scholarly journals Resource Leveling Based on Backward Controlling Activity in Line of Balance

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Lihui Zhang ◽  
Yaping Tang ◽  
Jianxun Qi
Keyword(s):  
Resource Management ◽  
Balance Method ◽  
Design Algorithm ◽  
Project Duration ◽  
Resource Leveling ◽  
Use Of Resources ◽  
Matlab Program ◽  
Proposed Model ◽  
Pipeline Project ◽  
Line Of Balance

The line of balance method that provides continuous and uninterrupted use of resources is one of the best methods for repetitive project resource management. This paper develops a resource leveling algorithm based on the backward controlling activity in line of balance. The backward controlling activity is a kind of special activity, and if its duration is prolonged the project duration could be reduced. It brings two advantages to the resource leveling: both the resource allocated on the backward activity and the project duration are reduced. A resource leveling algorithm is presented which permits the number of crews of the backward controlling activity to be reduced until the terminal situation is reached, where the backward controlling activity does not exist or the number of crews cannot be reduced. That adjustment enables the productivity of all activities to be consistent. An illustrative pipeline project demonstrates the improvement in resource leveling. And this study designed a MATLAB program to execute the design algorithm. The proposed model could help practitioners to achieve the goals of both resource leveling and project duration reduction without increasing any resource.

scholarly journals A Cost Optimization Model for Multiresource Leveling Problem without Project Duration Constraint

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Qingyou Yan ◽  
Qian Zhang ◽  
Xin Zou
Keyword(s):  
Optimization Model ◽  
Cost Optimization ◽  
Resource Usage ◽  
Project Duration ◽  
Resource Demand ◽  
Resource Leveling ◽  
Proposed Model ◽  
Liquidated Damages ◽  
The Relationship

The study of traditional resource leveling problem aims at minimizing the resource usage fluctuations and obtaining sustainable resource supplement, which is accomplished by adjusting noncritical activities within their start and finish time. However, there exist limitations in terms of the traditional resource leveling problem based on the fixed project duration. This paper assumes that the duration can be changed in a certain range and then analyzes the relationship between the scarce resource usage fluctuations and project cost. This paper proposes an optimization model for the multiresource leveling problem. We take into consideration five kinds of cost: the extra hire cost when the resource demand is greater than the resource available amount, the idle cost of resource when the resource available amount is greater than the resource demand, the indirect cost related to the duration, the liquidated damages when the project duration is extended, and the incentive fee when the project duration is reduced. The optimal objective of this model is to minimize the sum of the aforementioned five kinds of cost. Finally, a case study is examined to highlight the characteristic of the proposed model at the end of this paper.

A project buffer and resource management model in energy sector; a case study in construction of a wind farm project

2020 ◽  
Vol 14 (6) ◽  
pp. 1123-1142
Author(s):  
Shakib Zohrehvandi ◽  
Mario Vanhoucke ◽  
Mohammad Khalilzadeh
Keyword(s):  
Resource Management ◽  
Wind Farm ◽  
Average Duration ◽  
Energy Sector ◽  
Management Model ◽  
Second Phase ◽  
Content Type ◽  
Project Duration ◽  
Resource Leveling ◽  
Buffer Sizing

Purpose This study aims to introduce an efficient project buffer and resource management (PBRM) model for project resource leveling and project buffer sizing and controlling of project buffer consumption of a wind power plant project to achieve a more realistic project duration. Design/methodology/approach The methodology of this research consists of three main phases. In the first phase of the research methodology, resource leveling is done in the project and resource conflicts of activities are identified. In the second phase, the project critical chain is determined, and the appropriate size of the project buffer is specified. In the third phase of the methodology, buffer consumption is controlled and monitored during the project implementation. After using the PBRM method, the results of this project were compared with those of the previous projects. Findings According to the obtained results, it can be concluded that using PBRM model in this wind turbine project construction, the project duration became 25 per cent shorter than the scheduled duration and also 29 per cent shorter than average duration of previous similar projects. Research limitations/implications One of the major problems with projects is that they are not completed according to schedule, and this creates time delays and losses in the implementation of projects. Today, as projects in the energy sector, especially renewable projects, are on the increase and also we are facing resource constraint in the implementation of projects, using scheduling techniques to minimize delays and obtain more realistic project duration is necessary. Practical implications This research was carried out in a wind farm project. In spite of the initial plan duration of 142 days and average duration of previous similar projects of 146 days, the project was completed in 113 days. Originality/value This paper introduces a practical project buffer and resource management model for project resource leveling, project buffer sizing and buffer consumption monitoring to reach a more realistic schedule in energy sector. This study adds to the literature by proposing the PBRM model in renewable energy sector.

