This paper studies the coordination effects between stages for scheduling problems where decision-making is a two-stage process. Two stages are considered as one system. The system can be a supply chain that links two stages, one stage representing a manufacturer; and the other, a distributor. It also can represent a single manufacturer, while each stage represents a different department responsible for a part of operations. A problem that jointly considers both stages in order to achieve ideal overall system performance is defined as a system problem. In practice, at times, it might not be feasible for the two stages to make coordinated decisions due to (i) the lack of channels that allow decision makers at the two stages to cooperate, and/or (ii) the optimal solution to the system problem is too difficult (or costly) to achieve.

Two practical approaches are applied to solve a variant of two-stage logistic scheduling problems. The Forward Approach is defined as a solution procedure by which the first stage of the system problem is solved first, followed by the second stage. Similarly, the Backward Approach is defined as a solution procedure by which the second stage of the system problem is solved prior to solving the first stage. In each approach, two stages are solved sequentially and the solution generated is treated as a heuristic solution with respect to the corresponding system problem. When decision makers at two stages make decisions locally without considering consequences to the entire system, ineffectiveness may result — even when each stage optimally solves its own problem. The trade-off between the time complexity and the solution quality is the main concern. This paper provides the worst-case performance analysis for each approach.

A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning) is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.

Output regulation for affine nonlinear systems driven by an exogenous signal is investigated in this paper. In the absence of the standard exosystem hypothesis, we assume availability of the instantaneous values of the exogenous signal and its first time-derivative for use in the control law. For affine nonlinear systems, the necessary and sufficient conditions of the solvability of approximate output regulation problem are obtained. The precise form of the control law is presented under some suitable assumptions.

The idea of the global manufacturing virtual network (GMVN) was introduced in 2000 in order to highlight an emerging as well as an alternative manufacturing system which will have strong impacts on industry and implications to management theory. During the last three years, more than thirty companies — based in the electronics, bio-pharmaceuticals, garment, and home electronics appliance industries — have been studied in the UK and China. The research project addresses the emergence of the collaborative manufacturing phenomenon at three levels — sector, system and enabling technology. This paper summarises preliminary research findings from fieldwork conducted over the last three years. It seeks to clarify the characteristics and functionality of GMVN through the case studies and to contrast it to other types of business model, such as the multinational corporations (MNC) and international strategic alliances (ISAs). It also raises some new research questions and themes for further research into GMVN.

In this paper, we introduce a new job shop model that minimizes a well-motivated inventory measure while assuring on-time job deliveries. For this new problem, we introduce precise notation and formalization. A decomposition scheme is discussed in detail, which is subsequently utilized in a new shifting bottleneck procedure (SBP) for the problem. In addition to SBP, we propose another heuristic method based on successive insertion of operations. Algorithms are fine tuned through experimentation. Moreover, the two heuristic procedures are compared in terms of computation time and solution quality, using disguised actual factory data.

Based on system dynamics approach, SDMUWEIC model is developed in order to evaluate future dynamics of urban water infrastructure development in China. Firstly, this paper presents the basic structure and characteristics of the model, focusing on water infrastructure’s dynamic relationships with population increase, economic development, water resources shortage and water conservation practices. Secondly, model veracity and robustness tests based on behavior reproduction and uncertainty analyses are illustrated. Thirdly, based on the model, future patterns of China’s urban water infrastructure investment requirements are simulated, and effectiveness of two different policy scenarios are evaluated. Finally, conclusions and policy implications are drawn, allowing insights into China’s sustainable water infrastructure policies and managements.

There seems to be a significant gap between the theoretical and the practical aspects of scheduling problems in the job shop environment. Theoretically, scheduling systems are designed on the basis of an optimum approach to the scheduling model. However in the practice, the optimum that is built into the scheduling applications seems to face some challenges when dealing with the dynamic character of a scheduling system, for instance machine breakdown or change of orders. Scheduling systems have become quite complex in the past few years. Competitive business environments and shorter product life cycles are the imminent challenges being faced by many companies these days. These challenges push companies to anticipate a demand driven supply chain in their business environment. A demand-driven supply chain incorporates the customer view into the supply chain processes. As a consequence of this, scheduling as a core process of the demand-driven supply chain must also reflect the customer view. In addition, other approaches to solving scheduling problems, for instance approaches based on human factors, prefer the scheduling system to be more flexible in both design and implementation. After discussion of these factors, the authors propose the integration of a different set of criteria for the development of scheduling systems which not only appears to have a better flexibility but also increased customer-focus.

In dynamic uncertain environments, the investment timing of the firm about adopting the existing new technology is influenced by the rival’s actions and technological progress. This paper employs option games approach to present a simplified duopoly continuous time model of technology adoption. In the model, the irreversible investment in adoption of the existing new technology is in strategic competitive circumstances and facing the threat of a further new technology after the competition setting is established. The purpose of the paper is to examine the effect of technological displacement on firms’ strategic investment. The results show that rapid displacement of the technology encourages the leader’s investment and discourages the follower’s investment. Comparing with the optimal timing without the expectation of a further new technology, the firm hastens to invest when no firm has invested; however, once one firm has invested first, the firm will delay its investment. Using mixed strategy analysis, competitive investment strategies with sequential exercise and simultaneous exercise are derived.