• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.325-328
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• 2003

Fierce competition in today's global markets, the heightened expectation of customers have forced business enterprises to invest in, and focus attentions on, their Supply Chains, Also Cross Docking is an essential part of SC, and integrating Cross Docking with vehicle routing scheduling is needed to smoothly link the physical flow of SC, However, there is no the mathematical model which focuses on Cross Docking with vehicle routing scheduling. Therefore, the integrating model considers Cross Docking and vehicle routing scheduling will be developed in this paper. And the solution based on Tabu algorithm to this model will be provided.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.145-153
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• 2008

The vehicle routing problem determines each vehicle routes to find the transportation costs, subject to meeting the customer demands of all delivery points in geography. Vehicle routing 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 aims to develop a heuristic method which combines guided local search with a tabu search in order to minimize the transportation costs for the vehicle routing assignment and uses ILOG programming library to solve. The computational tests were performed using the benchmark problems. And computational experiments on these instances show that the proposed heuristic yields better results than the simple tabu search does.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.97-109
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• 2000

This work proposes a neural network approach to solve vehicle routing problems which have diverse application areas such as vehicle routing and robot programming. In solving these problems, classical mathematical approaches have many difficulties. In particular, it is almost impossible to implement a real-time vehicle routing with multiple vehicles. Recently, many researchers proposed methods to overcome the limitation by adopting heuristic algorithms, genetic algorithms, neural network techniques and others. The most basic model for path planning is the Travelling Salesman Problem(TSP) for a minimum distance path. We extend this for a problem with dynamic upcoming of new positions with multiple vehicles. In this paper, we propose an algorithm based on SOM(Self-Organization Map) to obtain a sub-optimal solution for a real-time vehicle routing problem. We develope a model of a generalized multiple TSP and suggest and efficient solving procedure.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.193-196
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• 2002

Vehicle routing problem is known to be a NP-hard problem, and is traditionally solved by some heuristic approaches. This paper investigates the application of the computer method COMSOAL to the optimal vehicle routing selection problem. The COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) is a computer heuristic originally developed to solve an assembly line balancing problem a few decades ago. The solution methodology of repeatedly running COMSOAL will result in many feasible solutions from which the best is chosen. This solution approach now becomes viable thanks to the significantly increased speed of recent computer technology. This paper discusses the adaptation of the COMSOAL approach to the known set of simple vehicle routing example problem. The results show that the COMSOAL can be a good possible approach to solve the vehicle routing problem.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.171-179
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• 2011

In this paper, we deal with the vehicle routing problem that could establish operational plan of military engineer for survivability support of artillery position construction. We propose VRPTW(vehicle routing problem with time-window) model of special form that considered service level to reflect the characteristics of military operations rather than the logic of economic efficiencies in the objective function. Furthermore we suggest modified particle swarm optimization algorithm for service based vehicle routing problem solution that can be possible to search in complicated and uncertain area and control relation softly between global and local search.

• Spatial Information Research
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• v.18 no.3
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• pp.63-72
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• 2010

The vehicle routing problem with simultaneous pick-up and delivery (VRPSPD) is a variant of the vehicle routing problem (VRP) that customers require simultaneously a pick-up and delivery service. The main objective of VRPSPD is to minimize a cost of routes satisfying many constraints. Traditional VRPSPD have been dealt with a static environment. The static environment means that a routing data and plan cannot be changed. For example, it is difficult to change a vehicle's routing plan so that a vehicle serves the pick-up demands of new customers during the delivery service. Therefore, traditional approach is not suitable for dynamic environments. To solve this problem, we propose a novel approach for finding efficient routes using a real-time re-routing heuristics based on the Location Based Service (LBS). Our re-routing heuristics can generate a new route for vehicle that satisfies a new customer's demand considering the current geographic location of a vehicle. Experimental results show that our methodology can reduce the traveling cost of vehicles comparing with other previous methods.

• Journal of the military operations research society of Korea
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• v.9 no.1
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• pp.75-85
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• 1983

This study is concerned with the problem of routing vehicles stationed at a central depot to supply customers with known demands, in such a way as to minimize the total distance travelled. The problem is referred to as the vehicle routing problem and is a generalization of the multiple traveling salesmen problem that has many practical applications. A branch-and-bound algorithm for the exact solution of the vehicle routing problem is presented. The algorithm finds the optimal number of vehicles as well as the minimum distance routes. A numerical example is given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.171-178
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• 2012

A possible heuristic to solve metropolitan area vehicle routing problems with variable vehicle speeds is suggested in this research. Delivery hours are classified into 4 different time zones to make variable vehicle speeds no change within the same time zone to make TDVRP simple to solve. The suggested heuristic consists of 2 stages such as initial solution development step and initial solution improvement step. A computer program using C++ is constructed to evaluate the suggested heuristic. Randomly generated vehicle routing problems are used for the experiments. This heuristic could be helpful to logistics companies by increasing delivery efficiencies since the 4 zone classification is taken from the observed traffic information offered by a local government.

• Korean Management Science Review
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• v.24 no.2
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• pp.43-56
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• 2007

VRP(Vehicle Routing Problem) is studied in this paper, where two different kinds of missions are to be completed. The objective is to minimize the total vehicle operating distance. A mixed integer programming formulation and a heuristic algorithm for a practical use are suggested. A heuristic algorithm consists of three phases such as clustering, constructing routes, and adjustment. In the first phase, customers are clustered so that the supply nodes are grouped with demand nodes to be served by the same vehicle. Vehicle routes are generated within the cluster in the second phase. Clusters and routes are adjusted in the third phase using the UF (unfitness) rule designed to determine the customers and the routes to be moved properly. It is shown that the suggested heuristic algorithm yields good performances within a relatively short computational time through computational experiment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.666-668
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• 1998

In this paper, we consider a bi-objective vehicle routing problem to minimize total distance traveled and maximize minimum integrated satisfaction level of selecting desirable routes in an fuzzy graph. The fuzzy graph reflects a real delivery situation in which there are a depot, some demand points, paths linking them, and distance and integrated satisfaction level are associated with each route. For solving the vi-objective problem we introduce a concept of routing vector and define non-dominated solution for comparing vectors. An efficient algorithm involving a selection method of non-dominated solutions based on DEA is proposed for the vehicle routing problem with rigid distance and integrated satisfaction level.