Using agent development tools to construct an agent-based system is a well applied approach. However, the development tools usually do not have the function to check the feasibility about the workflow of the agent system during it implementation stage. Therefore, to develop an evaluation approach to analyze the feasibility of a developing agent system such that the improper workflow of an agent system can be found in the early design stage is a necessary task to reduce the risk of implementation.

In this research, a Petri Net (PN) based three-stage evaluation approach was developed.

In the conceptual stage, the pitfall of the current agent system developing process was examined and an improvement analysis process was specified. Then, in the system design stage, an evaluation approach which extracted the process log file from a developing agent system into a PN model in terms of a process mining approach-α algorithm was proposed. This model was simulated in a PN simulation package. The agent system performance was evaluated in terms of analyzing the deadlock phenomena of the PN model. Finally, in the implementation stage, the proposed concept was implemented by using an agent developing tool JADE and a PN simulation tool CPN. An agent-based robotic assembly system was used to examine the possible deadlock of the agent system.

This paper focuses on the scheduling problem in assembly islands environment with fixed-position layouts. In such configuration, the product normally remains in one location for its entire manufacturing period while machines, materials and workers are moved to an assembly site called an assembly island. This production layout has some unique features such as moving assembly workers, tools and materials; limited space at assembly site; considerable distance between islands. The authors first give the definition and mathematical model for the scheduling problem and then propose a two-level genetic algorithm to obtain a near optimal solution to minimize the makespan. Experimental results show that this algorithm is effective. The performance analysis of the proposed algorithm indicates that it is more efficient in the airline or shipbuilding industry than in the machine or tool final assembly companies.

Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R&D investments. This research incorporates an ontology schema to extract and represent patent concepts. A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery. The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims, a feature that enables the grouping of patents that define related key innovations. Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation. The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification (RFID) patents and by analyzing the legal implications of automated discovery of patent infringement.

Data mining has been successfully applied in many fields to find useful information stored in vast databases. Market segmentation, which segments data into homogenous clusters by using cluster analysis, is among the most important of the applications in data mining. In this study, we propose a clustering system which integrates particle swarm optimization and honey bee mating optimization methods (PSHBMO). Simulations for a benchmark test function show that our proposed method is better equipped to find the global optimum than other well-known clustering algorithms. Finally, the proposed clustering system is applied to a real-world consumer electronic company to perform market segmentation via the RFM model.

Since collaborative, planning, forecasting, and replenishment (CPFR) was first proposed in 1998, numerous studies have focused on exploring its implementation in retailing contexts. While a considerable body of research has emphasized reduced costs, increased sales and improved forecasting ability, there has been a lack of research on the importance of each of the various factors which affect such implementations. In order to find out the critical success factors affecting CPFR implementation, this paper first collected related influence factors regarding adopting CPFR or business to business (B2B) information systems, and further constructed a factor table with a three-layer hierarchical structure. A pair wise analytic hierarchy process (AHP) questionnaire was designed and distributed to experts who were familiar with implementing CPFR in the retailing industry. After questionnaires were returned, we found out the weights of each impact factor by using a fuzzy analytic hierarchy process (fuzzy AHP) approach. The importance of each critical impact factor was investigated, and the paths of the critical success factors were also analyzed. The results of this study can provide more precise information with regard to allocating optimal resources for retailers implementing CPFR.

We examine a capacitated system exposed to random stepwise capacity disruptions with exponentially distributed interarrival times and uniformly distributed magnitudes. We explore two countermeasure policies for a risk-neutral decision maker who seeks to maximize the long-run average reward. A one-phase policy considers implementation of countermeasures throughout the entirety of a disruption cycle. The results of this analysis form a basis for a two-phase model which implements countermeasures during only a fraction of a disruption cycle. We present an extensive numerical analysis as well as a sensitivity study on the fluctuations of some system parameter values.

We discuss a single-period inventory system with a general S-shaped utility function and exponential demand. It is shown that there exists a state-dependent order-up-to policy to be optimal. Behaviors of optimal policy are illustrated by a numerical example.

The relationship between tacit knowledge and informal networks has not been fully explored. Tacit knowledge cannot be well managed directly, because it is deeply embedded in individual actions and experiences. This study proposes an approach to managing informal networks as a new middle way to facilitate the sharing and transferring of tacit knowledge, owing to the fact that tacit knowledge is mostly shared and transferred through informal networks. To support the idea of the approach, an empirical case study of applying the approach to management of a large scientific project is also included. Our findings suggest that informal networks play essential roles for management of tacit knowledge, and thus good management of informal networks can lead to efficient sharing and transferring of tacit knowledge.