scholarly journals Multiproject Resources Allocation Model under Fuzzy Random Environment and Its Application to Industrial Equipment Installation Engineering

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Jun Gang ◽  
Jiuping Xu ◽  
Yinfeng Xu
Keyword(s):  
Resource Allocation ◽  
Random Environment ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Practical Case ◽  
Industrial Equipment ◽  
Allocation Model ◽  
Proposed Model ◽  
Resource Costs ◽  
Fuzzy Random

This paper focuses on a multiproject resource allocation problem in a bilevel organization. To solve this problem, a bilevel multiproject resource allocation model under a fuzzy random environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the company manager aims to allocate the company's resources to multiple projects to achieve the lowest cost, which include resource costs and a tardiness penalty. On the lower level, each project manager attempts to schedule their resource-constrained project, with minimization of project duration as the main objective. In contrast to prior studies, uncertainty in resource allocation has been explicitly considered. Specifically, our research uses fuzzy random variables to model uncertain activity durations and resource costs. To search for the optimal solution of the bilevel model, a hybrid algorithm made up of an adaptive particle swarm optimization, an adaptive hybrid genetic algorithm, and a fuzzy random simulation algorithm is also proposed. Finally, the efficiency of the proposed model and algorithm is evaluated through a practical case from an industrial equipment installation company. The results show that the proposed model is efficient in dealing with practical resource allocation problems in a bilevel organization.

scholarly journals A Multiobjective Allocation Model for Emergency Resources That Balance Efficiency and Fairness

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yanyan Wang ◽  
Baiqing Sun
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Emergency Relief ◽  
Resource Allocation Model ◽  
Proposed Model ◽  
Emergency Resource

Efficiency and fairness are two important goals of disaster rescue. However, the existing models usually unilaterally consider the efficiency or fairness of resource allocation. Based on this, a multiobjective emergency resource allocation model that can balance efficiency and fairness is proposed. The object of the proposed model is to minimize the total allocating costs of resources and the total losses caused by insufficient resources. Then the particle swarm optimization is applied to solve the model. Finally, a computational example is conducted based on the emergency relief resource allocation after Ya’an earthquake in China to verify the applicability of the proposed model.

Resource Allocation Model Toward Seismic Water Pipeline Risk Mitigation Measures

2019 ◽  
Author(s):  
Elnaz Peyghaleh ◽  
Tarek Alkhrdaji
Keyword(s):  
Resource Allocation ◽  
Risk Analysis ◽  
Seismic Hazard ◽  
Seismic Risk ◽  
Distribution Systems ◽  
Risk Mitigation ◽  
Mitigation Measures ◽  
Seismic Risk Analysis ◽  
Allocation Model ◽  
Proposed Model

Abstract History of earthquake’s damages have illustrated the high vulnerability and risks associated with failure of water transfer and distribution systems. Adequate mitigation plans to reduce such seismic risks are required for sustainable development. The first step in developing a mitigation plan is prioritizing the limited available budget to address the most critical mitigation measures. This paper presents an optimization model that can be utilized for financial resource allocation towards earthquake risk mitigation measures for water pipelines. It presents a framework that can be used by decision-makers (authorities, stockholders, owners and contractors) to structure budget allocation strategy for seismic risk mitigation measures such as repair, retrofit, and/or replacement of steel and concrete pipelines. A stochastic model is presented to establish optimal mitigation measures based on minimizing repair and retrofit costs, post-earthquake replacement costs, and especially earthquake-induced large losses. To consider the earthquake induced loss on pipelines, the indirect loss due to water shortage and business interruption in the industries which needs water is also considered. The model is applied to a pilot area to demonstrate the practical application aspects of the proposed model. Pipeline exposure database, built environment occupancy type, pipeline vulnerability functions, and regional seismic hazard characteristics are used to calculate a probabilistic seismic risk for the pilot area. The Global Earthquake Model’s (GEM) OpenQuake software is used to run various seismic risk analysis. Event-based seismic hazard and risk analyses are used to develop the hazard curves and maps in terms of peak ground velocity (PGV) for the study area. The results of this study show the variation of seismic losses and mitigation costs for pipelines located within the study area based on their location and the types of repair. Performing seismic risk analysis analyses using the proposed model provides a valuable tool for determining the risk associated with a network of pipelines in a region, and the costs of repair based on acceptable risk level. It can be used for decision making and to establish type and budgets for most critical repairs for a specific region.

