• International conference on construction engineering and project management
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• 2009.05a
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• pp.400-408
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• 2009

Simulation applications for analyzing the productivity of construction operations at operation level and project schedules at project level are crucial methods in project management. The application at two different levels should be very tightly linked to each other in practice. However, appropriate integration at the levels is not achieved in that existing systems do not support to integrate operation models into a schedule model. This paper presents a new approach named to Discrete Event Simulation-Nesting modeling approach, which supports not only productivity analysis at operation level but also schedule management at a project level. The system developed by the authors allows creating operation models at the operation level, maintaining them in operation model library, executing sensitivity analysis to find the behaviors of the operation models when different combination of resources are used as existing DES systems do. On top of the conventional functions, the new system facilitates to find the optimum solution of resource combinations which satisfy the user's interest by computing the hourly productivity and the hourly cost of the operation. By drag-and-dropping an operation model kept in the operation model library, the operation models are integrated into an activity of the schedule model. When a complete schedule model is established by nesting operation models into the schedule model, stochastic simulation based scheduling is executed. A case study is presented to demonstrate the new simulation system and verify the validity of the system.

• Journal of Construction Engineering and Project Management
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• v.5 no.2
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• pp.11-15
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• 2015

The Last PlannerSystem (LPS) has been implemented on construction projects to increase work flow reliability, a precondition for project performance againstproductivity and progress targets. The LPS encompasses four tiers of planning processes:master scheduling, phase scheduling, lookahead planning, and commitment / weeklywork planning. This research highlights deficiencies in the current implementation of LPS including poor lookahead planning which results in poor linkage between weeklywork plans and the master schedule. This poor linkage undetermines the ability of theweekly work planning process to select for execution tasks that are critical to projectsuccess. As a result, percent plan complete (PPC) becomes a weak indicator of project progress. The purpose of this research is to improve lookahead planning (the bridgebetween weekly work planning and master scheduling), improve PPC, and improve theselection of tasks that are critical to project success by increasing the link betweenShould, Can, Will, and Did (components of the LPS), thereby rendering PPC a betterindicator of project progress. The research employs the case study research method to describe deficiencies inthe current implementation of the LPS and suggest guidelines for a better application ofLPS in general and lookahead planning in particular. It then introduces an analyticalsimulation model to analyze the lookahead planning process. This is done by examining the impact on PPC of increasing two lookahead planning performance metrics: tasksanticipated (TA) and tasks made ready (TMR). Finally, the research investigates theimportance of the lookahead planning functions: identification and removal ofconstraints, task breakdown, and operations design.The research findings confirm the positive impact of improving lookaheadplanning (i.e., TA and TMR) on PPC. It also recognizes the need to perform lookaheadplanning differently for three types of work involving different levels of uncertainty:stable work, medium uncertainty work, and highly emergent work.The research confirms the LPS rules for practice and specifically the need to planin greater detail as time gets closer to performing the work. It highlights the role of LPSas a production system that incorporates deliberate planning (predetermined andoptimized) and situated planning (flexible and adaptive). Finally, the research presents recommendations for production planningimprovements in three areas: process related, (suggesting guidelines for practice),technical, (highlighting issues with current software programs and advocating theinclusion of collaborative planning capability), and organizational improvements(suggesting transitional steps when applying the LPS).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.949-966
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• 2013

Recently, Wireless Body Area Network (WBAN) has promise to revolutionize human daily life. The need for multiple sensors and constant monitoring lead these systems to be energy hungry and expensive with short operating lifetimes. In this paper, we offer a review of existing work of WBAN and focus on energy-aware management in it. We emphasize that nodes computation, wireless communication, topology deployment and energy scavenging are main domains for making a long-lived WBAN. We study the popular power management technique Dynamic Voltage and Frequency Scaling (DVFS) and identify the impact of slack time in Dynamic Power Management (DPM), and finally propose an enhanced dynamic power management method to schedule scaled jobs at slack time with the goal of saving energy and keeping system reliability. Theoretical and experimental evaluations exhibit the effectiveness and efficiency of the proposed method.

• IE interfaces
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• v.13 no.3
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• pp.358-377
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• 2000

A supply chain is a network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products, and the distribution of these finished products to customers. SCM(Supp1y Chain Management), by definition, encompasses all activities associated with moving goods, from the raw materials stage through to the end user. It includes source and procurement, production scheduling, order processing, inventory management, transportation, warehousing, and customer service. Importantly, it encompasses the information systems used to monitor these activities. In this paper, the present situation and problems of marketing process in Korean agricultural environments were reviewed through a systematic methodology, and then we proposed a new business process for solving these problems by appling a supply chain management. We expect this supply chain management system applied to agricultural marketing process can improve significantly the rationality and transparency of Korean agricultural marketing structure.

