• Journal of Communications and Networks
• /
• v.17 no.3
• /
• pp.247-255
• /
• 2015

In this paper, we propose an adaptive time division multiple access based medium access control (MAC) protocol, called bitmap-assisted shortest job first based MAC (BS-MAC), for hierarchical wireless sensor networks (WSNs). The main contribution of BS-MAC is that: (a) It uses small size time slots. (b) The number of those time slots is more than the number of member nodes. (c) Shortest job first (SJF) algorithm to schedule time slots. (d) Short node address (1 byte) to identify members nodes. First two contributions of BS-MAC handle adaptive traffic loads of all members in an efficient manner. The SJF algorithm reduces node's job completion time and to minimize the average packet delay of nodes. The short node address reduces the control overhead and makes the proposed scheme an energy efficient. The simulation results verify that the proposed BS-MAC transmits more data with less delay and energy consumption compared to the existing MAC protocols.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.13 no.21
• /
• pp.91-100
• /
• 1990

This paper is concerned with the development of optimal algorithms for multi-stage flowshop scheduling problems with sequence dependent setup times. In the previous researches the setup time of a job is considered to be able to begin at the earliest opportunity given a particular sequence at the start of operations. In this paper the setup time of a job is considered to be able to begin only at the completion of that job on the previous machine to reflect the effects of the setup time to the performance measure of sequence dependent setup time flowshop scheduling. The results of the study consist of two areas; first, a general integer programming(IP) model is formulated and a nixed integer linear programming(MILP) model is also formulated by introducing a new binary variable. Second a depth-first branch and bound algorithm is developed. To reduce the computational burdens we use the best heuristic schedule developed by Choi(1989) as the first trial. The experiments for developed algorithm are designed for a 4$\times$3$\times$3 factorial design with 360 observations. The experimental factors are PS(ratio of processing time to setup time), M(number of machines), N(number of jobs).

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.32 no.1
• /
• pp.72-78
• /
• 2009

This paper considers a multiagent scheduling problem under public information where a machine is shared by multiple agents. Each agent has a local objective among the minimization of total completion time and the minimization of maximum. In this problem, it is assumed that scheduling information is public. Therefore an agent can access to complete information of other agents and pursue efficient schedules in a centralized manner. We propose an enumeration scheme to find Pareto optimal schedules and a multiobjective genetic algorithm as a heuristic approach. Experimental results indicate that the proposed genetic algorithm yields close-to Pareto optimal solution under a variety of experimental conditions.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2007.11a
• /
• pp.327-330
• /
• 2007

This paper verifies that the normality assumption that the simulation output data, Project Completion Times (PCTs), follow normal distribution is not always acceptable and the existing belief may lead to misleading results. A risk quantification method, which measures the effect caused by the assumption, relative to the probability distribution of PCTs is implemented as an algorithm in MATLAB. To validate the reliability of the quantification, several series of simulation experiments have been carried out to analyze a set of simulation output data which are obtained from different type of Probability Distribution Function (PDF) assigned to activities'duration in a network. The method facilitates to find the effect of PDF type and its parameters. The procedure necessary for performing the risk quantification method is described in detail along with the findings. This paper contributes to improving the reliability of simulation based scheduling method, as well as increasing the accuracy of analysis results.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.5
• /
• pp.489-496
• /
• 2006

The random-based cleaning algorithm is a simple algorithm widely used in commercial vacuum cleaning robots. This algorithm has two limitations, that is, cleaning takes a long time and there is no guarantee that the cleaning will cover the whole cleaning area. This has lead to customer dissatisfaction. Thus, in recent years, many intelligent cleaning algorithms that takes into consideration information gathered from the cleaning area environment have been proposed. The plowing-based algorithm, which is the most efficient algorithm known to date when there are no obstacles in the cleaning area, has a deficiency that when obstacle prevail, its performance is not guaranteed. In this paper, we propose the Group-k algorithm that is efficient for that situation, that is, when obstacle prevail. The goal is not to complete the cleaning as soon as possible, but to clean the majority of the cleaning area as fast as possible. The motivation behind this is that areas close to obstacles are usually difficult for robots to handle, and hence, many require human assistance anyway In our approach, obstacles are grouped by the complexity of the obstacles, which we refer to as 'complex rank', and then decide the cleaning route based on this complex rank. Results from our simulation-based experiments show that although the cleaning completion time takes longer than the plowing-based algorithm, the Group-k algorithm cleans the majority of the cleaning area faster than the plowing algorithm.