An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment

2020 ◽  
Vol 13 (5) ◽  
pp. 957-964
Author(s):  
Siva Rama Krishna ◽  
Mohammed Ali Hussain
Keyword(s):  
Resource Allocation ◽  
Real Time ◽  
Cloud Services ◽  
Management Technique ◽  
Cloud Environment ◽  
Allocation Model ◽  
Proposed Model ◽  
Cloud Server ◽  
Different Types ◽  
Multi Level

Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.

scholarly journals Resource allocation model for grid computing environment

2020 ◽  
Vol 6 (2) ◽  
pp. 185
Author(s):  
Ardi Pujiyanta ◽  
Lukito Edi Nugroho ◽  
Widyawan Widyawan
Keyword(s):  
Resource Allocation ◽  
Grid Computing ◽  
Resource Utilization ◽  
Waiting Time ◽  
Resource Use ◽  
Waiting Times ◽  
Allocation Model ◽  
Technical Problems ◽  
Scheduling Policy ◽  
Proposed Model

Grid computing is a collection of heterogeneous resources that is highly dynamic and unpredictable. It is typically used for solving scientific or technical problems that require a large number of computer processing cycles or access to substantial amounts of data. Various resource allocation strategies have been used to make resource use more productive, with subsequent distributed environmental performance increases. The user sends a job by providing a predetermined time limit for running that job. Then, the scheduler gives priority to work according to the request and scheduling policy and places it in the waiting queue. When the resource is released, the scheduler selects the job from the waiting queue with a specific algorithm. Requests will be rejected if the required resources are not available. The user can re-submit a new request by modifying the parameter until available resources can be found. Eventually, there is a decrease in idle resources between work and resource utilization, and the waiting time will increase. An effective scheduling policy is required to improve resource use and reduce waiting times. In this paper, the FCFS-LRH method is proposed, where jobs received will be sorted by arrival time, execution time, and the number of resources needed. After the sorting process, the work will be placed in a logical view, and the job will be sent to the actual resource when it executes. The experimental results show that the proposed model can increase resource utilization by 1.34% and reduce waiting time by 20.47% when compared to existing approaches. This finding could be beneficially implemented in cloud systems resource allocation management.

Optimization Model and Algorithm Help to Screen and Treat Sexually Transmitted Diseases

2012 ◽  
pp. 233-250
Author(s):  
Kun Zhao ◽  
Guantao Chen ◽  
Thomas Gift ◽  
Guoyu Tao
Keyword(s):  
Resource Allocation ◽  
Sexually Transmitted Diseases ◽  
Real Life ◽  
Optimal Solution ◽  
The United States ◽  
Allocation Model ◽  
Sexually Transmitted ◽  
Resource Allocation Model ◽  
Excel Solver ◽  
Treat All

Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) are two common sexually transmitted diseases among women in the United States. Publicly funded programs usually do not have enough money to screen and treat all patients. Therefore, the authors propose a new resource allocation model to assist clinical managers to make decisions on identifying at-risk population groups, as well as selecting a screening and treatment strategy for CT and GC patients under a fixed budget. At the same time, the authors also develop a two-step branch-and-bound algorithm tailor-made for our model. Running on real-life data, the algorithm calculates the optimal solution within a very short time. The new algorithm also improves the accuracy of an approximate solution obtained by Excel Solver. This study has shown that a resource allocation model and algorithm might have a significant impact on real clinical issues.

