Time and Cost Optimization Using Dynamic Programming and FMS Scheduling

Recently, energy storage systems (ESSs) are becoming more important as renewable and microgrid technologies advance. ESSs can act as a buffer between generation and load and enable commercial and industrial end users to reduce their electricity expenses by controlling the charge/discharge amount. In this paper, to derive efficient charge/discharge schedules of ESSs based on time-of-use pricing with renewable energy, a combination of genetic algorithm and dynamic programming is proposed. The performance of the combined method is improved by adjusting the size of the base units of dynamic programming. We show the effectiveness of the proposed method by simulating experiments with load and generation profiles of various commercial electricity consumers.

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Scheduling and Cost Optimization of Multi-Storey Building

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g4890.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 75-80

Keyword(s):

Dynamic Programming ◽

Time Management ◽

Cost Optimization ◽

Optimization Techniques ◽

Programming Method ◽

Dynamic Programming Method ◽

Coordination Failure ◽

Project Cost ◽

Software Cost ◽

The Cost

Now a days construction gets delayed in the site due to the factors such as inefficient management, design issues, economic variation, coordination failure between time schedule and execution etc. The major problem faced by the construction sector is not able to manage the fund allocated for the work and not able to complete the work in targeted days. This can be overcome by the implementation of scheduling and cost optimization process in the construction field. Scheduling is a critical component of successful time management. Optimizing cost should be carried out to aware the building cost is kept within the estimated cost limits .This paper deals with scheduling of multi-story building and cost effective method to find required cost for completion of the project. On going project is located at Nagar Kovil and Manual scheduling data collected from the site. Scheduling of the project is done with primavera p6 software. Cost required for the project is also computed with primaveraP6 software. For reducing the cost of the project, required actions are taken. The cost is reduced by proper using of resources, such as manpower, materials and machines. Computerized scheduling provides clear scheduling data for execution of the project. Cost required for the project execution is also reduced with proper scheduling process. Cost is optimized by using a dynamic programming method. Microsoft excel is used for dynamic method of cost optimization. In this project cost is considerably reduced by using the dynamic programming method of cost optimization techniques.

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Least cost scheduling for repetitive projects

Canadian Journal of Civil Engineering ◽

10.1139/l93-109 ◽

1993 ◽

Vol 20 (5) ◽

pp. 834-843 ◽

Cited By ~ 3

Author(s):

Osama Moselhi ◽

Khaled El-Rayes

Keyword(s):

Dynamic Programming ◽

Learning Curve ◽

Cost Optimization ◽

Indirect Costs ◽

Optimization Process ◽

Planning And Scheduling ◽

Weather Impact ◽

Project Duration ◽

Curve Effect ◽

The Impact

Dynamic programming has been utilized to optimize scheduling of repetitive projects. The optimization criterion of existing techniques, however, is limited to minimizing the project duration and does not account for the impact of such optimization on the project cost. While the minimization of the project duration may reduce the project indirect costs, it does not guarantee a minimum total cost for the project. In practice, minimizing the overall cost of a project is frequently regarded to be more important than minimizing its duration. The objective of this paper is to present a flexible model that incorporates cost in the optimization process. In addition, the model is capable of considering the weather impact and the learning curve effect in the optimization process, simulating two important factors affecting productivity on this class of projects. The model utilizes dynamic programming and performs the solution in two stages: first, a forward path to identify local minimum conditions; and then a backward path to ensure a global minimum state. A numerical example from the literature is analyzed in order to demonstrate the use of the model, test its validity, and illustrate the significance of incorporating cost, weather impact, and the learning curve effect in the optimization process. Key words: planning and scheduling, repetitive projects, linear scheduling, cost optimization, dynamic programming, learning curve effect, weather impact.

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