Use of float consumption rate in resource leveling of construction projects

The management of resources has been claimed to be as important as scheduling methods.Inefficiency in managing resources may bring about severe delays and cost overruns caused by resource shortages in some cases and/or idle resources in others.Therefore,resources should be utilized efficiently to prevent project failures.Resource leveling is one of the approaches that are used for the management of resources.It aims to minimize fluctuations,peaks,and valleys in resource utilization without changing the completion time of a project and the number of resources required.Although the main principle behind traditional resource leveling is achieving an even flow of resources while the original project duration remains unchanged,the literature supports the need to develop an efficient model that discriminates among the activities that are selected for participation in resource leveling.For this purpose,this study has developed a model that considers the float consumption rates of activities in resource leveling.The float consumption rate is the percentage that is set to determine the maximum amount of float which will be consumed to shift the start time of the activity.The proposed model allows a scheduler to assign float consumption rates to each activity that can be used during the resource leveling procedure.When the required information is inputted,the proposed model automatically changes the required daily resources as it shifts the noncritical activities along their available total float times.The proposed model is expected to minimize the likelihood of severe delays and cost overruns.The model is demonstrated by constructing a network and its resource utilization histograms.

Establish Green Industry System,Improve the Comprehensive Utilization Level of Salt Lake Resources,and Create A New Pattern of Green Sustainable Development in Qaidam Basin

Qaidam Basin in Qinghai Province has rich multiple complex resources with salt lakes as the core.These resources form a special condition for the development of green economy,having rare and particular nature.The

Measurement and assessment of water resources carrying capacity in Henan Province, China

As demands on limited water resources intensify, concerns are being raised about water resources carrying capacity（WRCC）, which is defined as the maximum sustainable socioeconomic scale that can be supported by available water resources and while maintaining defined environmental conditions. This paper proposes a distributed quantitative model for WRCC, based on the principles of optimization, and considering hydro-economic interaction, water supply, water quality, and socioeconomic development constraints. With the model, the WRCCs of 60 subregions in Henan Province were determined for different development periods. The results showed that the water resources carrying level of Henan Province was suitably loaded in 2010, but that the province would be mildly overloaded in 2030 with respect to the socioeconomic development planning goals. The restricting factors for WRCC included the available water resources, the increasing rate of GDP, the urbanization ratio, the irrigation water utilization coefficient, the industrial water recycling rate, and the wastewater reuse rate, of which the available water resources was the most crucial factor. Because these factors varied temporally and spatially, the trends in predicted WRCC were inconsistent across different subregions and periods.

An extended particle swarm optimization algorithm based on coarse-grained and fine-grained criteria and its application

In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.

An Optimal Resource Provision Policy in Cloud Computing Based on Customer Profiles

Conventional resource provision algorithms focus on how to maximize resource utilization and meet a fixed constraint of response time which be written in service level agreement（SLA）.Unfortunately,the expected response time is highly variable and it is usually longer than the value of SLA.So,it leads to a poor resource utilization and unnecessary servers migration.We develop a framework for customer-driven dynamic resource allocation in cloud computing.Termed CDSMS（customer-driven service manage system）,and the framework’s contributions are twofold.First,it can reduce the total migration times by adjusting the value of parameters of response time dynamically according to customers’profiles.Second,it can choose a best resource provision algorithm automatically in different scenarios to improve resource utilization.Finally,we perform a serious experiment in a real cloud computing platform.Experimental results show that CDSMS provides a satisfactory solution for the prediction of expected response time and the interval period between two tasks and reduce the total resource usage cost.

Developing a new model for simultaneous scheduling of two grand projects based on game theory and solving the model with Benders decomposition

Grand infrastructure projects,such as dam,power plant,petroleum,and gas industry projects,have several contractors working on them in several independent sub-projects.The concern of reducing the duration of these projects is one of the important issues among various aspects;thus,our aim is to fulfill the requirements by using the game theory approach.In this study,a mixed-integer programming model consisting of game theory and project scheduling is developed to reduce the duration of projects with a minimum increase in costs.In this model,two contractors in successive periods are entered into a step-by-step competition by the employer during dynamic games,considering an exchange in their limited resources.The optimum solution of the game in each stage are selected as the strategy,and the resources during the game are considered to be renewable and limited.The strategy of each contractor can be described as follows:1)share their resources with the other contractor and 2)not share the resources with the other contractor.This model can act dynamically in all circumstances during project implementation.If a player chooses a non-optimum strategy,then this strategy can immediately update itself at the succeeding time period.The proposed model is solved using the exact Benders decomposition method,which is coded in GAMS software.The results suggest the implementation of four step-by-step games between the contractors.Then,the results of our model are compared with those of the conventional models.The projects’duration in our model is reduced by 22.2%.The nominal revenue of both contractors has also reached a significant value of 46078 units compared with the relative value of zero units in the original model.Moreover,we observed in both projects the decreases of 19.5%,20.9%,and 19.7%in the total stagnation of resources of types 1,2,and 3,respectively.