This paper studies a two-stage production system with n job orders where each job needs two sequential operations. In addition to the two in-house production facilities, the manufacturer has another option of outsourcing some stage-one operations to a remote outside supplier. The jobs with their stage-one operations outsourced are subject to a batch transportation delay from the outside supplier before their respective stage-two operations can be started in-house. The problem is to design an integrated schedule that considers both the in-house production and the outsourcing with the aim of optimally balancing the outsourcing cost and the makespan. The problem is NP-hard. We have developed an optimal algorithm and a heuristic algorithm to solve the problem, and conducted computational experiments to validate our model and algorithms. Our modeling and algorithm framework can be extended to handle other more general cases such as when the outside supplier has a production facility with a different processing efficiency and when there are many outside suppliers on a spot market.

The need to manage water resource within a holistic approach is crucial in Indonesia. Conflict situation which involves a variety of stakeholder needs an appropriate methodology to handle it. The purpose of this paper is to provide an integrated framework of a river basin pollution case in Indonesia. This research try to obtain an understanding of the complexity of inter-relationship between stakeholders of the Citarum River Basin and to give feasible recommendations based on a new developed integrated framework. We first describe the problem in a comprehensive way, then develop a framework to analyze the conflict. Next, we propose a general procedure to apply it, which we call Drama-theoretic Dilemma Analysis (DtDA). After analyzing the conflicts that arise there between stakeholders using DtDA, we will show how to resolve the dilemmas by using holistic as well as intensive approaches. By applying DtDA in stakeholder analysis and resolving the dilemmas that arise in the interactions between them, we can identify barriers for collaboration.

An information theory method is proposed to test the Granger causality and contemporaneous conditional independence in Granger causality graph models. In the graphs, the vertex set denotes the component series of the multivariate time series, and the directed edges denote causal dependence, while the undirected edges reflect the instantaneous dependence. The presence of the edges is measured by a statistics based on conditional mutual information and tested by a permutation procedure. Furthermore, for the existed relations, a statistics based on the difference between general conditional mutual information and linear conditional mutual information is proposed to test the nonlinearity. The significance of the nonlinear test statistics is determined by a bootstrap method based on surrogate data. We investigate the finite sample behavior of the procedure through simulation time series with different dependence structures, including linear and nonlinear relations.

Association rule mining plays an important role in knowledge and information discovery. Often for a dataset, a huge number of rules can be extracted, but many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant rules is a promising approach to solve this problem. However, existing work (Pasquier et al. 2005, Xu & Li 2007) is only focused on single level datasets. In this paper, we firstly present a definition for redundancy and a concise representation called Reliable basis for representing non-redundant association rules, then we propose an extension to the previous work that can remove hierarchically redundant rules from multi-level datasets. We also show that the resulting concise representation of non-redundant association rules is lossless since all association rules can be derived from the representation. Experiments show that our extension can effectively generate multilevel non-redundant rules.

Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R&D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R&D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification (IPC) codes B25C, B25D, B25F (categories for power hand tools) and B24B, C09G and H011 (categories for chemical mechanical polishing) are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.

In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual distribution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.

With the buyer’s market strengthening, retailers have begun to lead in product development by introducing their own private label products. The success of such a new product launch relies on transmission of demand information along a supply chain, yet this new phenomenon has been little researched. This paper attempts to address such an issue by modeling vertical information transmission in a supply chain consisting of a retailer and a manufacturer. The retailer would like to introduce a new private label product and knows the demand of the product to be either high or low, while the manufacturer only knows the prior distribution of the demand type. This study attempts to find whether a wholesale-price contract or a two-part tariff contract can facilitate the manufacturer to identify the demand type. The results show that the two-part tariff contract is always effective in realizing information transmission as long as the retailer’s reserve profit remains within a reasonable range.