• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.253-256
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• 2013

For the reliable delivery of safety-related information to vehicles in the VANET, a reliable VANET routing protocol is required. In this paper, we propose a routing protocol that works based on the road vehicle density information for fast and reliable communications among vehicles within the city environment VANET. In the proposed mechanism, each vehicle computes the road vehicle density by using beacon messages and the road information. Based on the road vehicle density information, each vehicle establishes a reliable route for packet delivery. Through the NS-2 based simulations, we compare our proposed mechanism with GPSR and show that our mechanism outperforms GPSR in terms of packet delivery success rate.

• 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 Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.75-96
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• 2016

This paper investigates the usefulness of drones in an urban delivery system. We define the vehicle and drone routing problem with time window (VDRPTW) and present a model that can describe a dual mode delivery system consisting of drones and vehicles in the metropolitan area. Drones are relatively free from traffic congestion but have limited flight range and capacity. Vehicles are not free from traffic congestion, and the complexity of urban road network reduces the efficiency of vehicles. Using drones and vehicles together can reduce inefficiency of the urban delivery system because of their complementary cooperation. In this paper, we assume that drones operate in a point-to-point manner between the depot and customers, and that customers in the need of fast delivery are willing to pay additional charges. For the experiment datasets, we use instances of Solomon (1987), which are well known in the Vehicle Routing Problem society. Moreover, to mirror the urban logistics demand trend, customers who want fast delivery are added to the Solomon's instances. We propose a hybrid evolutionary algorithm for solving VDRPTW. The experiment results provide different useful insights according to the geographical distributions of customers. In the instances where customers are randomly located and in instances where some customers are randomly located while others form some clusters, the dual mode delivery system displays lower total cost and higher customer satisfaction. In instances with clustered customers, the dual mode delivery system exhibits narrow competition for the total cost with the delivery system that uses only vehicles. In this case, using drones and vehicles together can reduce the level of dissatisfaction of customers who take their cargo over the time-window. From the view point of strategic flexibility, the dual mode delivery system appears to be more interesting. In meeting the objective of maximizing customer satisfaction, the use of drones and vehicles incurs less cost and requires fewer resources.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.1-7
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• 2015

This research is to develop a possible process to apply k-means clustering to an efficient vehicle routing process under time varying vehicle moving speeds. Time varying vehicle moving speeds are easy to find in metropolitan area. There is a big difference between the moving time requirements of two specific delivery points. Less delivery times are necessary if a delivery vehicle moves after or before rush hours. Various vehicle moving speeds make the efficient vehicle route search process extremely difficult to find even for near optimum routes due to the changes of required time between delivery points. Delivery area division is designed to simplify this complicated VRPs due to time various vehicle speeds. Certain divided area can be grouped into few adjacent divisions to assume that no vehicle speed change in each division. The vehicle speeds moving between two delivery points within this adjacent division can be assumed to be same. This indicates that it is possible to search optimum routes based upon the distance between two points as regular traveling salesman problems. This makes the complicated search process simple to attack since few local optimum routes can be found and then connects them to make a complete route. A possible method to divide area using k-means clustering is suggested and detailed examples are given with explanations in this paper. It is clear that the results obtained using the suggested process are more reasonable than other methods. The suggested area division process can be used to generate better area division promising improved vehicle route generations.

• IE interfaces
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• v.13 no.3
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• pp.455-461
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• 2000

Logistics systems can be evaluated by how productively distribution center operates, how promptly transportation vehicle dispatches, how efficiently facility layout is, and so on. In this paper, a practical vehicle routing scheme with fixed delivery time and fixed vehicle routes is introduced. The method helps the distribution center reduce logistics cost with respect to dispatching vehicles, and satisfy the customer with pre-determined delivery time window constraints. A case study has shown that the proposed scheme not only generates a feasible schedule with time windows, but also balances material flow in warehouse.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.24-33
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• 2011

In this paper, we consider the Location-Routing Problem with simultaneous pickup and delivery (LRPSPD) which is a general case of the location-routing problem. The LRPSPD is defined as finding locations of the depots and designing vehicle routes in such a way that pickup and delivery demands of each customer must be performed with same vehicle and the overall cost is minimized. Since the LRPSPD is an NP-hard problem, we propose a hybrid heuristic approach based on genetic algorithms (GA) and simulated annealing (SA) to solve the problem. To evaluate the performance of the proposed approach, we conduct an experimental study and compare its results with those obtained by a branch-and-cut algorithm on a set of instances derived from the literature. Computational results indicate that the proposed hybrid algorithm is able to find optimal or very good quality solutions in a reasonable computation time.

• Journal of Distribution Science
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• v.10 no.12
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• pp.19-24
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• 2012

Purpose - This paper discusses the heterogeneous fixed fleet vehicle routing problem with pick-up and delivery (HFFVRPPD), for vehicles with different capacities, fixed costs, and travel costs. Research Design, data, methodology - This paper made nine assumptions for establishing a mathematical model to describe HFFVRPPD. It established a practical mathematical model, and because of the non-deterministic polynomial-time hard (NP-hard), improved the traditional simulated annealing algorithm and tested a new algorithm using a certain scale model. Result - We calculated the minimum cost of the heterogeneous fixed fleet vehicle routing problem (HFFVRP) with a single task and, on comparing the results with the actual HFFVRP for the single task alone, observed that the total cost of HFFVRPPD reduced significantly by 46.7%. The results showed that the new algorithm provides better solutions and stability. Conclusions - This paper, by comparing the HFFVRP and HFFVRPPD results, highlights certain advantages of using HFFVRPPD in physical distribution enterprises, such as saving distribution vehicles, reducing logistics cost, and raising economic benefits.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.40-48
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• 2022

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab's AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS's complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab's production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab's AMHS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.16 no.28
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• pp.195-202
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• 1993

This paper considers the vehicle routing problem taking account of not only delivery but pick-up at the same time. A mathematical formulation is presented for finding the route which satisfies all the demands of customers and enables picking up most containers without exceeding the capacity of the vehicle. A table comparing traveling distance and the pick-up amount is provided by using heuristic method, which will be of help to the decision makers.

• 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.