scholarly journals STUDY AND APPLY ACO ALGORITHM IN TIME-COST OPTIMIZATION OF CONSTRUCTION PROJECT

2010 ◽  
Vol 13 (1) ◽  
pp. 17-30
Author(s):  
Luan Hong Pham ◽  
Nhan Thanh Duong
Keyword(s):  
Cost Optimization ◽  
Construction Project ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Time Cost ◽  
Project Duration ◽  
Proposed Model ◽  
Feasible Solutions ◽  
Computational Resources

Time-cost optimization problem is one of the most important aspects of construction project management. In order to maximize the return, construction planners would strive to optimize the project duration and cost concurrently. Over the years, many researches have been conducted to model the time-cost relationships; the modeling techniques range from the heuristic method and mathematical approach to genetic algorithm. In this paper, an evolutionary-based optimization algorithm known as ant colony optimization (ACO) is applied to solve the multi-objective time-cost problem. By incorporating with the modified adaptive weight approach (MAWA), the proposed model will find out the most feasible solutions. The concept of the ACO-TCO model is developed by a computer program in the Visual Basic platforms. An example was analyzed to illustrate the capabilities of the proposed model and to compare against GA-based TCO model. The results indicate that ant colony system approach is able to generate better solutions without making the most of computational resources which can provide a useful means to support construction planners and managers in efficiently making better time-cost decisions.

scholarly journals Effective Factors in the Establishment and Implementation of Patient Electronic Referral System in the Healthcare System: A Qualitative Study

2021 ◽  
Author(s):  
Fatemeh Tajari ◽  
Ghahraman Mahmoudi ◽  
Fatemeh Dabbaghi ◽  
Jamshid Yazdani-Charati
Keyword(s):  
Content Analysis ◽  
Resource Management ◽  
Service Providers ◽  
Referral System ◽  
Successful Implementation ◽  
Structured Interviews ◽  
Health Services Use ◽  
Effective Factors ◽  
Use Of Resources ◽  
Electronic Referral

Background and Purpose: The electronic referral system was established in Iran with the aim of increasing access to care, improving interaction, and efficient use of resources. The purpose of this study was to determine the effective factors in the establishment and implementation of electronic referral system in Iran. Methods: The present study was qualitative with a content analysis approach. In order to collect information, in-depth and semi-structured interviews were used. After interviewing 42 people, the information was saturated. The interviews were transcribed and analyzed, and the main topics were quoted, cited, and coded. The content analysis method was used and all authors participated in the analysis process to avoid bias and agreement. Results: Four main themes and eleven sub-themes were mentioned as effective themes in the implementation of the electronic referral system by the interviewees including resource management (human resource management, financing and equipment, and infrastructure), stewardship (policy-making, inter-sectorial leadership, and intra-sectorial governance), advocacy stakeholders (senior officials and policy-makers related to the program, service providers, community), and social commitment (commitment and accountability of service providers). Conclusions: Successful implementation of electronic referral systems requires the development of an operational plan that correctly identifies the factors affecting the establishment and implementation, as well as the cooperation of all responsible organizations which can help improve the establishment and sustainability of the program. It is suggested that managers and officials active in the field of health services use the results of this study in the establishment and implementation of electronic referral system.

scholarly journals A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

2020 ◽  
Vol 12 (8) ◽  
pp. 3103 ◽  
Author(s):  
Hyojoo Son ◽  
Changwan Kim
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Power System ◽  
Electricity Demand ◽  
Efficient Production ◽  
Power System Planning ◽  
System Planning ◽  
Residential Sector ◽  
Use Of Resources ◽  
Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

scholarly journals Distributed Meta-Brokering P2P Overlay for Scheduling in Cloud Federation

