• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.635-640
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• 2003

Due to the development of information technology and widespread use of telecommunications networks, the design of mesh-survivable net works has received considerable attention in recent years. This paper deals with spare capacity planning scheme in mesh-based fiber-optic networks. In this study, a new spare capacity planning scheme is proposed utilizing path restoration with maximal sharing of share capacity that is traced by the spare capacity incremental factor (after this, we called "SCIF"). We compare it with three other spare capacity planning scheme : link capacity of IP (Integer Programming), SLPA(Spare Link Placement Algorithm) and GA(Genetic Algorithm). The approach shows better performance with heuristics algorithm for determining the spare capacity assignment and the computational time is easily controlled allowing the approach to scale to large networks. The major advantages of the new approach are reduction of spare capacity and a polynomial time complexity.omplexity.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.125-133
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• 2012

This paper deals with the passenger transfer trips counted from smart bus-card data from Seoul transit network to understand the current operational condition of the system. Objective of this study is to relocate the location of the transit transfer centers. It delivers a bi-level programing model. The upper model is a linear 0-1 binary integer program having the objective of total travel cost minimization constrained by the number of transfer centers and the total construction budget. The lower model is an user equilibrium assignment model determining the passengers' route choice according to the transfer center locations. The proposed bi-level programming model was tested in an example network. The result showed that the proposed was able to find the optimal solution.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.70-77
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• 2012

Dial-a-ride is the most widely available transit service for disabled persons or seniors in the United States and Europe. This paper studies a static dial-a-ride problem considering multiple depots, heterogeneous vehicles, and soft time windows. In this paper, we apply a heuristic based on clustering first-routing second(HCR) to a real-world large dial-a-ride problem from Maryland Transit Administration(MTA). MTA's real operation is compared with the results of developed heuristic for 24 cases. The objective function of the proposed model is to minimize the total cost composed of the service provider's cost and the customers' inconvenience cost. For the comparison, the objective function values of HCR do not include waiting cost, delay cost, and excess ride cost. The objective function values from HCR are better than those from MTA's operation for all cases. This result shows that our heuristic method can make the real operation better and more efficient.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.56-63
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• 2016

For air express service providers offering various express delivery services such as overnight delivery and next-business day delivery services, establishing quickly cargo loading plans is one of important issues owing to the characteristics of air express business, i.e., a short amount of time is available to complete all cargo loading operations before flight departure after receiving air express containers, pallets and bulks. On the other hand, one of major concerns in the air cargo loading planning is to make a plan that insures the stability of an aircraft to avoid take-off, flight, and landing accidents. To this end, this paper considers an air cargo loading planning problem, which is the problem of determining locations in the aircraft cargo space where air containers, pallets and bulks to be loaded while insuring the aircraft stability, motivated from DHL and Air Hong Kong. The objective of the problem is to maximize the total revenue gained from loading air express containers, pallets and bulks. To solve the problem, this paper suggests a simulated annealing algorithm to overcome impracticality of the integer programming model developed by a previous study requiring excessive computation time. The results of computational experiments show that the heuristic algorithm is a viable tool for establishing express cargo loading plans as giving robust and good solutions in a short amount of computation time. Scenario analyses are performed to investigate the effect of the current activities of air express carriers on the revenue change and to draw practical implications for air express service providers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3474-3497
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• 2016

Network Function Virtualization (NFV) and Software Defined Networking (SDN) are recently considered as very promising drivers of the evolution of existing middlebox services, which play intrinsic and fundamental roles in today's networks. To address the virtual service deployment issues that caused by introducing NFV or SDN to networks, this paper proposes an optimal solution by combining quantum genetic algorithm with cooperative game theory. Specifically, we first state the concrete content of the service deployment problem and describe the system framework based on the architecture of SDN. Second, for the service location placement sub-problem, an integer linear programming model is built, which aims at minimizing the network transport delay by selecting suitable service locations, and then a heuristic solution is designed based on the improved quantum genetic algorithm. Third, for the service amount placement sub-problem, we apply the rigorous cooperative game-theoretic approach to build the mathematical model, and implement a distributed algorithm corresponding to Nash bargaining solution. Finally, experimental results show that our proposed method can calculate automatically the optimized placement locations, which reduces 30% of the average traffic delay compared to that of the random placement scheme. Meanwhile, the service amount placement approach can achieve the performance that the average metric values of satisfaction degree and fairness index reach above 90%. And evaluation results demonstrate that our proposed mechanism has a comprehensive advantage for network application.

