A new multi-objective optimization formulation for rail-car fleet sizing problems

2009 ◽  
Vol 10 (2) ◽  
pp. 175-198 ◽  
Author(s):  
Hamid Reza Sayarshad ◽  
Timothy Marler
Keyword(s):  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fleet Sizing ◽  
Optimization Formulation

Multi-Objective Optimization Formulation to Make Creative Teams in Teamology

2010 ◽  
Author(s):  
Masao Arakawa
Keyword(s):  
Optimization Algorithms ◽  
Project Based Learning ◽  
Project Teams ◽  
Genetic Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Learning Teams ◽  
Optimization Formulation

Teamology is established by Prof. Wilde to make creative teams in project teams. As a first step, it needs questionnaires to characterize personality for each member who joins the projects. Assume in academic project based learning teams, a number of students join and we are going to make several teams. Each team should have the same potential if and only if we can make every team as that. In order to create these teams, we need to quantify students’ characters, and we need to formalize them to meet the guideline of Teamology. In this study, we are going to make multi-objective optimization formulation of Teamology, and show an example of team making by using a genetic optimization algorithms with data that was taken PBL course in Kagawa University.

The Product-Mix Optimization with outside Processing Based on Theory of Constraints Oriented Cloud Manufacturing

2011 ◽  
Vol 121-126 ◽  
pp. 1306-1310 ◽  
Author(s):  
Bing Zheng ◽  
Yi Cong Gao ◽  
Yun Wang
Keyword(s):  
Analytical Solution ◽  
Optimization Problem ◽  
Cloud Manufacturing ◽  
Theory Of Constraints ◽  
Multi Objective Optimization ◽  
Product Mix ◽  
Final Model ◽  
Processing Problem ◽  
Multi Objective ◽  
Optimization Formulation

In order to solve the multi-objective product-mix optimal problems oriented cloud manufacturing, SPEA2+ is used to realize the outside processing problem of lifting cross sliding trimensional parking equipment (multi-section trimensional parking equipment), which oriented cloud manufacturing. The outside processing problem is formulated as a multi-objective optimization problem and identified an analytical solution. The case illustration of parts of PSH2 lifting cross sliding trimensional parking equipment is taken as an example, examining the multi-objective optimization model. The model enables managers to determine which products to manufacture and which to outside processing. The solution of the multi-objective optimization formulation enables managers to apply the model by computing an operational ratio. The final model is simpler to apply and requires the computation of fewer variables than other prevalent models.

Solving multi-objective optimization formulation for fleet planning in a railway industry

2010 ◽  
Vol 181 (1) ◽  
pp. 185-197 ◽  
Author(s):  
Hamid Reza Sayarshad ◽  
Nikbakhsh Javadian ◽  
Reza Tavakkoli-Moghaddam ◽  
Nastaran Forghani
Keyword(s):  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fleet Planning ◽  
Railway Industry ◽  
Optimization Formulation
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