scholarly journals Integer Optimization Techniques

2019 ◽  
pp. 669-680
Keyword(s):  
Optimization Techniques ◽  
Integer Optimization

Mixed Integer Optimization With NURBs HyPerModels

2007 ◽  
Author(s):  
Cameron J. Turner ◽  
Richard H. Crawford ◽  
Matthew I. Campbell
Keyword(s):  
Design Space ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Underlying Structure ◽  
Optimization Strategy ◽  
Integer Optimization ◽  
Design Variables ◽  
Complementary Approach ◽  
Integer Programming Problems ◽  
Optimum Solution

The challenge of determining the best design in a multimodal design space with multiple local optimal solutions often challenges the best available optimization techniques. By casting the objective function of the optimization problem in the form of a Non-Uniform Rational B-spline (NURBs) metamodel, known as a HyPerModel, significant optimization advantages can be achieved, including the ability to efficiently find the global metamodel optimum solution with less computational expense than traditional approaches. This optimization strategy, defined by the HyPerOp algorithm, uses the underlying structure of a HyPerModel to intelligently select starting points for optimization runs and to identify regions of the design space that do not contain locations for the global metamodel optimum location. This paper describes the application of the HyPerOp algorithm to mixed integer programming problems and demonstrates its use with two example applications. The algorithm works with design spaces composed of continuous and integer design variables and provides a complementary approach for improved optimization capabilities.

Time Scheduling of Transit Systems With Transfer Considerations Using Genetic Algorithms

1998 ◽  
Vol 6 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Partha Chakroborty
Keyword(s):  
Genetic Algorithms ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Integer Optimization ◽  
Transit System ◽  
Decision Variables ◽  
Transit Scheduling ◽  
Algorithmic Approaches

Scheduling of a bus transit system must be formulated as an optimization problem, if the level of service to passengers is to be maximized within the available resources. In this paper, we present a formulation of a transit system scheduling problem with the objective of minimizing the overall waiting time of transferring and nontransferring passengers while satisfying a number of resource- and service-related constraints. It is observed that the number of variables and constraints for even a simple transit system (a single bus station with three routes) is too large to tackle using classical mixed-integer optimization techniques. The paper shows that genetic algorithms (GAs) are ideal for these problems, mainly because they (i) naturally handle binary variables, thereby taking care of transfer decision variables, which constitute the majority of the decision variables in the transit scheduling problem; and (ii) allow procedure-based declarations, thereby allowing complex algorithmic approaches (involving if then-else conditions) to be handled easily. The paper also shows how easily the same GA procedure with minimal modifications can handle a number of other more pragmatic extensions to the simple transit scheduling problem: buses with limited capacity, buses that do not arrive exactly as per scheduled times, and a multiple-station transit system having common routes among bus stations. Simulation results show the success of GAs in all these problems and suggest the application of GAs in more complex scheduling problems.

Review on the aerodynamics of intermediate compressor duct

2020 ◽  
Vol 14 (4) ◽  
pp. 7446-7468
Author(s):  
Manish Sharma ◽  
Beena D. Baloni
Keyword(s):  
High Pressure ◽  
Pressure Loss ◽  
Flow Analysis ◽  
Outer Wall ◽  
Optimization Techniques ◽  
Optimum Pressure ◽  
Research Areas ◽  
Static Pressure Distribution ◽  
High Pressure Compressor ◽  
Pressure Compressor

In a turbofan engine, the air is brought from the low to the high-pressure compressor through an intermediate compressor duct. Weight and design space limitations impel to its design as an S-shaped. Despite it, the intermediate duct has to guide the flow carefully to the high-pressure compressor without disturbances and flow separations hence, flow analysis within the duct has been attractive to the researchers ever since its inception. Consequently, a number of researchers and experimentalists from the aerospace industry could not keep themselves away from this research. Further demand for increasing by-pass ratio will change the shape and weight of the duct that uplift encourages them to continue research in this field. Innumerable studies related to S-shaped duct have proven that its performance depends on many factors like curvature, upstream compressor’s vortices, swirl, insertion of struts, geometrical aspects, Mach number and many more. The application of flow control devices, wall shape optimization techniques, and integrated concepts lead a better system performance and shorten the duct length.  This review paper is an endeavor to encapsulate all the above aspects and finally, it can be concluded that the intermediate duct is a key component to keep the overall weight and specific fuel consumption low. The shape and curvature of the duct significantly affect the pressure distortion. The wall static pressure distribution along the inner wall significantly higher than that of the outer wall. Duct pressure loss enhances with the aggressive design of duct, incursion of struts, thick inlet boundary layer and higher swirl at the inlet. Thus, one should focus on research areas for better aerodynamic effects of the above parameters which give duct design with optimum pressure loss and non-uniformity within the duct.

Application of Computational Mechanics to Tire Design—Yesterday, Today, and Tomorrow

2011 ◽  
Vol 39 (4) ◽  
pp. 223-244 ◽  
Author(s):  
Y. Nakajima
Keyword(s):  
Finite Element ◽  
New Technologies ◽  
Computational Mechanics ◽  
Design Procedure ◽  
Side Wall ◽  
Rolling Resistance ◽  
Optimization Techniques ◽  
Stress Strain ◽  
Element Analysis ◽  
Crown Shape

Abstract The tire technology related with the computational mechanics is reviewed from the standpoint of yesterday, today, and tomorrow. Yesterday: A finite element method was developed in the 1950s as a tool of computational mechanics. In the tire manufacturers, finite element analysis (FEA) was started applying to a tire analysis in the beginning of 1970s and this was much earlier than the vehicle industry, electric industry, and others. The main reason was that construction and configurations of a tire were so complicated that analytical approach could not solve many problems related with tire mechanics. Since commercial software was not so popular in 1970s, in-house axisymmetric codes were developed for three kinds of application such as stress/strain, heat conduction, and modal analysis. Since FEA could make the stress/strain visible in a tire, the application area was mainly tire durability. Today: combining FEA with optimization techniques, the tire design procedure is drastically changed in side wall shape, tire crown shape, pitch variation, tire pattern, etc. So the computational mechanics becomes an indispensable tool for tire industry. Furthermore, an insight to improve tire performance is obtained from the optimized solution and the new technologies were created from the insight. Then, FEA is applied to various areas such as hydroplaning and snow traction based on the formulation of fluid–tire interaction. Since the computational mechanics enables us to see what we could not see, new tire patterns were developed by seeing the streamline in tire contact area and shear stress in snow in traction.Tomorrow: The computational mechanics will be applied in multidisciplinary areas and nano-scale areas to create new technologies. The environmental subjects will be more important such as rolling resistance, noise and wear.

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