• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.389-398
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• 2010

The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.959-968
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• 1999

several papers have reported that parallel heuristics or hybrid approaches combining several heuristics can get better results. However, the parallelization and hybridization of any search methods on the single CPU type computer need enormous computation time. that case, we need more elegant combination method. For this purpose, we propose Fast Model of Parallel Heuristics(FMPH). FMPH is based on the island model of parallel genetic algorithms and takes local search to the elite solution obtained form each island(sub group). In this paper we introduce how can we adapt FMPH to the job-shop scheduling problem notorious as the most difficult NP-hard problem and report the excellent results of several famous benchmark problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2303-2308
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• 2012

In this paper, we deal with the problem of scheduling parallel tasks with deadlines. Parallel tasks can be simultaneously executed on various machines and specially, we consider the malleable tasks, that is, the tasks whose execution time is given by a function of the number of machines on which they are executed. The goal of the problem is to maximize the throughput of tasks completed within their deadlines. This problem is well-known as NP-hard problem. Thus we will find an approximation algorithm, and its performance is compared with that of the optimal algorithm and analyzed by finding the approximation ratio. In particular, the algorithm has more resources, that is, more machines, than the optimal algorithm. This is called the resource augmentation analysis. We propose an algorithm to guarantee the approximation ratio of 3.67 using 1.5 times machines.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12B
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• pp.728-734
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• 2007

In this paper, we propose a GOSST(Grade Of Services Steiner minimum Tree) heuristic mechanism for the design of a physical multiple security grade network with minimum construction cost. On the network, each node can communicate with other nodes by its desiring security grade. Added to the existing network security methods, the preventing method from illegal physical access is necessary for more safe communication. To construct such network with minimum cost, the GOSST problem is applied. As the GOSST problem is a NP-Hard problem, a heuristic with reasonable complexity is necessary for a practical solution. In this research, to design the physical multiple security grade network with the minimum construction cost, the reformed our previous Distance Direct GOSST heuristic mechanism is proposed. The mechanism brings average 29.5% reduction in network construction cost in comparison with the experimental control G-MST.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.155-165
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• 2012

This paper suggests traveling salesman problem algorithm that have been unsolved problem with NP-Hard. The proposed algorithm is a heuristic with edge-swap method. The classical method finds the initial solution starts with first node and visits to mostly adjacent nodes then decides the traveling path. This paper selects minimum weight edge for each nodes, then perform Min-Min method that start from minimum weight edge among the selected edges and Min-Max method that starts from maximum weight edges among it. Then we decide tie initial solution to minimum path length between Min-Min and Min-Max method. To get the final optimal solution, we apply previous two-opt to initial solution. Also, we suggest extended 3-opt and 4-opt additionally. For the 7 actual experimental data, this algorithm can be get the optimal solutions of state-of-the-art with fast and correct.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.1149-1160
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• 2005

Generally, the network topology design problem, which is difficult to solve with the classical method because it has exponentially increasing complexity with the augmented network size, is characterized as a kind of NP-hard combinatorial optimization problem. The problem of this research is to design the highly reliable network topology considering the connection cost and all-terminal network reliability, which can be defined as the probability that every pair of nodes can communicate with each other. In order to solve the highly reliable network topology design problem minimizing the construction cost subject to network reliability, we proposes an efficient two phase approach to design reliable network topology, i.e., the first phase employs, a genetic algorithm (GA) which uses $Pr\ddot{u}fer$ number for encoding method and backtracking Algorithm for network reliability calculation, to find the spanning tree; the second phase is a greedy method which searches the optimal network topology based on the spanning ree obtained in the first phase, with considering 2-connectivity. finally, we show some experiments to demonstrate the effectiveness and efficiency of our two phase approach.

• Journal of Internet Computing and Services
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• v.8 no.1
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• pp.25-31
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• 2007

The development of efficient Quality of Service (QoS) routing algorithms in high speed networks is very difficult since divergent services require various quality conditions, If the QoS parameter we concern is to measure the delay on that link, then the routing algorithm obtains the Least Delay (LD) path, Meanwhile, if the parameter is to measure of the link cast, then it calculates the Least Cost (LC) path. The Delay Constrained Least Cast (DCLC) path problem of the mixed issues on LD and LC has been shown to be NP-hard. The path cost of LD path is relatively mere expensive than that of LC path, and the path delay of LC path is relatively higher than that of LD path in DCLC problem. In this paper. we propose the algorithm based on estimated path for the DCLC problem and investigate its performance, It employs a new parameter which is probabilistic combination of cost and delay, We have performed empirical evaluation that compares our proposed algorithm with the DCUR in various network situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3134-3155
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• 2020

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush-Kuhn-Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms.

• Journal of the Korea Society for Simulation
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• v.16 no.1
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• pp.1-9
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• 2007

The development of efficient Qualify of Service (QoS) routing algorithms in high speed networks is extremely difficult to achieve, because in order to operate, divergent services require various quality conditions. If the parameter of concern is to measure the delay on a specific link, the routing algorithm obtains the Least Delay (LD) path. Meanwhile, if the parameter is to measure the link cost, the Least Cost (LC) path is calculated. The Delay Constrained Least Cost (DCLC) path problem of the mixed issues on the LD and LC has been shown to be NP-hard. The path cost of LD path is relatively more expensive than that of the LC path, and the path delay of the LC path is relatively higher than that of the LD path in the DCLC problem. In this paper, we introduce the new factor for balancing cost and delay. The simulation result shows that our introduced factor is satisfied to solve the DCLC problem.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.21-29
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• 2015

The set partitioning problem is a well-known NP-hard combinatorial optimization problem, and it is formulated as an integer programming model. This paper proposes an Integer Programming-based Local Search for solving the set partitioning problem. The key point is to solve the set partitioning problem as the set covering problem. First, an initial solution is generated by a simple heuristic for the set covering problem, and then the solution is set as the current solution. Next, the following process is repeated. The original set covering problem is reduced based on the current solution, and the reduced problem is solved by Integer Programming which includes a specific element in the objective function to derive the solution for the set partitioning problem. Experimental results on a set of OR-Library instances show that the proposed algorithm outperforms pure integer programming as well as the existing heuristic algorithms both in solution quality and time.