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 852 ◽  
Author(s):  
Sajid Latif ◽  
Syed Mushhad Gilani ◽  
Rana Liaqat Ali ◽  
Misbah Liaqat ◽  
Kwang-Man Ko
Keyword(s):  
Resource Management ◽  
Service Provision ◽  
Large Scale ◽  
Service Providers ◽  
Service Level Agreement ◽  
Service Level ◽  
Distributed Resources ◽  
Proposed Model ◽  
Geographically Distributed ◽  
Application Programming

The interconnected cloud (Intercloud) federation is an emerging paradigm that revolutionizes the scalable service provision of geographically distributed resources. Large-scale distributed resources require well-coordinated and automated frameworks to facilitate service provision in a seamless and systematic manner. Unquestionably, standalone service providers must communicate and federate their cloud sites with other vendors to enable the infinite pooling of resources. The pooling of these resources provides uninterpretable services to increasingly growing cloud users more efficiently, and ensures an improved Service Level Agreement (SLA). However, the research of Intercloud resource management is in its infancy. Therefore, standard interfaces, protocols, and uniform architectural components need to be developed for seamless interaction among federated clouds. In this study, we propose a distributed meta-brokering-enabled scheduling framework for provision of user application services in the federated cloud environment. Modularized architecture of the proposed system with uniform configuration in participating resource sites orchestrate the critical operations of resource management effectively, and form the federation schema. Overlaid meta-brokering instances are implemented on the top of local resource brokers to keep the global functionality isolated. These instances in overlay topology communicate in a P2P manner to maintain decentralization, high scalability, and load manageability. The proposed framework has been implemented and evaluated by extending the Java-based CloudSim 3.0.3 simulation application programming interfaces (APIs). The presented results validate the proposed model and its efficiency to facilitate user application execution with the desired QoS parameters.

A Proposed Model for Predicting Employees’ Performance Using Data Mining Techniques

2020 ◽  
Vol 17 (8) ◽  
pp. 3804-3809
Author(s):  
A. Yovan Felix ◽  
Karthik Reddy Vuyyuru ◽  
Viswas Puli
Keyword(s):  
Data Mining ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Human Resource Professionals ◽  
Quality Factors ◽  
Proposed Model ◽  
Opportune Time ◽  
Using Data ◽  
The Impact

Human Resource Management has gotten one of the basic pastimes of supervisors and chiefs in practically wide variety of corporations to include plans for accurately locating profoundly qualified representatives. In similar way, administrations come to be intrigued about the presentation of these representatives. Particularly to guarantee the fitting person apportioned to the beneficial employment on the opportune time. From right here the enthusiasm of statistics in mining process has been growing that its goal is disclosure of facts from huge measures of statistics. Three fundamental Data Mining strategies were applied for building the arrangement version and distinguishing the quality factors that emphatically impact the exhibition. To get a profoundly actual version, a few trials were achieved dependent on the beyond procedures which can be actualized in WEKA tool for empowering leaders and Human Resource professionals to anticipate and improve the exhibition of their representatives. This paper makes use of Hadoop for the remedy of great measure of data with which may be guaranteed to be able to decide the impact.

Comparison of Search-Based Software Engineering Algorithms for Resource Allocation Optimization

2016 ◽  
Vol 25 (4) ◽  
pp. 629-642 ◽  
Author(s):  
Nazia Bibi ◽  
Zeeshan Anwar ◽  
Ali Ahsan
Keyword(s):  
Resource Allocation ◽  
Software Engineering ◽  
Resource Utilization ◽  
Experimental Results ◽  
Project Cost ◽  
Project Duration ◽  
Search Based Software Engineering ◽  
Resource Leveling ◽  
Pareto Fronts ◽  
Percent Decrease