Emergency Logistics System Based on Bilevel Optimization Model

2013 ◽  
Vol 694-697 ◽  
pp. 3605-3609
Author(s):  
Bo Liu ◽  
Bo Li ◽  
Yan Li
Keyword(s):  
Genetic Algorithm ◽  
Bilevel Programming ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Bilevel Optimization ◽  
Logistics System ◽  
Emergency Logistics ◽  
Proposed Model ◽  
Bilevel Programming Model

A bilevel programming model is established to determine the emergency storage centers location and the resource supply plan of the provincial and municipal levels by the collaborative mode of the vertical supply and lateral transfer for the emergency logistics system in the unusual emergencies. And the optimal solution is obtained by the hybrid genetic algorithm. Finally, the case shows the effectiveness of the proposed model and its algorithm.

scholarly journals A Reliability-Based Resource Allocation Model for Transportation Networks Affected by Natural Disasters

1970 ◽  
Vol 24 (6) ◽  
pp. 505-513 ◽  
Author(s):  
Afshin Shariat Mohaymany ◽  
Shideh Ehteshamrad ◽  
Mohsen Babaei
Keyword(s):  
Resource Allocation ◽  
Natural Disasters ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Travel Time Reliability ◽  
Supply Uncertainty ◽  
Allocation Model ◽  
Resource Allocation Model ◽  
Proposed Model ◽  
Do So

This paper is concerned with the development of a resource allocation model for road networks under supply uncertainty caused by natural disasters. An optimization model is proposed to determine which links should be invested for the system to perform better while encountering natural disasters such as earthquake. The connectivity reliability and travel time reliability of origin-destinations (ODs) are selected as performance measures to do so. The Monte-Carlo simulation method is used to estimate the reliability measures and the model is solved by the genetic algorithm. The proposed model is implemented on a test network to demonstrate the results.

Research on Resource Allocation Strategy of PaaS Platform

2019 ◽  
Vol 12 (1) ◽  
pp. 63-76
Author(s):  
Hongen Peng ◽  
Yabin Xu
Keyword(s):  
Resource Allocation ◽  
Waiting Time ◽  
Ant Colony Algorithm ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Heuristic Scheduling ◽  
Local Optimal Solution ◽  
Resource Allocation Strategy

In order to allocate elastic resource to the application of PaaS platform, the authors analyze the key technologies and the particularity of resource scheduling in PaaS platform, and design an application-oriented resource allocation model and heuristic scheduling algorithm based on an ant colony algorithm. Different from the existing resource allocation methods based on virtual machines in IaaS, the scheduling strategy is based on Application in PaaS platform. According to the analysis of the application layout, the heuristic algorithm is used to minimize the number of application migration and reduce the waiting time of the task. In order to avoid falling into the loop or local optimal solution, the authors also used a tabu search technique. The results of comparative experiments show that, this strategy has higher resource utilization and shorter task waiting time.

scholarly journals Multi-phase and Integrated Multi-objective Cyclic Operating Room Scheduling Based on an Improved NSGA-ⅡApproach

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 599 ◽  
Author(s):  
Qian Lu ◽  
Xiaomin Zhu ◽  
Dong Wei ◽  
Kaiyuan Bai ◽  
Jinsheng Gao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Operating Room ◽  
Programming Model ◽  
Second Phase ◽  
Nsga Ii ◽  
Surgical Specialty ◽  
Allocation Model ◽  
Multi Objective ◽  
Proposed Model ◽  
Multi Phase

The operating room (OR) is an important department in a hospital, and the scheduling of surgeries in ORs is a challenging combinatorial optimization problem. In this paper, we address the problem of multiple resource allocation of ORs and propose a surgery scheduling scheme for OR units. To solve this problem, a multi-phase and integrated multi-objective linear programming model is proposed. The first phase of the proposed model is a resource allocation model, which mainly focuses on the allocation of ORs for each surgical specialty (SS). Based on the results of the first phase, the second phase is the cyclic Master Surgical Schedule model, which aims to schedule the surgeries in each SS. The proposed models are solved by the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which was improved. Finally, two numerical experiments based on practical data are provided to verify the effectiveness of the proposed models as well as to evaluate the performance of the improved NSGA-II. Our final results illustrate that our proposed model can provide hospital managers with a series of “optimal” solutions to effectively allocate relevant resources and ORs for surgeries, and they show that the improved NSGA-II has high computational efficiency and is more suitable in solving larger-scale problems.

Sign in / Sign up

Export Citation Format

Share Document