Multi-Objective Optimization of Human Resource Allocation in Production Cells with Different Training Modes

2012 ◽  
Vol 601 ◽  
pp. 521-525
Author(s):  
Cai Juan Li ◽  
Xiao Yun Wu ◽  
Xiao Dong Zhang
Keyword(s):  
Resource Allocation ◽  
Staff Training ◽  
Programming Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The People ◽  
Training Mode ◽  
The Difference ◽  
Production Cell

Aiming at the difference of the people as a particularity resource。In this paper ,the personnel training mode is divided into junior and senior, and a multi-objective integer programming model is established at the lowest cost of staff training, the highest man-machine adaptability degree and minimum personnel workload. Calculating example of a real production cell is presented. The results show that the model is correct and the necessity for classification of training modes.The model can help the management to adopt reasonable training mode and achieve desirable objectives.

Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

2019 ◽  
Vol 2673 (7) ◽  
pp. 215-226 ◽  
Author(s):  
Huizhuo Cao ◽  
Xuemei Li ◽  
Vikrant Vaze ◽  
Xueyan Li
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Pareto Frontier ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
High Speed Rail ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Single Objective

Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing–Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.

scholarly journals Impact of Social Welfare Metrics on Energy Allocation in Multi-Objective Optimization

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2961
Author(s):  
Anders Clausen ◽  
Aisha Umair ◽  
Yves Demazeau ◽  
Bo Nørregaard Jørgensen
Keyword(s):  
Resource Allocation ◽  
Social Welfare ◽  
Energy Demand ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Multi Objective Optimization ◽  
Multi Agent Systems ◽  
Multi Objective ◽  
Pareto Sets ◽  
The Impact

Resource allocation problems are at the core of the smart grid where energy supply and demand must match. Multi-objective optimization can be applied in such cases to find the optimal allocation of energy resources among consumers considering energy domain factors such as variable and intermittent production, market prices, or demand response events. In this regard, this paper considers consumer energy demand and system-wide energy constraints to be individual objectives and optimization variables to be the allocation of energy over time to each of the consumers. This paper considers a case in which multi-objective optimization is used to generate Pareto sets of solutions containing possible allocations for multiple energy intensive consumers constituted by commercial greenhouse growers. We consider the problem of selecting a final solution from these Pareto sets, one of maximizing the social welfare between objectives. Social welfare is a set of metrics often applied to multi-agent systems to evaluate the overall system performance. We introduce and apply social welfare ordering using different social welfare metrics to select solutions from these sets to investigate the impact of the type of social welfare metric on the optimization outcome. The results of our experiments indicate how different social welfare metrics affect the optimization outcome and how that translates to general resource allocation strategies.

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