We address the problem of determining the sequence of a vehicle with fixed capacity to visit n nodes at which a predetermined amount is picked up and/or delivered. The objective is to minimize the total travel distance of the vehicle, while satisfying the pick-up/delivery requirements and feasibility at all nodes. Existing methods for the problem allows the vehicle to visit a node twice, which is impractical in many real situations. We propose a heuristic algorithm, in which every node is visited exactly once. Computational results using random problems indicate that the proposed heuristic outperforms existing methods for practical range of the number of nodes in reasonable computation time.

In a flexible manufacturing system, a cell controller is able to identify and evaluate a number of alternative decisions to meet the objectives set by the factory level controller. In this paper, a Petri net-based cell controller is presented to accomplish this task. A static model is developed by using the Integrated Computer Aided Definition(IDEF0) method to represent clear functional relationships among the objects of the system. Based on the static model, two Petri net models are developed for the physical part flow and for the information flow. Multiple decision alternatives are generated from the physical part flow model and are synchronized with the information flow model for execution of the selected alternative.

This paper applies the binary tree classifier and discriminant analysis methods to predicting failures of banks and insurance companies. In this study, discriminant analysis is generally better than the binary tree classifier in the classification of bank defaults; the binary tree is generally better than discriminant analysis in the classification of insurance company defaults. This situation can be explained that the performance of a classifier depends on the characteristics of the data. If the data are dispersed appropriately for the classifier, the classifier will show a good performance. Otherwise, it may show a poor performance. The two data sets (bank and insurance) are analyzed to explain the better performance of the binary tree in insurance and the worse performance in bank; the better performance of discriminant analysis in bank and the worse performance in insurance.

An EOQ-like inventory model for a manufacturing process is studied. The system is assumed to deteriorate during the production process. The results are either the production of a number of defective items, or the breakdown of the production machine. The optimal production lot size is derived. The model is extended to the case in which the probabilities of making defective items and machine breakdowns are a function of both the quantity (amount) and quality (performance) of the consumed setup cost (including the preventive maintenance cost). We further assume that the setup performance can be improved by investing in the performance improvement program. Hence, the same or a better setup outcome can be achieved with a lower setup cost. We then investigate the optimal setup cost and investment policy simultaneously, thereby achieving a better process quality and setup cost reduction concurrently.

Tabu search (TS) has been successfully applied for solving many complex combinatorial optimization problems in the areas of operations research and production control. However, TS is for single-objective problems in its present form. In this article, a TS-based heuristic is developed to determine Pareto-efficient solutions to a multi-objective combinatorial optimization problem. The developed algorithm is then applied to the discrete optimal design problem in statistics to demonstrate its usefulness.