A Discrete Data Base Multiple Objective Optimization of Milling Operation Through Geometric Programming

1990 ◽  
Vol 112 (4) ◽  
pp. 368-374 ◽  
Author(s):  
N. K. Jha
Keyword(s):  
Objective Function ◽  
Production Planning ◽  
Process Planning ◽  
Geometric Programming ◽  
Multiple Objective ◽  
Optimum Process ◽  
Multiple Objective Optimization ◽  
Objective Functions ◽  
Milling Operations ◽  
Milling Operation

The optimum process planning of the milling operation has been attempted through multiple objectives. A multiple objective function based on cost of production and rate of production in milling operations has been developed. The unified objective function thus developed serves as true arbiter balancing the values and objectives of local or individual objective functions. Most of the machine settings are discrete in nature and this has been considered in computerized production planning of milling. The complete approach has been demonstrated through an example.

scholarly journals Fresa: A Plant Propagation Algorithm for Black-Box Single and Multiple Objective Optimization

2021 ◽  
Vol 2 (4) ◽  
Keyword(s):  
Mathematical Programming ◽  
Objective Function ◽  
Optimization Problems ◽  
Population Based ◽  
Black Box ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Plant Propagation ◽  
Propagation Algorithm ◽  
Multiple Dispatch

Fresa implements a nature inspired plant propagation algorithm for the solution of single and multiple objective optimization problems. The method is population based and evolutionary. Treating the objective function as a black box, the implementation is able to solve problems exhibiting behaviour that is challenging for mathematical programming methods. Fresa is easily adapted to new problems which may benefit from bespoke representations of solutions by taking advantage of the dynamic typing and multiple dispatch capabilities of the Julia language. Further, the support for threads in Julia enables an efficient implementation on multi-core computers.

ENHANCED INTERACTIVE SATISFYING OPTIMIZATION APPROACH TO MULTIPLE OBJECTIVE OPTIMIZATION WITH PREEMPTIVE PRIORITIES

2006 ◽  
Vol 05 (01) ◽  
pp. 47-63 ◽  
Author(s):  
CHAOFANG HU ◽  
SHAOYUAN LI
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Previous Method ◽  
Optimization Models ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Optimization Approach ◽  
Fuzzy Relations ◽  
Objective Functions ◽  
Preemptive Priority

This paper proposes an enhanced interactive satisfying optimization method based on goal programming for the multiple objective optimization problem with preemptive priorities. Based on the previous method, the approach presented makes the higher priority achieve the higher satisfying degree. For three fuzzy relations of the objective functions, the corresponding optimization models are proposed. Not only can satisfying results for all the objectives be acquired, but the preemptive priority requirement can also be simultaneously actualized. The balance between optimization and priorities is realized. We demonstrate the power of this proposed method by illustrative examples.

scholarly journals Multi-Objective Function for SWIPT System by SADDE

2020 ◽  
Vol 10 (9) ◽  
pp. 3124
Author(s):  
Wei Chien ◽  
Chien-Ching Chiu ◽  
Yu-Ting Cheng ◽  
Wei-Lin Fang ◽  
Eng Hock Lim
Keyword(s):  
Objective Function ◽  
Path Loss ◽  
Wireless Power Transfer ◽  
Function Optimization ◽  
Multiple Objective ◽  
Power Transfer ◽  
Objective Functions ◽  
Wireless Power ◽  
Real Environment ◽  
Single Objective

Simultaneous wireless information and power transfer (SWIPT) optimization with multiple objective function optimization is presented in the millimeter band in this paper. Three different objective functions that are used for harvest power (HP), capacity, and bit error rate (BER) were studied. There are three different nodes in real environment for wireless power transfer (WPT) and SWIPT. The channel estimation calculated by shooting and bouncing ray/image techniques includes multi-path, fading effect, and path-loss in the real environment. We applied beamforming techniques at the transmitter to focus the transmitter energy in order to reduce the multi-path effect and adjust the length of the feed line on each array element in order to find the extremum of the objective functions by the self-adaptive dynamic differential evolution (SADDE) method. Numerical results showed that SWIPT node cannot achieve good performance by single objective function, but wireless power transfer (WPT) can. Nevertheless, both WPT and SWIPT nodes can meet the criteria by the multiple objective function. The harvesting power ratio as well as the BER and capacity can be improved by the multiple objective function to an acceptable level by only reducing a little harvesting energy compared to the best harvesting energy for the single objective function. Finally, the multiple optimization function cannot merely provide good information quality for SWIPT node but achieve good total harvesting power for WPT and SWIPT node as well.

scholarly journals Optimization of resource leveling problem under multiple objective criteria using a symbiotic organisms search

2019 ◽  
Vol 21 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Doddy Prayogo ◽  
Christianto Tirta Kusuma
Keyword(s):  
Particle Swarm Optimization ◽  
Resource Management ◽  
Objective Function ◽  
Multiple Objective ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Cost Overruns ◽  
Symbiotic Organisms Search ◽  
Resource Leveling ◽  
Symbiotic Organisms

Bad scheduling and resource management can cause delays or cost overruns. Optimization in solving resource leveling is necessary to avoid those problems. Several objective criteria are used to solve resource leveling. Each of them has the same objective, which is to reduce the fluctuation of resource demand of the project. This study compares the performance of particle swarm optimization (PSO) and symbiotic organisms search (SOS) in solving resource leveling problems using separate objective functions in order to find which one produces a better solution. The results show that SOS produced a better solution than PSO, and one objective function is better in solving resource leveling than the others.

Scalable Computing Architecture for Time-Dependent Transportation Optimization Problems Based on High-Performance Computing Techniques

2019 ◽  
Vol 2673 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang
Keyword(s):  
Objective Function ◽  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Dynamic Network ◽  
Alternative Formulation ◽  
High Fidelity ◽  
Objective Functions ◽  
Performance Computing ◽  
Transportation Optimization

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.

scholarly journals System Dynamics-Multiple Objective Optimization Model for Water Resource Management: A Case Study in Jiaxing City, China

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 671
Author(s):  
Xiaoying Zhou ◽  
Feier Wang ◽  
Kuan Huang ◽  
Huichun Zhang ◽  
Jie Yu ◽  
...  
Keyword(s):  
Resource Management ◽  
System Dynamics ◽  
Water Resource ◽  
Water Allocation ◽  
Water Resource Management ◽  
Water Shortage ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Model Framework

Predicting and allocating water resources have become important tasks in water resource management. System dynamics and optimal planning models are widely applied to solve individual problems, but are seldom combined in studies. In this work, we developed a framework involving a system dynamics-multiple objective optimization (SD-MOO) model, which integrated the functions of simulation, policy control, and water allocation, and applied it to a case study of water management in Jiaxing, China to demonstrate the modeling. The predicted results of the case study showed that water shortage would not occur at a high-inflow level during 2018–2035 but would appear at mid- and low-inflow levels in 2025 and 2022, respectively. After we made dynamic adjustments to water use efficiency, economic growth, population growth, and water resource utilization, the predicted water shortage rates decreased by approximately 69–70% at the mid- and low-inflow levels in 2025 and 2035 compared to the scenarios without any adjustment strategies. Water allocation schemes obtained from the “prediction + dynamic regulation + optimization” framework were competitive in terms of social, economic and environmental benefits and flexibly satisfied the water demands. The case study demonstrated that the SD-MOO model framework could be an effective tool in achieving sustainable water resource management.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

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