• Journal of Construction Engineering and Project Management
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• v.7 no.1
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• pp.1-12
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• 2017

Construction delay is one of the basic constrains to achieve the project objectives in developing countries. This study aims to find the causes and effects of construction delays in developing countries. A thorough literature review has been done following the content analysis method. The relevant literature of 28 developing countries was collected from the scholarly journals published in the period of 2006 to 2016. The different developing countries are grouped into three geographic regions, i.e. South and Southeast Asia, Middle East, and Africa. In these regions, total 53 potential causes of delay under 8 major groups are identified. Frequency and ranking of these factors have been done. The factors, delay in progress payment by owner, contractors' cash flow problem, improper planning and scheduling, poor site management, and change order by owner during construction, are acknowledged as critical causes of delay in developing countries. This study will assist both academic and professional experts providing more insight about the construction delays and project management in developing countries.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.127-128
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• 2019

The cost of procurement in construction projects varies depending on the type of project, but the proportion of the cost component is very large. Therefore, efficient procurement system operation affects the success of the project. However, difficulties arise in procurement management due to problems such as availability of data due to limited information, inaccurate scheduling and integration of costs. Therefore, the purpose of this study is to develop a procurement management system to help efficient communication decision by combining Big Data which can analyze a lot of information and Block Chain technology which can secure information and record reliability to satisfy the above requirements. The results of this study are used to develop a system to develop academically improved procurement management system and practically to develop a system to secure business competitiveness and to facilitate rapid communication among project participants.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.201-208
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• 2011

Today's highly competitive construction scenario forces all the major players in the field to take up multiple projects which have put an undue pressure on the resources available within the organization. Under such a situation, there are many instances where in the resource requirement exceeds its availability due to multiple activities (with same resource requirement) which are scheduled to start simultaneously and thus results in the constrained resource becoming a bottleneck of the project. As a consequence of sharing resources, this paper studies the impact on the completion date of two similar projects under two different conditions, the first one resulting in a postponed end date and the second without any postponement. The resource utilization, the possibility of substitution of a resource and its subsequent impact on the deadline of the project is analyzed under these two circumstances. The study is done on a Critical Chain Project Management (CCPM) platform instead of leaving the schedule with a traditional Critical Path Method (CPM) finish, which gives an added advantage of validating the robustness of the emerging CCPM trend in the field of resource management.

• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.15-27
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• 2020

This study starts with the idea that the process of creating a Gantt Chart for schedule planning is similar to Tetris game with only a straight line. In Tetris games, the X axis is M machines and the Y axis is time. It is assumed that all types of orders can be worked without separation in all machines, but if the types of orders are different, setup cost will be incurred without delay. In this study, the game described above was named Gantris and the game environment was implemented. The AI-scheduling table through in-depth reinforcement learning compares the real-time scheduling table with the human-made game schedule. In the comparative study, the learning environment was studied in single order list learning environment and random order list learning environment. The two systems to be compared in this study are four machines (Machine)-two types of system (4M2T) and ten machines-six types of system (10M6T). As a performance indicator of the generated schedule, a weighted sum of setup cost, makespan and idle time in processing 100 orders were scheduled. As a result of the comparative study, in 4M2T system, regardless of the learning environment, the learned system generated schedule plan with better performance index than the experimenter. In the case of 10M6T system, the AI system generated a schedule of better performance indicators than the experimenter in a single learning environment, but showed a bad performance index than the experimenter in random learning environment. However, in comparing the number of job changes, the learning system showed better results than those of the 4M2T and 10M6T, showing excellent scheduling performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.312-321
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• 2018

The Korean DR market proposes suppression of peak demand under reliability crisis caused a natural disaster or unexpected power plant accidents as well as saving power plant construction costs and expanding amount of reserve as utility's perspective. End-user is notified a DR event signal DR execution before one hour, and executes DR based on requested amount of load reduction. This paper proposes a DR energy management algorithm that can be scheduled the optimal operations of chiller system and ESS in the next day considering the TOU tariff and DR scheme. In this DR algorithm is divided into two scheduling's; day-ahead operation scheduling with temperature forecasting error and operation rescheduling on DR operation. In day-ahead operation scheduling, the operations of DR resources are scheduled based on the finite number of ambient temperature scenarios, which have been generated based on the historical ambient temperature data. As well as, the uncertainties in DR event including requested amount of load reduction and specified DR duration are also considered as scenarios. Also, operation rescheduling on DR operation day is proposed to ensure thermal comfort and the benefit of a COB owner. The proposed method minimizes the expected energy cost by a mixed integer linear programming (MILP).

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.17-27
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• 2022

In this paper, ED-H algorithm, an optimal real-time scheduling algorithm dealing with the characteristics of the integrated energy harvester system with a capacitor, is extended to satisfy the time constraint under the blackout state which is a deliberate power-off state by an intelligent power management unit adopted in the system. If the power supply system does not have enough energy, it temporarily shuts off the power supply to protect the circuit and capacitor and resumes the supply again when the capacitor is fully charged, which may delay the task execution during these blackout states by calculating the time according to the occurrence of the events. To mitigate the problem, even if task execution is delayed by the original ED-H algorithm, the remaining time of the subsequent time units no longer can afford to delay the execution of the task is predicted in the extended algorithm and the task is forced to be scheduled to meet the time deadline. According to the simulation results, it is confirmed that the algorithm proposed in this paper has a high scheduling performance increase of 0.4% to 7.7% depending on the characteristics of the set of tasks compared to the ED-H.