scholarly journals Extremal-Micro Genetic Algorithm Model for Time-Cost Optimization with Optimal Labour Productivity

2021 ◽  
Vol 67 (12) ◽  
pp. 682-691
Author(s):  
Sivakumar A ◽  
Bagath Singh N ◽  
Sathiamurthi P ◽  
Karthi Vinith K.S.
Keyword(s):  
Genetic Algorithm ◽  
Labour Productivity ◽  
Cost Optimization ◽  
Model Performance ◽  
Time Cost ◽  
Production Time ◽  
Proposed Model ◽  
Bus Body ◽  
Micro Genetic Algorithm ◽  
Genetic Algorithm Model

In a highly competitive manufacturing environment, it is critical to balance production time and cost simultaneously. Numerous attempts have been made to provide various solutions to strike a balance between these factors. However, more effort is still required to address these challenges in terms of labour productivity. This study proposes an integrated substitution and management improvement technique for enhancing the effectiveness of labour resources and equipment. Furthermore, in the context of time-cost optimization with optimal labour productivity, an extremal-micro genetic algorithm (Ex-μGA) model has been proposed. A real-world case from the labour-intensive medium-scale bus body fabricating industry is used to validate the proposed model performance. According to the results, the proposed model can optimize production time and cost by 34 % and 19 %, respectively, while maintaining optimal labour productivity. In addition, this study provides an alternative method for dealing with production parameter imbalances and assisting production managers in developing labour schedules more effectively.

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 Fuzzy Based Project Time-Cost Optimization Using Simulated Annealing Search Technique

2016 ◽  
pp. 1473-1486
Author(s):  
Khan Md. Ariful Haque ◽  
M. Ahsan Akhtar Hasin
Keyword(s):  
Simulated Annealing ◽  
Cost Optimization ◽  
Process Time ◽  
Time Cost ◽  
Deterministic Models ◽  
Productivity Level ◽  
Proposed Model ◽  
Cut Method ◽  
Project Scheduling Problem ◽  
Fuzzy Logic Theory

The Project time-cost optimization is inherently a complex task. Because of various kinds of uncertainties, such as weather, productivity level, inflation, human factors etc. during project execution process, time and cost of each activity may vary significantly. The complexity multiplies several folds when the operational times are not deterministic, rather fuzzy in nature. Therefore, deterministic models for time-cost optimization are not yet efficient. It is very difficult to find the exact solution of savings in both time and cost. To make such problems realistic, triangular fuzzy numbers and the concept of a-cut method in fuzzy logic theory are employed to model the problem. Because of NP-hard nature of the project scheduling problem, this paper develops a simple approach with Simulated Annealing (SA) based searching technique. The proposed model leads the decision makers to choose the desired solution under different values of a-cut. Finally, taking a real project, the performance of SA has been tested.

Fuzzy Based Project Time-Cost Optimization Using Simulated Annealing Search Technique

2014 ◽  
Vol 5 (1) ◽  
pp. 90-103 ◽  
Author(s):  
Khan Md. Ariful Haque ◽  
M. Ahsan Akhtar Hasin
Keyword(s):  
Simulated Annealing ◽  
Cost Optimization ◽  
Process Time ◽  
Time Cost ◽  
Deterministic Models ◽  
Productivity Level ◽  
Proposed Model ◽  
Cut Method ◽  
Project Scheduling Problem ◽  
Fuzzy Logic Theory

The Project time-cost optimization is inherently a complex task. Because of various kinds of uncertainties, such as weather, productivity level, inflation, human factors etc. during project execution process, time and cost of each activity may vary significantly. The complexity multiplies several folds when the operational times are not deterministic, rather fuzzy in nature. Therefore, deterministic models for time-cost optimization are not yet efficient. It is very difficult to find the exact solution of savings in both time and cost. To make such problems realistic, triangular fuzzy numbers and the concept of a-cut method in fuzzy logic theory are employed to model the problem. Because of NP-hard nature of the project scheduling problem, this paper develops a simple approach with Simulated Annealing (SA) based searching technique. The proposed model leads the decision makers to choose the desired solution under different values of a-cut. Finally, taking a real project, the performance of SA has been tested.

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