• IE interfaces
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• v.13 no.3
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• pp.424-430
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• 2000

Supply chain management (SCM) is an area that has recently received a great deal of attention in the business community. While SCM is relatively new, the idea of coordinated planning is not. During the last decades, many researchers have investigated multi-stage inventory problems. However, only a few papers address the problem of cost-optimal coordination of multi-stage inventory control with respect to service measures. Even published approaches have a shortcoming in dealing with a delivery lead time consisted of a shipping time and a waiting time. Assumed that there is no waiting time, or that the delivery lead time is implicitly compounded of a shipping time and a waiting time, the problem is often simplified into a multi-stage buffer allocation and a single-stage stochastic buffer sizing problem at all installations. This paper presents a simulation-based heuristic algorithm and a comparison with others for the problem that cannot be decomposed into a multi-stage buffer allocation and a single-stage stochastic buffer sizing problem because the waiting time ties together all stages. The comparison shows that the simulation-based heuristic algorithm performs better than other approaches in saving average inventory cost for both Poisson and Normal demands.

• Korean Management Science Review
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• v.9 no.1
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• pp.119-137
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• 1992

A two phase heuristic algorithm has been developed for the resolution of resource conflicts in a single project scheduling problem. Phase 1 of the algorithm generates a feasible schedule by repairing resource conflicts. Phase 2 finds local improvements in the schedule found in phase 1. Then, the algorithm has been applied to multi project and job shop scheduling. Computational results are compared with those of dispatching procedures. Index Terms-disjunctive constraints, heuristic algorithm, project scheduling, job-shop scheduling.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.1
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• pp.58-67
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• 1995

A path planning algorithm using a laser range finder are presented for real-tiem navigation of an autonomous guided vehicle. Considering that the laser range finder has the excellent resolution with respect to angular and distance measurements, a sophisticated local path planning algorithm is achieved by using the human's heuristic method. In the case of which the man knows not rhe path, but the goal direction, the man forwards to the goal direction, avoids obstacle if it appears, and selects the best pathway when there are multi-passable ways between objects. These heuristic principles are applied to the path decision of autonomous guided vehicle such as forward open, side open and no way. Also, the effectiveness of the established path planning algorithm is estimated by computer simulation in complex environment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.343-352
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• 1993

In this study, a modified algorithm for the exchange heuristic is developed and applied to a resource-constrained scheduling problem. The problem involves multiple projects and multiple resource categories and allows flexible resource allocation to each activity. The objective is to minimize the maximum completion time. The exchange heuristkc is a multiple pass algorithm which makes improvements upon a given initial feasible schedule. Four different modified algorithms are proposed. The original algorithm and the new algorithms were compared through an experimental investigation. All the proposed algorithms reduce the maximum completion time much more effectively than the original algorithm. Especially, one of four proposed algorithms obviously outperforms the other three algorithms. The algorithm of the best performance produces significantly shorter schedules than the original algorithm, though it requires up to three times more computation time. However, in most situations, a reduction in schedule length means a significant reduction in the total cost.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8C
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• pp.729-740
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• 2002

This paper studies the issues arising in the virtual topology reconfiguration phase of Multi-wavelength Routed Optical Networks. This reconfiguration process means to change the virtual topology in response to the changing traffic patterns in the higher layer. We formulate the optimal reconfiguration policy as a multi-stage decision-making problem to maximize the expected reward and cost function over an infinite horizon. Then we propose a new heuristic algorithm based on node-exchange to reconfigure the virtual topology to meet the traffic requirement. To counter the continual approximation problem brought by heuristic approach, we take the traffic prediction into consideration. We further propose a new heuristic reconfiguration algorithm called Prediction based Multi-stage Reconfiguration approach to realize the optimal reconfiguration policy based on predicted traffic. Simulation results show that our reconfiguration policy significantly outperforms the conventional one, while the required physical resources are limited.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2914-2935
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• 2016

With the increasing popularization of cloud computing, how to reduce physical energy consumption and increase resource utilization while maintaining system performance has become a research hotspot of virtual machine deployment in cloud platform. Although some related researches have been reported to solve this problem, most of them used the traditional heuristic algorithm based on greedy algorithm and only considered effect of single-dimensional resource (CPU or Memory) on energy consumption. With considerations to multi-dimensional resource utilization, this paper analyzed impact of multi-dimensional resources on energy consumption of cloud computation. A multi-dimensional resource constraint that could maintain normal system operation was proposed. Later, a novel virtual machine deployment method (NVMDM) based on improved particle swarm optimization (IPSO) and Euclidean distance was put forward. It deals with problems like how to generate the initial particle swarm through the improved first-fit algorithm based on resource constraint (IFFABRC), how to define measure standard of credibility of individual and global optimal solutions of particles by combining with Bayesian transform, and how to define fitness function of particle swarm according to the multi-dimensional resource constraint relationship. The proposed NVMDM was proved superior to existing heuristic algorithm in developing performances of physical machines. It could improve utilization of CPU, memory, disk and bandwidth effectively and control task execution time of users within the range of resource constraint.

• Journal of Korean Society for Quality Management
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• v.18 no.1
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• pp.65-76
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• 1990

A lot-sizing model for multi-stage MRP systems is proposed, in which known demands must be satisfied. In this model, an approach with considerations of initial inventory and limited production capacity is involved. Most of the studies on the lot-sizing techniques for multi-stage material requirement planning systems have been focused upon two basic approaches. One approach is to develope an algorithm yielding an optimal solution. Due to the computational complexity and sensitivity of the optimal solution to the problem of lot sizing, heuristic approaches are often employed. In this paper, the heuristic approach is used by sequential application of a single-stage algorithm with a set of modified cost by the concept of multi-echelon costs. The proposed method is compared with an lot-sizing method(Florian-Klein Model) to prove its effectiveness by numerical examples.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.319-325
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• 2001

Recently. AGV systems are used to serve the raw material to each work stations automatically. There exists a trade-off between the adequate service supply and the number of purchased AGVs. Also, to reduce the overall production cost, the amount of inventory hold on the shop floor should be considered. In this paper, we present a heuristic technique for determining the number of AGVs which includes the net present fixed costs of each station, each purchased AGV, delivering cost, stock inventory cost, and safety stock inventory cost. Secondly, by using a genetic algorithm, the optimal number of AGVs and the optimal reorder quantity at each station are decided. Lastly, to verify then heuristic algorithm, we have done a computer simulation with different GA parameters.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.75-88
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• 1997

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.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.1-9
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• 2013

We propose a heuristic algorithm for war-game model that is appropriate for warfare in which the maneuver of the attacker is relatively certain. Our model is based on a multi-weapon extention of the Lanchester's square law. However, instead of dealing with the differential equations, we use a multi-period linear approximation which not only facilitates a solution method but also reflects discrete natures of warfare. Then our game model turns out to be a continuous game known to have an ${\varepsilon}$-Nash equilibrium for all ${\varepsilon}{\geq}0$. Therefore, our model approximates an optimal warfare strategies for both players as well as an efficient reinforcement of area defense system that guarantees a peaceful equilibrium. Finally, we report the performance of a practical best-response type heuristic for finding an ${\varepsilon}$-Nash equilibrium for a real-scale problem.