Semiconductor manufacturing with the dedicated machine constraint is a new challenge for scheduling problems. Previous studies have either not taken this constraint into account or proposed heuristic approaches which might not provide the efficient makespan and the efficient time. This paper proposes a goal-oriented approach called the optimization of makespan, load, and execution time (OMLE) approach which considers the above issues. The desired goals find a schedule for machines which simultaneously optimizes the makespan and the load among machines in an efficient time. Experiments are presented to validate the approach.

This paper proposes a graph framework to undertake the issue of scheduling for dedicated machine constraint in the semiconductor manufacturing system. By finding the shortest paths in the graph, the framework finds the best scheduling result with an optimal makespan for the system. The framework first constructs a graph based on the current situation and then schedules wafers to machines under the constraint. Experiments are presented to validate the proposed graph framework.

We recently proposed the Edgewise Greedy Algorithm (EGA) for learning a decomposable Markov network of treewidth k approximating a given joint probability distribution of n discrete random variables. The main ingredient of our algorithm is the stepwise forward selection algorithm (FSA) due to Deshpande, Garofalakis, and Jordan. EGA is an efficient alternative to the algorithm (HGA) by Malvestuto, which constructs a model of treewidth k by selecting hyperedges of order k+1. In this paper, we present results of empirical studies that compare HGA, EGA and FSA-K which is a straightforward application of FSA, in terms of approximation accuracy (measured by KL-divergence) and computational time. Our experiments show that (1) on the average, all three algorithms produce similar approximation accuracy; (2) EGA produces comparable or better approximation accuracy and is the most efficient among the three. (3) Malvestuto's algorithm is the least efficient one, although it tends to produce better accuracy when the treewidth is bigger than half of the number of random variabls; (4) EGA coupled with local search has the best approximation accuracy overall, at a cost of increased computation time by 50 percent.

We propose an integer programming (IP) framework to undertake the dedicated photolithography machine constraint in semiconductor manufacturing. The constraint is one of the new challenges set by the process engineer in semiconductor manufacturing due to natural bias of photolithography machines. Previous researches either did not take the constraint into account or the proposed heuristic approach might not efficiently fit the fast-changing market of semiconductor manufacturing. In this paper, the proposed IP framework provides an approach to minimize the production cost in an efficient time. We also present the experiments to validate the approach.

We present a multiagent scheduling (MS) system to tackle the dedicated machine constraint in this paper. The dedicated machine constraint is one of the new issues of the photolithography machinery due to natural bias. Natural bias will impact the alignment of patterns between different photolithography layers. The dedicated machine constraint is the most important challenge to improve productivity and fulfill the request for customers in semiconductor manufacturing today. In this paper, the proposed MS system is based on a resource schedule and execution matrix (RSEM) and keeps the load balancing among photolithography machines during each scheduling step according to the current load among the photolithography machines in the production line. We describe the prototype system including the agents and the coordination strategies in the paper. We also demonstrate the simulation result that validated the proposed MS system.

We propose a heuristic load balancing (LB) scheduling approach based on a resource schedule and execution matrix (RSEM) to tackle the dedicated machine constraint for the photolithography process in semiconductor manufacturing. The constraint of having a dedicated machine is one of the new challenges introduced in photolithography machinery due to natural bias. With this dedicated machine constraint, if we randomly schedule the wafer lots to arbitrary photolithography machines at the first photolithography stage, then the load of all photolithography machines might become unbalanced. However, many scheduling policies or modeling methods proposed by previous research for the semiconductor manufacturing production have not discussed this dedicated machine constraint. In this paper, along with providing the LB approach to the issue of the dedicated machine constraint, we also present a novel model-the representation and manipulation methods for the task patterns. The advantage of LB is to easily schedule the wafer lots by simple calculation on a two-dimensional matrix. We present the result of the simulations to validate our approach as well.

CRM is a strategy that integrates concepts of knowledge management, data mining, and data warehousing in order to support an organization’s decision-making process to retain long-term and profitable relationships with its customers. This research is part of a long-term study to examine systematically CRM factors that affect design decisions for CRM data warehouses in order to build a taxonomy of CRM analyses and to determine the impact of those analyses on CRM data warehousing design decisions. This article presents the design implications that CRM poses to data warehousing and then proposes a robust multidimensional starter model that supports CRM analyses. Additional research contributions include the introduction of two new measures, percent success ratio and CRM suitability ratio by which CRM models can be evaluated, the identification of and classification of CRM queries, and a preliminary heuristic for designing data warehouses to support CRM analyses.

CRM is a strategy that integrates the concepts of Knowledge Management, Data Mining, and Data Warehousing in order to support the organization's decision-making process to retain long-term and profitable relationships with its customers. In this paper, we first present the design implications that CRM poses to data warehousing, and then propose a robust multidimensional starter model that supports CRM analyses. We then present sample CRM queries, test our starter model using those queries and define two measures (% success ratio and CRM suitability ratio) by which CRM models can be evaluated. We finally introduce a preliminary heuristic for designing data warehouses to support CRM analyses. Our study shows that our starter model can be used to analyze various profitability analyses such as customer profitability analysis, market profitability analysis, product profitability analysis, and channel profitability analysis.

Accurate summary data is of paramount concern in data warehouse systems; however, there have been few attempts to completely characterize the ability to summarize measures. The sum operator is the typical aggregate operator for summarizing the large amount of data in these systems. We look to uncover and characterize potentially inaccurate summaries resulting from aggregating measures using the sum operator. We discuss the effect of classification hierarchies, and non-, semi-, and fully- additive measures on summary data, and develop a taxonomy of the additive nature of measures. Additionally, averaging and rounding rules can add complexity to seemingly simple aggregations. To deal with these problems, we describe the importance of storing metadata that can be used to restrict potentially inaccurate aggregate queries. These summary constraints could be integrated into data warehouses, just as integrity constraints and are integrated into OLTP systems. We conclude by suggesting methods for identifying and dealing with non- and semi- additive attributes.

This work describes an integration of an entity/relationship model and fuzzy databases. It outlines a formal model for representing fuzziness in the ER model, and sketchs a version of the ER algebra, adapted to manipulating fuzzy databases.

There are many versions of Entity-Relationship (ER) Models. This paper proposes an algebra for a binary ER model with directional relationships. The proposed algebra can be used as the basis of a data manipulation language for an ER database management system.

In this paper a model is developed for the optimization of distributed information systems. Compared with the previous work in this area, the model is more complete, since it considers simultaneously the distribution of processing power, the allocation of programs and databases, and the assignment of communication line capacities. It also considers the return flow of information, as well as the dependencies between programs and databases. In addition, an algorithm, based on the "bounded branch and bound" integer programming technique, has been developed to obtain the optimal solution of the model. The algorithm is more efficient than several existing general nonlinear integer programming algorithms. Also, it avoids some of the disadvantages of heuristic and decomposition algorithms which are used widely in the optimization of computer networks and distributed databases. The algorithm has been implemented in Fortran, and the computation times of the algorithm for several test problems have been found very reasonable.

A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about the real world. A special diagrammatic technique is introduced as a tool for database design. An example of database design and description using the model and the diagrammatic technique is given. Some implications for data integrity, information retrieval, and data manipulation are discussed. The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented.

A data model, called the entity-relationship model, which incorporates the semantic information in the real world is proposed. A special diagramatic technique is introduced for exhibiting entities and relationships. An example of data base design and description using the model and the diagramatic technique is given. The implications on data integrity, information retrieval, and data manipulation are discussed.