• Journal of KIISE
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• v.44 no.9
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• pp.986-991
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• 2017

In this paper, we present a method for synthesizing imperative programs from input-output examples. Given (1) a set of input-output examples, (2) an incomplete program, and (3) variables and integer constants to be used, the synthesizer outputs a complete program that satisfies all of the given examples. The basic synthesis algorithm enumerates all possible candidate programs until the solution program is found (enumerative search). However, it is too slow for practical use due to the huge search space. To accelerate the search speed, our approach uses code optimization and avoids unnecessary search for the programs that are syntactically different but semantically equivalent. We have evaluated our synthesis algorithm on 20 introductory programming problems, and the results show that our method improves the speed of the basic algorithm by 10x on average.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1624-1647
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• 2020

Vehicular network is one of the most important subjects for researchers in recent years. Anycast routing protocols have many applications in vehicular ad hoc networks. The aim of an anycast protocol is sending packets to at least one of the receivers among candidate receivers. Studies done on anycast protocols over vehicular networks, however, have capability of implementation on some applications; they are partial, and application specific. No need to say that the lack of a comprehensive study, having a strong analytical background, is felt. Mathematical modeling in vehicular networks is difficult because the topology of these networks is dynamic. In this paper, it has been demonstrated that vehicular networks can be modeled based on time-expanded networks. The focus of this article is on geographical anycast. Three different scenarios were proposed including sending geographic anycast packet to exactly-one-destination, to at-least-one-destination, and to K-anycast destination, which can cover important applications of geographical anycast routing protocols. As the proposed model is of MILP type, a decentralized heuristic algorithm was presented. The evaluation process of this study includes the production of numerical results by Branch and Bound algorithm in general algebraic modeling system (GAMS) software and simulation of the proposed protocol in OMNET++ simulator. The comprehension of the result of proposed protocol and model shows that the applicability of this proposed protocol and its reactive conformity with the presented models based on presented metrics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1098-1107
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• 1999

In multimedia applications, a source usually generates multiple streams. By heterogeneous multimedia multicast, we mean a recipient can receive some of them, not necessarily all of them. A recipient bids for what it wants to receive and the source gains the same amount when a connection is established. The problem of distributing streams for heterogeneous multicast to maximize the source's gain, can be solved using a 0-1 integer programming, hewn as NP-complete. In this paper, we propose efficient stream distribution algorithms in two different types of multicast models. The first restricted model assumes that the capacity for a link in the multicast tree is grater than or equal to the capacities of its descendant links. In the second unrestricted model, we drop out the restriction in the restricted model. Proposed algorithms have better time and space complexities compared with any existing one. In addition, distributed implementations are straightforward, which is very useful for large networks.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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• pp.233-242
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• 2015

This paper analyzes a multi-product inbound lot-sizing and outbound dispatching problem with multi-vehicle types in a third-party logistics distribution center. The product must be delivered to the customers within the delivery time window and backlogging is not allowed. Replenishing orders are shipped by several types of vehicles with two types of the freight costs, i.e., uniform and decreasing, are considered. The objective of this study is to determine the lot-size and dispatching schedules to minimize the total cost with the sum of inbound and outbound transportation and inventory costs over the entire time horizon. In this study, we mathematically derive a mixed-integer programming model and propose a genetic algorithm (GA1) based on a local search heuristic algorithm to solve large-scale problems. In addition, we suggest a new genetic algorithm (GA2) with an adjusting algorithm to improve the performance of GA1. The basic mechanism of the GA2 is to provide an unidirectional partial move of products to available containers in the previous period. Finally, we analyze the results of GA1 and GA2 by evaluate the relative performance using the gap between the objective values of CPLEX and the each algorithm.

• Industrial Engineering and Management Systems
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• v.7 no.2
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• pp.126-132
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• 2008

The bin packing problem (BPP) is an NP-Complete Problem. The problem can be described as there are $N=\{1,2,{\cdots},n\}$ which is a set of item indices and $L=\{s1,s2,{\cdots},sn\}$ be a set of item sizes sj, where $0<sj{\leq}1$, ${\forall}j{\in}N$. The objective is to minimize the number of bins used for packing items in N into a bin such that the total size of items in a bin does not exceed the bin capacity. Assume that the bins have capacity equal to one. In the past, many researchers put on effort to find the heuristic algorithms instead of solving the problem to optimality. Then, the quality of solution may be measured by the asymptotic worst-case ratio or the average-case ratio. The First Fit Decreasing (FFD) is one of the algorithms that its asymptotic worst-case ratio equals to 11/9. Many researchers prove the asymptotic worst-case ratio by using the weighting function and the proof is in a lengthy format. In this study, we found an easier way to prove that the asymptotic worst-case ratio of the First Fit Decreasing (FFD) is not more than 11/9. The proof comes from two ideas which are the occupied space in a bin is more than the size of the item and the occupied space in the optimal solution is less than occupied space in the FFD solution. The occupied space is later called the weighting function. The objective is to determine the maximum occupied space of the heuristics by using integer programming. The maximum value is the key to the asymptotic worst-case ratio.