• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.1-8
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• 2015

Safety critical systems require exhaustive verification of safety properties, because even a single corner-case fault can cause a critical safety failure. However, existing verification approaches are too costly in terms of time and computational resource required, making it hard to be applied in practice. In this paper, we implemented a tool for minimizing the size of the verification target w.r.t. verification properties to check, based on program slicing technique[1]. The efficacy of program slicing using our tool is demonstrated in a case study with a verification target Trampoline[3], which is an open source automotive operating system compliant with OSEK/VDX[2]. Experiments have shown enhanced performance in verification, with a 71% reduction in the size of the code.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.179-184
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• 2002

We consider the heterogeneous fleet vehicle routing problem (HVRP), a variant of the classical vehicle routing problem (VRP). The HVRP differs from the classical VRP in that it deals with a heterogeneous fleet of vehicles having various capacities, fixed costs, and variables costs. Therefore the HVRP is to find the fleet composition and a set of routes with minimum total cost. We give an integer programming formulation of the problem and propose an algorithm to solve it. Although the formulation has exponentially many variables, we can efficiently solve the linear programming relaxation of it by using the column generation technique. To generate profitable columns we solve a shortest path problem with capacity constraints using dynamic programming. After solving the linear programming relaxation, we apply a branch-and-bound procedure. We test the proposed algorithm on a set of benchmark instances. Test results show that the algorithm gives best-known solutions to almost all instances.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.795-807
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• 2014

Vehicular Ad hoc Network (VANET) has gone through a rich amount of research and currently is making its way towards the deployment. However, surprisingly it evolved to rather more applications and services-rich breed referred to as VANET-based clouds due to the advancements in the automobile and communication technologies. Security and privacy have always been the challenges for the think tanks to deploy this technology on mass scale. It is even worse that some security issues are orthogonally related to each other such as privacy, revocation and route tracing. In this paper, we aim at a specific VANET-based clouds framework proposed by Hussain et al. namely VANET using Clouds (VuC) where VANET and cloud infrastructure cooperate with each other in order to provide VANET users (more precisely subscribers) with services. We specifically target the aforementioned conflicted privacy, route tracing, and revocation problem in VANET-based clouds environment. We propose a multiple pseudonymous approach for privacy reasons and leverage the beacons stored in the cloud infrastructure for both route tracing and revocation. In the proposed scheme, revocation authorities after colluding, can trace the path taken by the target node for a specified timespan and can also revoke the identity if needed. Our proposed scheme is secure, conditional privacy preserved, and is computationally less expensive than the previously proposed schemes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.16-22
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• 2019

After Dantzig and Rasmer introduced Vehicle Routing Problem in 1959, this field has been studied with numerous approaches so far. Classical Vehicle Routing Problem can be described as a problem of multiple number of homogeneous vehicles sharing a same starting node and having their own routes to meet the needs of demand nodes. After satisfying all the needs, they go back to the starting node. In order to apply the real world problem, this problem had been developed with additional constraints and pick up & delivery model is one of them. To enhance the effectiveness of pick up & delivery, hub became a popular concept, which often helps reducing the overall cost and improving the quality of service. Lots of studies have suggested heuristic methods to realize this problem because it often becomes a NP-hard problem. However, because of this characteristic, there are not many studies solving this problem optimally. If the problem can be solved in polynomial time, optimal solution is the best option. Therefore, this study proposes a new mathematical model to solve this problem optimally, verified by a real world problem. The main improvements of this study compared to real world case are firstly, make drivers visit every nodes once except hub, secondly, make drivers visit every nodes at the right time, and thirdly, make drivers start and end their journey at their own homes.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.52-58
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• 2005

In this paper, we address a real-world periodic vehicle routing problem with time windows (PVRPTW). In addition to the general requirements of single-day vehicle routing problem, each stop has required number of visits within a cycle period in PVRPTW. Thus, we need to determine optimized days of visit for each stop with consideration of the cycle-period days together. The problem also requires consistent vehicle assignment to the stops. We developed a clustering based 3-phase approach for this problem: 1) stop-route assignment, 2) stop-day assignment, and 3) stop sequencing within a single-day route. Using the approach, we could reduce the number of routes and improve the routing efficiency for several real-world problems.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.71-79
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• 1990

