• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.142-148
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• 2008

Facility Layout Planning is concerned with how to arrange facilities necessary for production in a given space. Its objective is often to minimize the total sum of all material flows multiplied by the distance among facilities. FLP belongs to NP complete problem; i.e., the number of possible layout solutions increases with the increase of the number of facilities. Thus, meta heuristics such as Genetic Algorithm (GA) and Simulated Annealing have been investigated to solve the FLP problems. However, one of the biggest problems which lie in the existing meta heuristics including GA is hard to find an appropriate combinations of parameters which result in optimal solutions for the specific problem. The Ant System algorithm with elitist and ranking strategies is used to solve the FLP problem as an another good alternative. Experimental results show that the AS algorithm is able to produce the same level of solution quality with less sensitive parameters selection comparing to the ones obtained by applying other existing meta heuristic algorithms.

• Journal of information and communication convergence engineering
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• v.5 no.3
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• pp.239-244
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• 2007

In the telematics system, a reasonable path search time should be guaranteed from a great number of user's queries, even though the optimal path with minimized travel time might be continuously changed by the traffic flows. Thus, the path search method should consider traffic flows of the roads and the search time as well. However, the existing path search methods are not able to cope efficiently with the change of the traffic flows and to search rapidly paths simultaneously. This paper proposes a new path search method for fast computation. It also reflects the traffic flows efficiently. Especially, in order to simplify the computation of variable heuristic values, it employs a simplification method for estimating values of traffic-adaptable heuristics. The experiments are carried out with the $A^*$ algorithm and the proposed method in terms of the execution time, the number of node accesses and the accuracy. The results obtained from the experiments show that the method achieves very fast execution time and the reasonable accuracy as well.

• Journal of the Korea Safety Management & Science
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• v.13 no.3
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• pp.107-114
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• 2011

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.123-137
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• 1995

Most of solution methods in scheduling attempt to generate good solutions by either developing algorithms or heuristic rules. However, scheduling problems in the real world require considering more factors such as multiple objectives, different combinations of heuristic rules due to problem characteristics. In this respect, the traditional mathematical a, pp.oach showed limited performance so that new a, pp.oaches need to be developed. Expert system is one of them. When an expert system is developed for scheduling one of the most difficult processes faced could be knowledge acquisition on scheduling heuristics. In this paper we propose a method for the acquisition of knowledge on the selection of scheduling heuristics using Dempster-Shafer Theory(DST). We also show the examples in the multi-objectives environment.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.2
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• pp.254-265
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• 1999

We consider two stage hybrid flowshop scheduling problem when there are two non-identical parallel machines at the first stage, and only one machine at the second stage. Several well-known sequence-first allocate-second heuristics are considered first. We then propose an allocate-first sequence-second heuristic to find minimum makespan schedule. The effectiveness of the proposed heuristic algorithm in finding a minimum makespan schedule is empirically evaluated by comparing with easily computable lower bound. The proposed heuristic algorithm as well as the existing heuristics are evaluated by simulation in four cases which have different processing time distribution, and it is found that the proposed algorithm is more effective in every case.

• IE interfaces
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• v.13 no.2
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• pp.195-203
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• 2000

This paper considers the problem of sequencing printed circuit boards(PCBs) on an automatic surface mount device(SMD) placement machine in order to minimize total setup time. Since the total set of component feeders needed by all boards cannot be loaded simultaneously on the magazine, the setup must be made between two successive boards in the sequence. It is assumed that the setup time depends on the number of component feeders to be replaced in the magazine. An important characteristic is that each feeder occupies a different number of slots in the magazine. This problem is equivalent to travelling salesman problem(TSP) except that the distances between two cities, that is, the setup times between two boards, are not known in advance. So, TSP-based heuristics with new distance functions are presented and their performances are compared through various test problems. Computational results indicate that our heuristics outperform existing methods.

• IE interfaces
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• v.19 no.1
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• pp.78-85
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• 2006

A centralized inventory system provides a number of stores with cost reduction, information sharing. In this paper, transportation costs are included in the inventory model. To build centralized warehouses, two things should be considered: how many warehouses are required and where these are located. The objective of this paper is to develop efficient heuristics to determine the location and the number of central warehouses by minimizing total costs. Throughout some computational experiments, the results of the heuristics are compared with an optimal solution.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.675-685
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• 2013

Generation capacities and transmission line constraints in a competitive electricity market make it troublesome to compute Nash Equilibrium (NE) for analyzing participants' strategic generation quantities. The NE can cause a mixed strategy NE rather than a pure strategy NE resulting in a more complicated computation of NE, especially in a multiplayer game. A two-level hierarchical optimization problem is used to model competition among multiple participants. There are difficulties in using a mathematical programming approach to solve a mixed strategy NE. This paper presents heuristics applied to the mathematical programming method for dealing with the constraints on generation capacities and transmission line flows. A new formulation based on the heuristics is provided with a set of linear and nonlinear equations, and an algorithm is suggested for using the heuristics and the newly-formulated equations.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.13-18
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• 2023

This paper aims to investigate data features for neighbor path selection (NPS) in computer network with regional failures. It is necessary to find an available alternate communication path in advance when regional failures due to earthquakes or forest fires occur simultaneously. We describe previous general heuristics and simulation heuristic to solve the NPS problem in the regional fault network. The data features of general heuristics using proximity and sharing factor and the data features of simulation heuristic using machine learning are explained through examples. Simulation heuristic may be better than general heuristics in terms of communication success. However, additional data features are necessary in order to apply the simulation heuristic to the real environment. We propose novel data features for NPS in computer network with regional failures and Keras modeling for computing the communication success probability of candidate neighbor path.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.281-286
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• 2024

This paper describes the solution method of Team AIRLAB used to participate in the League of Robot Runners Competition which tackles the problem of Lifelong Multi-agent Pathfinding (MAPF). In lifelong MAPF, multiple agents are tasked to navigate to their respective goal locations where new goals are consecutively revealed once they reach initial goals. The agents need to avoid collisions and deadlock situations while they navigate to perform tasks. Our method consists of (i) Puzzle Heuristics, (ii) MAPF-LNS2, and (iii) RHCR. The Puzzle Heuristics is our own algorithm that generates a compact heuristic table contributing to reduce memory consumption and computation time. MAPF-LNS2 and RHCR are state-of-the-art algorithms for MAPF. By combining these three algorithms, our method can improve the efficiency of paths for all agents significantly.