• Korean Management Science Review
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• v.17 no.2
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• pp.125-134
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• 2000

Keeping the promised delivery date for a customer order is crucial for a company to promote customer satisfaction and generate further businesses. For this, a company should be able to quote the delivery date that can be achieved with the capacity available on the shop floor. In a dynamic make-to-order manufacturing environment, the problem of determining a delivery date for an incoming order with consideration of resource capacity, workload, and finished-product inventory can hardly be solved by an analytical solution procedure. This paper considers a situation in which a delivery date for a customer order is determined based on a job schedule, and presents the SimTriD algorithm that provides the best scheduling for determining a delivery date of customer order through the job schedule that efficiently utilizes manufacturing resources with consideration of interacting factors such as resource utilization, finished-product inventory, and due date.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.41-41
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• 1992

In the real world situations that some jobs need be processed only on certain limited machines frequently occur due to the capacity restrictions of machines such as tools fixtures or material handling equipment. In this paper we consider n-job non-preemptive and m parallel machines scheduling problem having two machines group. The objective function is to minimize the sum of earliness and tardiness with different release times and due dates. The problem is formulated as a mixed integer programming problem. The problem is proved to be Np-complete. Thus a heuristic is developed to solve this problem. To illustrate its suitability and efficiency a proposed heuristic is compared with a genetic algorithm and tabu search for a large number of randomly generated test problems in ship engine assembly shop. Through the experimental results it is showed that the proposed algorithm yields good solutions efficiently.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.237-242
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• 2002

The sales activity of most of small manufacturing companies is based on orders of buyers. The process of promotion, receipt and selection of orders of the manufacturers is closely coupled with the load status of the production lines. The decision on whether to accept an order or not, or the selection of optimal order set among excessive orders is entirely dependent on the schedule of production lines. However, in the real world, since the production scheduling activity is mainly performed by human experts, most of small manufacturers are suffer from being unable to meet due dates, lack of rapid decision on the acceptance of new order. To cope with this problem, this paper deals with the development of an agent for selecting an optimal order set automatically. The main engine of selection agent is based on the typical job-shop scheduling model since our target domain is the injection molding company. To solve the problem, we have formulated it as IP (Integer Program) model, and it has been successfully implemented by ILOG and selection agent. And we have suggested an architecture of an agent for tackling web based order selection problems.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.105-110
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• 2024

Nowadays, permutation problems with large state spaces and the path to solution is irrelevant such as N-Queens problem has the same general property for many important applications such as integrated-circuit design, factory-floor layout, job-shop scheduling, automatic programming, telecommunications network optimization, vehicle routing, and portfolio management. Therefore, methods which are able to find a solution are very important. Genetic algorithm (GA) is one the most well-known methods for solving N-Queens problem and applicable to a wide range of permutation problems. In the absence of specialized solution for a particular problem, genetic algorithm would be efficient. But holism and random choices cause problem for genetic algorithm in searching large state spaces. So, the efficiency of this algorithm would be demoted when the size of state space of the problem grows exponentially. In this paper, the new method presented based on genetic algorithm to cover this weakness. This new method is trying to provide partial view for genetic algorithm by locally searching the state space. This may cause genetic algorithm to take shorter steps toward the solution. To find the first solution and other solutions in N-Queens problem using proposed method: dividing N-Queens problem into subproblems, which configuring initial population of genetic algorithm. The proposed method is evaluated and compares it with two similar methods that indicate the amount of performance improvement.