• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.225-230
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• 2002

The constrained off-line competitive deletion problem is a simple form of the set manipulation operations problem. It excludes the insertion operation from the off-line competitive deletion problem. An optimal sequential algorithm and a CREW PRAM algorithm which runs $O(log^2nloglogn)$ time using O(n/loglogn) processors were already presented in the literature. In this paper, we present a reconfigurable mesh algorithm for the constrained off-line competitive deletion problem. The proposed algorithm is executed in a constant time on an $n{\times}n$ reconfigurable mesh, and the result is $AT^2$ optimal.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.617-620
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• 2000

In this paper, we show an application of constraint logic programming to the operation scheduling on machines in a job shop. Constraint logic programming is a new genre of programming technique combining the declarative aspect of logic programming with the efficiency of constraint manipulation and solving mechanisms. Due to the latter feature, combinatorial search problems like scheduling may be resolved efficiently. In this study, the jobs that consist of a set of related operations are supposed to be constrained by precedence and resource availability. We also explore how the constraint solving mechanisms can be defined over a scheduling domain. Thus the scheduling approach presented here has two benefits: the flexibility that can be expected from an artificial intelligence tool by simplifying greatly the problem; and the efficiency that stems from the capability of constraint logic programming to manipulate constraints to prune the search space in an a priori manner.

• Journal of the Korean Operations Research and Management Science Society
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• v.1 no.1
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• pp.151-170
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• 1976

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be prduced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of products through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No$_{i}$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) No$_{i}$, But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plants shows this point.int.