an arbitrary shape can be used in the plate analysis using the BEM.

Three types of industrial buyer-seller relational process models are available: joining theory, stage theory, and state theory. However, historically, these models have developed based on the knowledge and cultural context of the Western world. Several researchers note that national culture may have an impact on international industrial buyer-seller relationships. Including culture in the models is highly important, especially as the business environment is increasingly more global and different countries have different business cultures. The goal of this paper is to define the most suitable industrial buyer-seller relational process models for describing relationships in various contexts. The paper includes a through literature review and a single case study in order to reach this objective. A new state theory model evolved during the research. It consists of two beginning states: searching and starting; four purely middles states: constant/static, decline, growth, and troubled; and a purely end state: termination. The state of dormant/inert is both a middle state and an end state, that is, when the relational actors are not in contact does not mean that the relationship has ended, but instead, for example, new legislation may have been implemented, which requires the actors to evaluate their relationship and its future. A relationship goes through the two beginning states in the order mentioned above, but after that, any state may occur.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

This paper provides a fuller characterization of the analytical upper bounds for American options than has been available to date. We establish properties required of analytical upper bounds without any direct reliance on the exercise boundary. A class of generalized European claims on the same underlying asset is then proposed as upper bounds. This set contains the existing closed form bounds of Margrabe, (1978) and Chen and Yeh (2002) as special cases and allows randomization of the maturity payoff. Owing to the European nature of the bounds, across-strike arbitrage conditions on option prices seem to carry over to the bounds. Among other things, European option spreads may be viewed as ratio positions on the early exercise option. To tighten the upper bound, we propose a quasi-bound that holds as an upper bound for most situations of interest and seems to offer considerable improvement over the currently available closed form bounds. As an approximation, the discounted value of Chen and Yeh's (2002) bound holds some promise. We also discuss implications for parametric and nonparametric empirical option pricing. Sample option quotes for the European (XEO) and the American (OEX) options on the S&P 100 Index appear well behaved with respect to the upper bound properties but the bid–ask spreads are too wide to permit a synthetic short position in the early exercise option.

Resource reconfiguration enables firms to adapt in dynamic environments by supplementing, removing, recombining, or redeploying resources. Whereas prior research has underscored the merits of resource reconfiguration and the modes for implementing it, little is known about the antecedents of this practice. According to prior research, under given industry conditions, resource reconfiguration is prompted by a firm’s corporate strategy and by characteristics of its knowledge assets. We complement this research by identifying learning from performance feedback as a fundamental driver of resource reconfiguration. We claim that performance decline relative to aspiration motivates the firm’s investment in knowledge reconfiguration, and that this investment is reinforced by the munificence of complementary resources in its industry, although uncertainty about the availability of such resources limits that investment. Testing our conjectures with a sample of 248 electronics firms during the period 1993–2001, we reveal a clear distinction between exploitative reconfiguration, which combines existing knowledge elements, and exploratory reconfiguration, which incorporates new knowledge elements. We demonstrate that performance decline relative to aspiration motivates a shift from exploitative reconfiguration to exploratory reconfiguration. Moreover, munificence of complementary resources mitigates the tradeoff between exploratory and exploitative reconfigurations, whereas uncertainty weakens the motivation to engage in both types of reconfiguration, despite the performance gap. Nevertheless, codeployment, which extends the deployment of knowledge assets to additional domains, is more susceptible to uncertainty than redeployment, which withdraws those assets from their original domain and reallocates them to new domains. Our study contributes to emerging research on resource reconfiguration, extends the literature on learning from performance feedback, and advances research on balancing exploration and exploitation.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

The research of unmanned air/ground vehicle (UAV/UGV) cooperation has attracted much attention due to its potential applications in disaster rescue and target surveillance. This paper aims to focus on the UAV/UGV cooperative target tracking and enclosing, considering the limits of detection and sensor failures.

The UAV/UGV cooperation structure is designed, contributing to homogeneous consistency and heterogeneous communication. The target tracking of UAVs is converted into a constraint optimization problem involving tracking cost, and the target enclosing of UGVs is modeled as formation control.

The energy estimation pigeon-inspired optimization is developed to generate control inputs for UAVs. And the controller combined with switchable topology is proposed, where the switching rule is flexible in dealing with some emergencies.

The proposed structure and algorithms can be easily applied to practice and help design the UAV/UGV control system.

The energy estimation mechanism is proposed for the target tracking of UAVs, and the rules of switching topologies ensure the target enclosing process of UGVs.