Narrow Results

This paper considers minimizing total completion time in a two-stage flowshop scheduling problem with m identical parallel machines at Stage 1 and a batch processor at Stage 2. We prove that the problem when all jobs have same processing time at Stage 2 is NP – hard and gave a two-approximation algorithm in O(n³) time. In the case that all jobs have arbitrary processing time at Stage 1 and at Stage 2, we give an approximation algorithm after pointing out that in this case the problem is strongly NP – hard. Hundreds of instances of a numerical experiment show that the worst-case ratio of this approximation algorithm is nearing 2.

This paper studies two types of reverse 1-center problems under uniform linear cost function where edge lengths are allowed to reduce. In the first type, the aim is that the objective value is bounded by a prescribed fixed value $p$ at minimum cost. The aim of the other is to improve the objective value as much as possible within a given budget. An algorithm based on dynamic programming is proposed to solve the first problem in linear time. Then, this algorithm is applied as a subroutine to design an algorithm to solve the second type of the problem in $O(nlog(nK))$ time in which $K$ is a fixed number dependent on the problem parameters. Under the similarity assumption, this algorithm has a better complexity than the Nguyen algorithm (2013) with quadratic-time complexity. Some numerical experiments are conducted to validate this fact in practice.

Leadership theories have evolved over the decades, reflecting its complex and multifaceted nature. This narrative literature review aims to analyze significant leadership literature in popular leadership course books and published peer-reviewed English articles, from which future research directions can be extrapolated. Various development stages of leadership, which hold different research values, are examined and discussed. Since some emerging theories appear to be understudied (shared/distributive leadership, complexity theory of leadership, etc.), there are arguably much scope and opportunities for these to be developed in the future.

In this paper, the author describes the fundamental theory and research methodologies in the field of fictitious economy. Fictitious economy refers to all activities of fictitious capital mainly based on financial platform. Compared with real economy, fictitious economy is another economic pattern, including its structure and evolution, existing at economic system, which can be viewed as “software” of economy. Although the concept of fictitious capital was initiated by Karl Marx, it has been expanded to include credit capital, knowledge capital, and social capital. According to this, the development of fictitious economy has five stages: (i) capitalization of spare money, (ii) socialization of profitable capital, (iii) marketization of priced security, (iv) internationalization of financial market, and (v) integration of global finance. The exchange and reexchange are a major movement of fictitious capital. While the uncertain price of fictitious capital creates the possibility of profitable investment, its expansion produces risk and its movement could not directly increase social wealth. The system of fictitious economy has five characteristics such as complexity, stability, high-risk, parasitism, and periodicity. This paper outlines the challenging research problems, including relationships between fictitious economy and real economy, regulatory factors, risk analysis and prevention, and evaluation system in fictitious economy. In addition, it elaborates on six research methods, known as complexity science, decision making under uncertainty, group decision making, complex data analysis for decision support, mathematical finance, and computer simulation to deal with fictitious economy problems.