• Journal of the military operations research society of Korea
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• v.13 no.1
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• pp.101-115
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• 1987

This paper presents an optimization based heuristic algorithm for a tour bus scheduling problem where buses consist of various kinds of sightseeing and commutation services. First, this algorithm transforms the prolem into a vehicle routing problem on whose nodes denote trips and arcs denote connections between trips. Second, a greedy heuritic routing technique is applied to find a good feasible bus-route set. Then the greedy feasible solution is improved by the simplex method using column generation technique. The algorithm provides a better near-optimal solution which gives much reductions in the total tour distance and the number of tour buses.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1244-1246
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• 2016

Influence maximization problem is to select seed node set, which maximizes information spread in social networks. Greedy algorithm shows an optimum solution, but has a high computational cost. A few heuristic algorithms were proposed to reduce the complexity, but their performance in influence maximization is limited. In this paper, we propose general degree discount algorithm, and show that it has better performance while keeping complexity low.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.17-24
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• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4640-4661
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• 2018

To cope with the explosive growth of Internet services, Service Function Chaining (SFC) based on Software-defined Networking (SDN) is an emerging and promising technology that has been suggested to meet this challenge. Determining the placement of Virtual Network Functions (VNFs) and routing paths that optimize the network utilization and resource consumption is a challenging problem, particularly without violating service level agreements (SLAs). This problem is called the optimal SFC placement problem and an Integer Linear Programming (ILP) formulation is provided. A greedy heuristic solution is also provided based on an improved two-step mapping algorithm. The obtained experimental results show that the proposed algorithm can automatically place VNFs at the optimal locations and find the optimal routing paths for each online request. This algorithm can increase the average request acceptance rate by about 17.6% and provide more than 20-fold reduction of the computational complexity compared to the Greedy algorithm. The feasibility of this approach is demonstrated via NetFPGA-10G prototype implementation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.142-152
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• 2009

We developed two heuristic methods to solve the problem considering a fleet of ships delivering chemical products from terminals to terminals. We need to decide how much of each product to carry, on which ship, subject to the conditions that all terminals must have sufficient products to meet demand, and the stock levels of the products cannot exceed the inventory capacity of that terminal. Mathematical formulation and the optimal objective value for the small size problems are compared with two greedy heuristic methods developed in terms of solution qualities and computing time. Numerical experiments on test problems indicate that the heuristics are effective at finding good solutions quickly.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.81-84
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• 2003

The ability to determine the optimal frequencies and offsets for independent and unrestricted ordering cycles for multiple items can be very valuable for managing storage capacity constrained facilities in a supply chain. The complexity of this problem has resulted in researchers focusing on more tractable surrogate problems that are special cases of the base problem. Murthy et al. (European Journal of Operation Research 2003) developed insights leading to solution of the original problem and present a heuristic for offsetting independent and unrestricted ordering cycles for items to minimize their joint storage requirements. However, their study cannot find optimal solution due to the Greedy Heuristic solution procedure. In this paper, we present a complete procedure to find the optimal solution for the model with a integer programming optimization approach and genetic algorithm. Numerical examples are included to compare each model with that of Murthy et at. Research of this type may prove useful in solving the more general problem of selecting order policies to minimize combined holding, ordering, and storage costs.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.177-198
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• 1994

We consider an algorithm for the Crew Scheduling Problem (CSP) based on the Transportation Problem approach. The main flows of the algorithm are arranged in three steps. First we propose a heuristic algorithm of the greedy principle to obtain an initial feasible solution. Secondary we present a method of formulating CSP into a Modified Transportation Problem format. Lastly the procedures of network search to get the optimal solution are presented. This algorithm can be applied to the general GSP and also to most combinatorial problems like the Vehicle Routing Problems. The computational results show that the large size CSP's could be tackled.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.113-121
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• 2014

This paper considers a scheduling problem in a two-machine flowshop with outsourcing strategy incorporated. The jobs can be either processed in the first machine or outsourced to outside subcontractors. This paper wants to determine which jobs to be processed in-house and which jobs to be outsourced. If any job is decided to be outsourced, then an additional outsourcing cost is charged The objective of this paper is to minimize the sum of scheduling cost and outsourcing cost under a budget constraint. At first this paper characterizes some solution properties, and then it derives solution procedure including DP (Dynamic Programming) and B&B (Branch-and-Bound) algorithms and a greedy-type heuristic. Finally the performance of the algorithms are evaluated with some numerical tests.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.33-41
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• 2021

This paper proposes heuristic greedy algorithm that can be find the solution within polynomial time with solution finding rule for the capacitated minimum spanning tree(CMST) problem, known as NP-hard. The CMST problem can be solved by computer-aided meta-heuristic because of the Esau-Williams heuristic polynomial time algorithm has a poor performance. Nevertheless the meta-heuristic methods has a limit performance that can't find optimal solution. This paper suggests visual by handed solution-finding rule for CMST. The proposed algorithm firstly construct MST, and initial feasible solution of CMST from MST, then optimizes the CMST with the subtree gates more adjacent to root node. As a result of total 30 cases of OR-LIB 10 data, Q=3,5,10, the proposed algorithm gets the best performance.