• Management Science and Financial Engineering
• /
• v.5 no.1
• /
• pp.1-25
• /
• 1999

There is a worldwide trend to automate the handling operations in port container terminals in an effort to improve productivity and reduce labor cost. This study iscusses how to apply an AGV(automated guided vehicle) system to the handling of containers in the yard of a port container ter-minal. The main issue of this paper is how to assign tasks of container delivery to AGVs during ship operations in an automated port container terminal. A dual-cycle operation is assumed in which the loading and the discharging operation can be performed alternately. Mixed integer linear program-ming formulations are suggested for the dispatching problem. The completion time of all the dis-charging and loading operations by a quayside crane is minimized, and the minimization of the total travel time of AGVs is also considered as a secondary objective. A heuristic method using useful properties of the dispatching problem is suggested to reduce the computational time. The perfor-mance of the heuristic algorithm is evaluated in light of solution quality and computation time.

• Management Science and Financial Engineering
• /
• v.20 no.2
• /
• pp.33-37
• /
• 2014

A flow shop scheduling problem involves scheduling jobs on multiple machines in series in order to optimize a given criterion. The flow time of a job is the amount of time the job spent before its completion and the stretch of the job is the ratio of its flow time to its processing time. In this paper, a hybrid genetic algorithm (HGA) approach is proposed for minimizing the total stretch in flow shop scheduling. HGA adopts the idea of seed selection and development in order to reduce the chance of premature convergence that may cause the loss of search power. The performance of HGA is compared with that of genetic algorithms (GAs).

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.506-516
• /
• 2005

This paper deals with an investment scheduling problem of maximizing net present value of dividend with reinvestment allowed, where each investment has certain capital requirement and generates deterministic profit. Such deterministic profit is calculated at completion of each investment and then allocated into two parts, including dividend and reinvestment, at each predetermined reinvestment time point. The objective is to make optimal scheduling of investments over a fixed planning horizon which maximizes total sum of the net present values of dividends subject to investment precedence relations and capital limit but with reinvestment allowed. In the analysis, the scheduling problem is transformed to a kind of parallel machine scheduling problem and formulated as an integer programming which is proven to be NP-complete. Thereupon, a depth-first branch-and-bound algorithm is derived. To test the effectiveness and efficiency of the derived algorithm, computational experiments are performed with some numerical instances. The experimental results show that the algorithm solves the problem relatively faster than the commercial software package (CPLEX 8.1), and optimally solves the instances with up to 30 investments within a reasonable time limit.

• Journal of Auto-vehicle Safety Association
• /
• v.16 no.2
• /
• pp.20-26
• /
• 2024

The delivery using drones has been attracting attention because it can innovatively reduce the delivery time from the time of order to completion of delivery compared to the current delivery system, and there have been pilot projects conducted for safe drone delivery. However, the current drone delivery system has the disadvantage of limiting the operational efficiency offered by fully autonomous delivery drones in that drones mainly deliver goods to pre-set landing sites or delivery bases, and the final delivery is still made by humans. In this paper, to overcome these limitations, we propose obstacle detection and landing site selection algorithm based on a vision sensor that enables safe drone landing at the delivery location of the product orderer, and experimentally prove the possibility of station-to-door delivery. The proposed algorithm forms a 3D map of point cloud based on simultaneous localization and mapping (SLAM) technology and presents a grid segmentation technique, allowing drones to stably find a landing site even in places without prior information. We aims to verify the performance of the proposed algorithm through streaming data received from the drone.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.4
• /
• pp.1501-1518
• /
• 2016

This paper presents an effective management of VM (Virtual Machine) for heterogeneous cloud using Common Deployment Model (CDM) brokering mechanism. The effective utilization of VM is achieved by means of task scheduling with VM placement technique. The placements of VM for the physical machine are analyzed with respect to execution time of the task. The idle time of the VMis utilized productively in order to improve the performance. The VMs are also scheduled to maintain the state of the current VM after the task completion. CDM based algorithm maintains two directories namely Active Directory (AD) and Passive Directory (PD). These directories maintain VM with proper configuration mapping of the physical machines to perform two operations namely VM migration and VM roll back. VM migration operation is performed from AD to PD whereas VM roll back operation is performed from PD to AD. The main objectives of the proposed algorithm is to manage the VM's idle time effectively and to maximize the utilization of resources at the data center. The VM placement and VM scheduling algorithms are analyzed in various dimensions of the cloud and the results are compared with iCanCloud model.