Multiobjective Approach for Time-Cost Optimization Using a Multi-mode Hybrid Genetic Algorithm

2015 ◽  
pp. 33-49
Author(s):  
Jorge Magalhães-Mendes
Keyword(s):  
Genetic Algorithm ◽  
Cost Optimization ◽  
Hybrid Genetic Algorithm ◽  
Time Cost ◽  
Multiobjective Approach ◽  
Multi Mode

scholarly journals MULTI-OBJECTIVE TIME-COST OPTIMIZATION USING COBB-DOUGLAS PRODUCTION FUNCTION AND HYBRID GENETIC ALGORITHM

2015 ◽  
Vol 22 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Zhigang SHEN ◽  
Ashkan HASSANI ◽  
Qian SHI
Keyword(s):  
Production Function ◽  
Cost Optimization ◽  
Hybrid Genetic Algorithm ◽  
Time Cost ◽  
Construction Time ◽  
Optimal Allocations ◽  
Crashing Cost ◽  
New Perspective ◽  
The Cost ◽  
Douglas Production Function

Existing research on construction time-cost tradeoff issues rarely explore the origin of the crashing cost. Crashing cost function was either assumed without much justification, or came from historical data of some real pro­jects. As a result the conclusions of the papers can hardly be used to guide allocations of labor and equipment resources respectively. The authors believe Cobb-Douglas function provides a much-needed piece to modeling the cost functions in the construction time-cost tradeoff problem during the crashing process. We believe this new perspective fills a gap of existing time-cost tradeoff research by considering project duration, labor and equipment cost as parameters of the Cobb- Douglas production function. A case study was presented to show how the proposed framework works. Our conclusion is that introducing Cobb-Douglas function into time-cost tradeoff problem provides us extra capacity to further identify the optimal allocations of labor and equipment resources during crashing.

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 Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249543
Author(s):  
Jianglong Yang ◽  
Li Zhou ◽  
Huwei Liu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hierarchical Clustering ◽  
Hybrid Genetic Algorithm ◽  
Order Picking ◽  
Location Information ◽  
Storage Space ◽  
Time Cost ◽  
Solution Algorithms ◽  
Multiple Orders

The utilization of a storage space can be considerably improved by using dense mobile racks. However, it is necessary to perform an optimisation study on the order picking to reduce the time cost as much as possible. According to the channel location information that needs to be sorted, the multiple orders are divided into different batches by using hierarchical clustering. On this basis, a mathematical model for the virtual order clusters formed in the batches is established to optimize the order cluster picking and rack position movement, with the minimum picking time as the objective. For this model, a hybrid genetic algorithm is designed, and the characteristics of the different examples and solution algorithms are further analysed to provide a reference for the solution of the order picking optimisation problem in a dense mobile rack warehouse.

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