Data Mining for Production Scheduling

Data mining is a fast growing field and many industrial engineering applications generate large amounts of data to which data mining techniques can be applied. In this paper we develop a data mining framework for production scheduling. This involves preprocessing of historic schedules into an appropriate data file, discovery of key scheduling concepts, and representation of the data mining results in a way that enables its use for job scheduling

Scalable Optimization-Based Feature Selection Using Random Sampling

We analyze an optimization-based approach called the NP-Filter for feature selection and show how the scalability of this method can be improved using random sampling of instances from the training data. The NP-Filter has attractive theoretical properties as the final solution quality can be quantified and it is flexible in terms of incorporating various feature evaluation methods. We show how the NP-Filter can automatically adjust to the randomness that occurs when a sample of training instances is used, and present numerical results that illustrate both this key result and the scalability improvement that are obtained

Data clustering using proximity matrices with missing values

In most applications of data clustering the input data includes vectors describing the location of each data point, from which distances between data points can be calculated and a proximity matrix constructed. In some applications, however, the only available input is the proximity matrix, that is, the distances between each pair of data point. Several clustering algorithms can still be applied, but if the proximity matrix has missing values no standard method is directly applicable. Imputation can be done to replace missing values, but most imputation methods do not apply when only the proximity matrix is available. As a partial solution to fill this gap, we propose the Proximity Matrix Completion (PMC) algorithm. This algorithm assumes that data is missing due to one of two reasons: complete dissimilarity or incomplete observations; and imputes values accordingly. To determine which case applies the data is modeled as a graph and a set of maximum cliques in the graph is found. Overlap between cliques then determines the case and hence the method of imputation for each missing data point. This approach is motivated by an application in plant breeding, where what is needed is to cluster new experimental seed varieties into sets of varieties that interact similarly to the environment, and this application is presented as a case study in the paper. The applicability, limitations and performance of the new algorithm versus other methods of imputation are further studied by applying it to datasets derived from three well-known test datasets

biclustermd: An R Package for Biclustering with Missing Values

Biclustering is a statistical learning technique that attempts to find homogeneous partitions of rows and columns of a data matrix. For example, movie ratings might be biclustered to group both raters and movies. biclust is a current R package allowing users to implement a variety of biclustering algorithms. However, its algorithms do not allow the data matrix to have missing values. We provide a new R package, biclustermd, which allows users to perform biclustering on numeric data even in the presence of missing values

An Active Learning Environment in an Integrated Industrial Engineering Curriculum

We are developing a new learning environment that supports a suite of interrelated modules based on real-world scenarios. The primary goals of the project are to integrate industrial engineering courses, improve students’ information technology skills, and enhance students’ problem solving skills. In particular, metacognitive abilities will be strengthened as students apply domain knowledge, data, methods and software tools while monitoring their own solution processes. This paper presents the design of two modules that have been developed

Integrated Curriculum to Improve Engineering Problem Solving

A series of modules based on realistic problems are being developed for our industrial engineering curriculum. These modules require students to use a variety of information technology skills to access, screen and analyze the data available to them. The modules are also designed to help students build relationships among the courses, which they traditionally treat as isolated bodies of knowledge. Students’ engineering problem solving will also be enhanced by the challenge presented by more realistic open-ended problems that are incorporated into the modules. This paper details the results of the latest module used in a Manufacturing Systems Engineering course

The Electronic Learning Portal: An Active Learning Environment for Information Technology Across the Curriculum

We are developing a new system that supports a suite of interrelated computer modules based on real-world scenarios. The primary goals of the project are to integrate industrial engineering courses, improve students’ information technology skills, and enhance students’ problem solving skills. In particular, metacognitive abilities will be strengthened as students apply domain knowledge, data, methods and software tools while monitoring their own solution processes. This paper presents some of our results from a pilot study in a large engineering economic analysis course

Information Technology Based Active Learning: A Pilot Study for Engineering Economy

We have recently designed a learning environment to add practical problem solving, increased information technology content, and active learning to industrial engineering courses. In particular, we have successfully implemented and tested a computer-based module for an undergraduate engineering economy course. In this module, students are required to formulate the problem, devise a plan of action, and derive a final solution using the domain knowledge acquired in class. In addition to improving understanding of the course material, the module is also designed to improve more general cognitive skills and specifically to enhance the metacognitive ability of the participating students. A prototype of the module is currently being used in a classroom setting and we report on our initial experiences and student outcomes. We also discuss how this will be extended to an active learning environment that uses information technology across the curriculum to integrate all required undergraduate courses

The Engineering Learning Portal for Problem Solving: Experience in a Large Engineering Economy Class

In an effort to improve students\u27 problem solving skills with information technology across the industrial engineering curriculum, we created an Internet based problem-solving environment. The module implemented for engineering economy presents a realistic problem that establishes connections with other courses. The design of the learning environment promotes metacognitive skill development by requiring students to explain each major problem solving action taken and to evaluate their own progress toward a solution. Experience in two successive semesters of a large introductory course indicates that information technology can be used effectively to create opportunities for students to collaboratively solve realistic engineering problems, thereby promoting deeper learning and higher order thinking. Greater student engagement and efficient evaluation mechanisms motivate faculty adoption of such a system

Reciprocity or Community? Different Cultural Pathways to Cooperation and Welfare

We compare efficiency-enhancing cooperation and its underlying motives in Iceland and the US. The two countries are distinct along all measures of national culture known to us. They are however both developed democracies with similar GDP/capita (PPP adjusted). These similarities make it possible to hold constant aspects of culture related to wealth and institutions. In an experimental Voluntary Contribution Mechanism (VCM), we prime the participants with different social foci, emphasizing either their directly cooperating team or their wider social unit. With a team focus, cooperation levels do not differ between the two cultures, but this superficial similarity masks deep-seated differences: When the focus is on the wider social unit cooperation increases in Iceland and declines in the US. Both when the contribution levels are the same and when they differ, members of the two cultures differ in their motives to cooperate: Icelanders tend to cooperate unconditionally, and US subjects conditionally with a strong emphasis on reciprocity. Our findings indicate that different cultures can achieve similar economic and societal performance through different cultural norms and suggest that cooperation should be encouraged through culturally tailored persuasion tactics