In this paper, the Double Shortest Arborescence & Merging method is presented as an efficient heuristic algorithm for the Public Vehicle Routing Problem which is to find the minimum total cost routes of M or less vehicles to traverse the required arcs(demand streets) at least once and return to their starting depot on a directed network. Double Shortest Arborescence which consists of forward shortest aborescence and backward one informs M or less shortest routes to traverse all required arcs. The number of these routes is reduced to M or less by merging routes. The computational experiment based on randomly generated networks reports that this algorithm is efficient.

• IE interfaces
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• v.17 no.spc
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• pp.103-110
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• 2004

This paper presents a two-phase method for the vehicle routing problems with time windows(VRPTW). In a supply chain management(SCM) environment, timely distribution is very important problem faced by most industries. The VRPTW is associated with SCM for each customer to be constrained the time of service. In the VRPTW, the objective is to design the least total travel time routes for a fleet of identical capacitated vehicles to service geographically scattered customers with pre-specified service time windows. The proposed approach is based on ant colony optimization(ACO) and improvement heuristic. In the first phase, an insertion based ACO is introduced for the route construction and its solutions is improved by an iterative random local search in the second phase. Experimental results show that the proposed two-phase method obtains very good solutions with respect to total travel time minimization.

• IE interfaces
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• v.17 no.spc
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• pp.28-36
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• 2004

The main purpose of this study is to find out the optimal solution of the vehicle routing problem considering heterogeneous vehicle(s), double-trips, and multi depots. This study suggests a mathematical programming model with new numerical formula which considers the amount of delivery and sub-tour elimination and gives optimal solution by using OPL-STUDIO(ILOG). This study also suggests modified genetic algorithm which considers the improvement of the creation method for initial solution, application of demanding point, individual and last learning method in order to find excellent solution, survival probability of infeasible solution for allowance, and floating mutation rate for escaping from local solution. The suggested modified genetic algorithm is compared with optimal solution of the existing problems. We found the better solution rather than the existing genetic algorithm. The suggested modified genetic algorithm is tested by Eilon and Fisher data(Eilon 22, Eilon 23, Eilon 30, Eilon 33, and Fisher 10), respectively.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.1
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• pp.34-44
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• 1987

The general vehicle routing problem has been studied by many researchers such as Christofides, et al. and Laporte, et al., but only limited effort has been devoted to developing the optimal algorithms. The purpose of this paper is to develop a branch and bound algorithm which determines the optimal vechicle routes and the optimal number of vehicles concurrenetly for the asymmetrical vehicle routing problem. In order to enhance the efficiency, this algorithm emphasizes the followings ; First, an efficient primal-dual approach is developed to solve subproblems which are called the specialized transportation problem, formed by relaxing the illegal subtour constraints from the vehicle routing problem, second, an improved branching scheme is developed to reduce the number of candidate subproblems by adequate utilization of vehicle capacity restrictions.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.346-354
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• 2004

Most industrial logistic systems have focused on carrying products from manufacturers or distribution centers to customers. In recent years, they are faced with the problem of integrating reverse flows into their transportation systems. In this paper, we address the vehicle routing problems with mixed delivery and pick-up(VRPMDP). Mixed operation of delivery and pick-up during a vehicle tour requires rearrangement of the goods on board. The VRPMDP considers the reshuffling time of goods at customers, hard time windows, and split operation of delivery and pick-up. We construct a mixed integer mathematical model and propose a new genetic algorithm named GAMP for VRPMDP. Computational experiments on various types of test problems are performed to evaluate GAMP against the modified Dethloff's algorithm. The results show that GAMP reduces the total vehicle operation time by 5.9% on average, but takes about six times longer computation time.