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The purpose of this paper is to develop a new approach called advanced overlapping production planning (AOPP) model which considers multi‐site process selection, sequential constraints, and capacity constraints in a manufacturing supply chain environment (MSCE). AOPP model may determine the capacity plan and order margin allocation for each site and machines in an MSCE and provide the capacity information for a production planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources.

First, an AOPP model is presented to model the production scheduling problem in a supply chain with the objective of minimizing the fulfilling cycle time of each order and the overloads of each machine group. Second, a genetic algorithm (GA)‐based approach for solving the AOPP model is developed. Finally, a heuristic adjustment approach is proposed for planners to adjust the production plan whenever there is an exception of production occurring.

The production schedule obtained from the GA‐based AOPP approach retains order margins in each operation against other overlapping operations, and it satisfies the capacity constraints of each machine group in an MSCE and results in a better performance in process planning and production planning with finite capacity. In practice, the overloading problem can be solved by adding a work shift or working overtime. The GA‐based AOPP model provides useful information for production planners to make such decisions.

Production planners need a more flexible production plan with order margins to compensate for the uncertainties which frequently occur in the supply and demand sides. This research develops a model to help planners manage the order margin of production planning in an MSCE and showing that order margins become a crucial factor for achieving effective production objective in terms of short OTD (or order cycle) time.

The overlapping production planning approach is a useful finite capacity planning approach for handling the capacity and order margin management in certain manufacturing environment (e.g. flow shop), but less on overcoming multi‐site process selection, sequential constraints, and capacity constraints in an MSCE. In addition, the capacity plan and order margin allocation information for each site and facilities are very important for a planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. This research addresses both issues.

The DC‐DC converters which convert one level of electrical voltage to the desired level are widely used in many electrical peripherals. During the past two decade, many different control laws have been developed. The proportional‐integral (PI) control and sliding‐mode control have been carried out for the DC‐DC converters since they are simple to implement and easy to design. However, its performance using PI control and sliding‐mode control is obviously quite limited. The purpose of this paper is to a self‐tuning nonlinear function control (STNFC) propose for the DC‐DC converters. The adaptation laws of the proposed STNFC system are derived in the sense of Lyapunov function, thus not only the controller parameters can be online tuned itself, but also the system's stability can be guaranteed.

In general, the accurate mathematical models of the DC‐DC converters are difficult to derive. This paper proposes a model‐free STNFC design method. Since the proposed STNFC uses a simple fuzzy system with three fuzzy rules base to implement the control law, the computational loading of the fuzzy inference mechanism is slight. So the proposed STNFC system is suitable for the real‐time practical applications. The controller parameters of the proposed STNFC system can online tune in the Lyapunov sense, thus the stability of closed‐loop system can be guaranteed.

The proposed STNFC system is applied to a DC‐DC converter based on a field‐programmable gate array chip. The experimental results are provided to demonstrate the proposed STNFC system can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response.

The proposed STNFC approach is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the successful development of STNFC system without heavy computational loading. The parameter‐learning algorithm is design based on the Lyapunov stability theorem to guarantee the system stability; the successful applications of the STNFC system to control the forward DC‐DC converter. And, the proposed STNFC methodology can be easily extended to other DC‐DC converters.

The purpose of this paper is to discuss the integration of information technology (IT) systems and the strengthening of customer service to raise business competitive advantage.

The case company uses IT systems to integrate customers and suppliers. In this paper, both are interviewed about how they work together for the development and operation of IT systems. A grounded theory approach is followed to develop the theoretical framework from the empirical evidence.

This research regards the whole machine industry as having the following influence, beginning with the related innovation business model. Previously, most machine tool companies played the role of product manufacturer, making high‐quality products the goal. This research indicated that not only can Taiwan manufacture high‐quality products but also that it can create product design and support a global localization service to the world.

In this paper, information architecture planning is designed to strengthen the bonds between the clients and the company.

This paper may help global companies concerned with the development, support, and operation of IT systems to look beyond the systems of their collaborative working to consider the effect of supporting a localization service in their systems.