Riyaz Sikora

A strategic analysis of inter organizational information sharing

In this paper we study the effect of inter organizational information sharing strategies on firm level performance under both stable as well as volatile market conditions. We use information exchange in a supply chain as a representation of inter organizational information sharing, and study five strategies for information sharing that range from minimal to near-complete information exchange. We present analytical evaluation of the relative performance of these strategies and experimental results from a proof-of-concept system. Our results show that near-complete information sharing that combines more than one type of information being shared has better performance in volatile market conditions.

A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes

Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GAs use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.

Coordination mechanisms for multi-agent manufacturing systems: applications to integrated manufacturing scheduling

The advent of information technology (IT) has made todays manufacturing systems increasingly distributed. Typically such a system consists of a complex array of computer-based decision units, controllers and databases. Rather than dealing with each component individually, it is necessary to have a new paradigm for management of manufacturing systems, so that all the components and their operations can be managed in an integrated fashion. The multi-agent framework presented in this paper is such a paradigm for achieving system integration. The authors specifically emphasize the coordination mechanisms needed for ensuring the orderly operations and concerted decision making among the components-i.e., agents-of the manufacturing systems. The application of the framework to a printed circuit board manufacturing system and the performance results are also described.

Framework for efficient feature selection in genetic algorithm based data mining

We present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of feature selection. Explicit feature selection is traditionally done as a wrapper approach where every candidate feature subset is evaluated by executing the data mining algorithm on that subset. In this article we present a GA for doing both the tasks of mining and feature selection simultaneously by evolving a binary code along side the chromosome structure used for evolving the rules. We then present a wrapper approach to feature selection based on Hausdorff distance measure. Results from applying the above techniques to a real world data mining problem show that combining both the feature selection methods provides the best performance in terms of prediction accuracy and computational efficiency.

Assessing the relative influence of journals in a citation network

Some journals are perceived as sources of knowledge; others serve as storers of knowledge. Learning the strengths and persuasions of journals is of value to academia, scholars, and publishers.

A genetic algorithm for integrating lot-sizing and sequencing in scheduling a capacitated flow line

Abstract In this paper we present a genetic algorithm for solving an important but difficult scheduling problem: that of integrating the lot-sizing and sequencing decisions in scheduling a flow line involving sequence dependent setup times, capacity constraints, limited buffer capacity between machines, and due dates. The problem is based on a real world manufacturing facility that is also described. Novel crossover and mutation operators are presented for both the lot-sizing and sequencing parts of the scheduling problem and the performance of the genetic algorithm is compared to a heuristic approach of integration previously shown to have been effective.

Integrating the lot-sizing and sequencing decisions for scheduling a capacitated flow line

Abstract In this paper we consider a general problem of scheduling a single flow line consisting of multiple machines and producing a given set of jobs. The manufacturing environment is characterized by sequence dependent set-up times, limited intermediate buffer space, and capacity constraints. In addition, jobs are assigned with due dates that have to be met. The objectives of the scheduling are: (1) to meet the due dates without violating the capacity constraints, (2) to minimize the makespan, and (3) to minimize the inventory holding costs. While most of the approaches in the literature treat the problem of scheduling in flow lines as two independent sub-problems of lot-sizing and sequencing, our approach integrates the lot-sizing and sequencing heuristics. The integrated approach uses the Silver-Meal heuristic (modified to include lot-splitting) for lot-sizing and an improvement procedure applied to Palmers heuristic for sequencing, which takes into account the actual sequence dependent set-up times and the limited intermedite buffer capacity. We evaluate the performance of the integrated approach and demonstrate its efficacy for scheduling a real world SMT manufacturing environment.

Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance

The development of powerful computers and faster input/output devices coupled with the need for storing and analyzing data have resulted in massive databases (of the order of terabytes). Such volumes of data clearly overwhelm more traditional data analysis methods. A new generation of tools and techniques are needed for finding interesting patterns in the data and discovering useful knowledge. In this paper we present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of self-adaptive feature selection together with a wrapper feature selection method based on Hausdorff distance measure.

A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based Learning

In this paper, we describe a machine learning technique based on a double-layered architecture and Genetic Algorithms (GAs), which can be used to learn decision rules for financial classification. Once the rules have been acquired, they can be stored in an expert system for future application. Genetic algorithms represent a class of learning algorithms modeled on the biological evolution process. Equipped with unique search behavior and solution-seeking properties, they provide an interesting technique for such classification tasks as bankruptcy prediction and credit analysis. However, some modifications to the basic genetic algorithms are necessary in order to make the method suitable for solving these classification problems. One of the objectives of this paper is to develop a representation scheme for the concepts to be learned that can be incorporated in a genetic algorithm. More importantly, we expand on the concept of the genetic algorithm and develop a learning method called the Double-layered Learning System (DLS) that integrates the genetic algorithm with a similarity-based learning technique called the Probabilistic Learning System (PLS1).By combining a learning paradigm that searches through the “hypothesis space” (GA) with a paradigm that searches through the “instance space” (PLS1), DLS permits synergistic improvement of the learning performance. We demonstrate the feasibility of such a hybrid approach computationally by presenting the design, implementation, and performance evaluation of DLS. DLS proves to be an effective improvement over both GA and PLS1. The efficacy of the results points towards the importance of such hybrid approaches in providing more robust machine learning methods. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

A Computational Study of Distributed Rule Learning

This report is concerned with a rule learning system called the Distributed Learning System (DLS). Its objective is two-fold: First, as the main contribution, the DLS as a rule-learning technique is described and the resulting computational performance is presented, with definitive computational benefits clearly demonstrated to show the efficacy of using the DLS. Second, the important parameters of the DLS are identified to show the characteristics of the Group Problem Solving (GPS) strategy as implemented in the DLS. On one hand this helps us pinpoint the critical designs of the DLS for effective rule learning; on the other hand this analysis can provide insight into the use of GPS as a more general rule-learning strategy.