• Journal of Distribution Science
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• v.16 no.2
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• pp.19-24
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• 2018

Purpose - Drone delivery is expected to revolutionize the supply chain industry. This paper aims to introduce a collaborative parcel delivery problem by truck and drone (hereinafter called "TDRP") and propose a novel heuristic method to solve the problem. Research design, data, and methodology - To show the effectiveness of collaborative delivery by truck and drone, we generate a toy problem composed of 9 customers and the speed of drone is assumed to be two times faster than truck. We compared the delivery completion times by 'truck only' case and 'truck and drone' case by solving the optimization problem respectively. Results - We provide literature reviews for truck and drone routing problem for collaborative delivery and propose a novel and original heuristic method to solve the problem with numerical example. By numerical example, collaborative delivery is expected to reduce delivery completion time by 12~33% than 'truck only' case. Conclusions - In this paper, we introduce the TDRP in order for collaborative delivery to be effective and propose a novel and original heuristic method to solve the problem. The results of research will be help to develop effective heuristic solution and optimize the parcel delivery by using drone.

• Journal of Distribution Science
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• v.15 no.2
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• pp.15-20
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• 2017

Purpose - This paper aims to suggest a delivery constrained internet shopping optimization problem (DISOP) which must be solved for online recommendation system to provide a customized service considering cost and delivery conditions at the same time. Research design, data, and methodology - To solve a (DISOP), we propose a multi-objective formulation and a solution approach. By using a commercial optimization software (LINDO), a (DISOP) can be solved iteratively and a pareto optimal set can be calculated for real-sized problem. Results - We propose a new research problem which is different with internet shopping optimization problem since our problem considers not only the purchasing cost but also delivery conditions at the same time. Furthermore, we suggest a multi-objective mathematical formulation for our research problem and provide a solution approach to get a pareto optimal set by using numerical example. Conclusions - This paper proposes a multi-objective optimization problem to solve internet shopping optimization problem with delivery constraint and a solution approach to get a pareto optimal set. The results of research will contribute to develop a customized comparison and recommendation system to help more easy and smart online shopping service.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.106-116
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• 2021

This paper considers a joint problem for blood inventory planning at hospitals and blood delivery planning from blood centers to hospitals, in order to alleviate the blood service imbalance between big and small hospitals being occurred in practice. The joint problem is to determine delivery timing, delivery quantity, delivery means such as medical drones and legacy blood vehicles, and inventory level to minimize inventory and delivery costs while satisfying hospitals' blood demand over a planning horizon. This problem is formulated as a mixed integer programming model by considering practical constraints such as blood lifespan and drone specification. To solve the problem, this paper employs a Lagrangian relaxation technique and suggests a time efficient Lagrangian heuristic algorithm. The performance of the suggested heuristic is evaluated by conducting computational experiments on randomly-generated problem instances, which are generated by mimicking the real data of Korean Red Cross in Seoul and other reliable sources. The results of computational experiments show that the suggested heuristic obtains near-optimal solutions in a shorter amount of time. In addition, we discuss the effect of changes in the length of blood lifespan, the number of planning periods, the number of hospitals, and drone specifications on the performance of the suggested Lagrangian heuristic.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.111-117
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• 2021

The delivery problem on a graph is that of minimizing the object delivery time from one vertex to another vertex on a graph with m vertices using n various speed robot agents. In this paper, we propose two optimal solution algorithms for the delivery problem on a graph with time complexity of O(㎥n) and O(㎥). After preprocessing to obtain the shortest path for all pairs of the graph, our algorithm processed by obtaining the shortest delivery path in the order of the vertices with the least delivery time. Assuming that the graph reflects the terrain on which to solve the problem, our O(㎥) algorithm actually has a time complexity of O(㎡n) as only one preprocessing is required for the various deployment of n robot agents.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.279-282
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• 2001

This paper is concerned with Vehicle Routing Problem to minimize the total delivery waiting time of customers. The delivery waiting time is the time taken to travel from the depot to the customer, which is important for the delivery of the perishing products or foods requiring freshness. We construct a mixed integer linear programming formulation of this problem, and CR(Clustering first Routing second) heuristic and SPH(set partitioning heuristic) are suggested. the results of computational experiments showed that these heuristics find successfully favorable solutions, in much shorter time than optimal.

• 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.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.28-34
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• 2003

The fundamental design issues that arise in the pickup and delivery system planning are optimizing the system with minimum cost and maximum throughput and service level. This study is concerned with the development of pickup and delivery system with customer responsive service level, DCM(Demand Chain Management). The distribution process and service map are consisted of manufacturing, warehousing, and pickup and delivery. First we formulated the vehicle pickup and delivery problem using GIS-VRP method so as to satisfy the customer service requests. Second, we developed a GUI-type computer program using AVL, automated vehicle location system. The computational results show that the proposed method is very effective on a set of test problems.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.167-178
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• 2019

In this paper, we study the multi-objectives vehicle and drone routing problem with time windows, MOVDRPTW for short, which is defined in an urban delivery network. We consider the dual modal delivery system consisting of drones and vehicles. Drones are used as a complement to the vehicle and operate in a point to point manner between the depot and the customer. Customers make various requests. They prefer to receive delivery services within the predetermined time range and some customers require fast delivery. The purpose of this paper is to investigate the effectiveness of the delivery strategy of using drones and vehicles together with a multi-objective measures. As experiment datasets, we use the instances generated based on actual courier delivery data. We propose a hybrid multi-objective evolutionary algorithm for solving MOVDRPTW. Our results confirm that the vehicle-drone mixed strategy has 30% cost advantage over vehicle only strategy.

• 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 the Korea Safety Management & Science
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• v.15 no.2
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• pp.193-204
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• 2013

The growing logistics strategy of a company is to optimize their vehicle route scheduling in their supply chain system. It is very important to analyze for continuous pickups and delivery vehicle scheduling. This paper is a computational study to investigate the effectiveness of continuous pickups and delivery vehicle routing problems. These scheduling problems have 3 subproblems; Inbound Vehicle Routing Problem with Makespan and Pickup, Line-haul Network Problem, and Outbound Vehicle Routing Problem with Delivery. In this paper, we propose 5 heuristic Algorithms; Selecting Routing Node, Routing Scheduling, Determining Vehicle Type with Number and Quantity, and Modification Selecting Routing Node. We apply these Algorithms to S vehicle company. The results of computational experiments demonstrate that proposed methods perform well and have better solutions than other methods considering the basic time and due-date.