AbstractA project manager balances the resource allocation using resource leveling algorithms after assigning resources to project activities. However, resource leveling does not ensure optimized allocation of resources. Furthermore, the duration and cost of a project may increase after leveling resources. The objectives of resource allocation optimization used in our research are to (i) increase resource utilization, (ii) decrease project cost, and (iii) decrease project duration. We implemented three search-based software engineering algorithms, i.e. multiobjective genetic algorithm, multiobjective particle swarm algorithm (MOPSO), and elicit nondominated sorting evolutionary strategy. Twelve experiments to optimize the resource allocation are performed on a published case study. The experimental results are analyzed and compared in the form of Pareto fronts, average Pareto fronts, percent increase in resource utilization, percent decrease in project cost, and percent decrease in project duration. The experimental results show that MOPSO is the best technique for resource optimization because after optimization with MOPSO, resource utilization is increased and the project cost and duration are reduced.

Perceived Human Resource Management Practices

2014 ◽  
Vol 13 (1) ◽  
pp. 21-33 ◽  
Author(s):  
Corine Boon ◽  
Frank D. Belschak ◽  
Deanne N. Den Hartog ◽  
Mark Pijnenburg
Keyword(s):  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Time Allocation ◽  
Management Practices ◽  
Governmental Organization ◽  
Extra Effort ◽  
Proposed Model ◽  
Human Resource Management Practices

How employees spend their work time can have important consequences for organizations. Although some research has examined the relationship between human resource management (HRM) and employee absence, we know less about whether HRM also affects employees’ time allocation at work. This study examines the role of perceived HRM and psychological processes in explaining employee absence and time allocation in the form of time spent on core task and contextual activities. Besides an avoidance mechanism, linking perceived HRM to absence via lower satisfaction, we propose an approach mechanism linking perceived HRM to time spent on task and contextual activities via satisfaction and willingness to exert extra effort. A study among 1,626 employees of a Dutch governmental organization supports the proposed model.

Modelling of tri-parameter bidding integrating cost, time and risk: a stochastic approach

2017 ◽  
Vol 22 (2) ◽  
pp. 117-134 ◽  
Author(s):  
Mohamad Rabie ◽  
Sameh El-Sayegh
Keyword(s):  
Stochastic Scheduling ◽  
Stochastic Approach ◽  
Time Parameter ◽  
Content Type ◽  
Project Duration ◽  
Baseline Schedule ◽  
Schedule Flexibility ◽  
Risk Parameter ◽  
Proposed Model ◽  
Schedule Risk

Purpose This paper aims to propose a new tri-parameter bidding model integrating cost, time and risk. The key value of the model is that it remains within the framework of the competitive bidding system while controlling the risk resulting from float loss. Design/methodology/approach The model utilizes stochastic scheduling to quantify the float loss impact at the project level. Prospective bidders are evaluated based on their total combined bid (TCB) including cost, time and risk. The risk parameter is calculated as the relative risk between the bidder’s schedule and the client’s baseline schedule. Findings The results confirmed that choosing the contractor based on the lowest price and time reduces the available float and increases the schedule risks. The probability of completing the project on time dropped from 46 per cent for the baseline schedule to 19 per cent for the bidder with the most compressed schedule. The selected bidder, using the proposed model, has the lowest TCB of cost, time and risk. Results show that adding the risk parameter in the evaluation changed the ranking of the bidders. Research limitations/implications The model does not discuss all project risks that the contractor retains. It focuses on schedule risks that result from shortening project duration. The model focuses on solving the problem with price plus time bidding method by addressing the schedule risk issue. Other criteria, such as sustainability, are not considered. Practical implications The proposed model encourages contractors to pay more attention to the time parameter and the schedule risks resulting from aggressive reduction in project duration. Originality/value Problems arose, in the current complex construction industry, as owners rely solely on price as the award criterion. Recently, the bi-parameter bidding system, A + B, introduced the time parameter to the awarding criteria. However, reducing the project duration by compressing the schedule consumes the float of non-critical activities, which reduces the schedule flexibility of a project. The proposed model allows clients to evaluate potential bidders objectively. Rather than evaluating the bidders based on price, in the conventional low bid system, or based on price and time, as in the A + B system, the bidders are evaluated based on three parameters: price, time and risk.

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