Tool requirement and pre-scheduling optimization model of the tool flow system of a digital workshop

2017 ◽  
Vol 231 (5) ◽  
pp. 838-849
Author(s):  
Qi Lei ◽  
Li Zeng ◽  
Yuchuan Song
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Method ◽  
Double Layer ◽  
Heuristic Algorithm ◽  
Optimization Model ◽  
Optimization Problems ◽  
Flow System ◽  
Scheduling Optimization ◽  
Purchase Cost

A new mathematical method and an optimization model are proposed in this study to solve the tool requirement and pre-scheduling optimization problems involved in the tool flow system of digital workshops. This model aims to minimize the system makespan under the constraint of the tool purchase cost. A double-layer genetic algorithm based on the heuristic algorithm is then developed. This algorithm not only combines the advantages but also avoids the weaknesses of the two algorithms. Finally, a case study is conducted to validate the effectiveness and superiority of the proposed algorithm and the tool-machine dual-resource pre-scheduling optimization model.

scholarly journals Modified Grasshopper Optimization Algorithm Based Genetic Algorithm for Global Optimization Problems: The System of Nonlinear Equations Case Study

2021 ◽  
Author(s):  
Hala A. Omar ◽  
Mohammed El-Shorbagy
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Local Minimum ◽  
Optimization Problems ◽  
Search Space ◽  
System Of Nonlinear Equations ◽  
Grasshopper Optimization Algorithm ◽  
Speed Up ◽  
Grasshopper Optimization

Abstract Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. But, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations have been proposed to overcome this problem. In this paper, a modified grasshopper optimization algorithm (MGOA) based genetic algorithm (GA) is proposed to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the Cmax control parameter to escape from the local minimum and move the search process to a new improved point. Parameter C is one of the most important parameters in GOA where it balances the exploration and exploitation of the search space. These modifications aim to lead to speed up the convergence rate by reducing the repeated solutions and the number of iterations. The proposed algorithm will be tested on the 19 main test functions to verify and investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve 5 different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Good results were achieved compared to the original GOA.

Research on the Equipment Maintenance Tasks Scheduling Optimization Problem Based on GA

2012 ◽  
Vol 452-453 ◽  
pp. 750-754
Author(s):  
Yi Ma ◽  
Yu Lu ◽  
Li Yun Chen ◽  
Ping Gu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Optimization Problem ◽  
New Method ◽  
Equipment Maintenance ◽  
Np Hard ◽  
Scheduling Optimization ◽  
Tasks Scheduling ◽  
Problem Design ◽  
The Military

Scientific maintenance tasks scheduling can improve maintenance effectiveness greatly. Aiming for the shortage of the research on the equipment maintenance tasks scheduling optimization (EMTSO) problem, this paper proposes a new method based on GA. The detail is as follows: Make the optimization model of the EMTSO problem by analyzing the characteristics of maintenance tasks scheduling systemically. Aiming for the NP hard characteristic of the problem, design the genetic algorithm to solve it. Finally, use the instance to validate the method. The result reflects that the method proposed by this paper can solve the equipment maintenance tasks scheduling optimization problem, and it has good applicable value in the military domain.

Optimization Method for Robust Continuous Parameter Design in the Target-being-Best Based on Genetic Evolution

2014 ◽  
Vol 1049-1050 ◽  
pp. 1272-1280
Author(s):  
Qiang Zeng ◽  
Ling Shen ◽  
Ze Bin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Theoretical Calculation ◽  
Optimization Model ◽  
Optimization Method ◽  
Parameter Design ◽  
Genetic Evolution ◽  
Quality Loss ◽  
Average Quality ◽  
Continuous Parameter

Aiming at the problem of robust continuous parameter design in the Target-being-best, in which the output value can be obtained by theoretical calculation, an optimization method based on genetic evolution is proposed. Firstly, the researched problem is described mathematically and an optimization model is established with the objective to minimize the average quality loss of a sample. Secondly, the optimization method based on genetic evolution for the researched problem is proposed. Thirdly, the genetic algorithm for robust continuous parameter design in the Target-being-best is presented and designed. Finally, the effectiveness of the proposed method is validated by case study.

scholarly journals A Multi Logistics Network Optimization Model of Power System based on Stochastic Demand and Inventory Decision

2021 ◽  
Vol 15 ◽  
pp. 1057-1061
Author(s):  
Youpeng Zhang ◽  
Jibin Zhu ◽  
Yunxin Chang ◽  
Yonghao Guo ◽  
Chaofeng Zhu
Keyword(s):  
Double Layer ◽  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Transportation Cost ◽  
Arc Routing ◽  
Routing Problem ◽  
Logistics Network ◽  
Purchase Cost ◽  
Inventory Decision ◽  
Capacitated Arc Routing

According to the disposal demand for packets and all kinds of constraints in the current condition, this paper discusses a modeling method of extended double-layer capacitated arc routing problems with Capacitated Arc Routing Problem (CARP) optimization model based on express logistics. The model is described in detail, and the solution of the model is discussed. The paper analyses and discusses the solution to complexity of the model, and proposes a better solution to the double-layer CARP optimization model. According to the scheme, the paper chooses a sort of improved ant colony algorithm to solve the model. And the results show that the scheme is beneficial to controlling the cost of logistics links, to minimizing purchase cost, transportation cost and delivery time. The scheme plays a very imperative role in enhancing the competitiveness of enterprises

scholarly journals Anthropic Principle Algorithm:A new Heuristic Optimization Meth

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Elder Oroski ◽  
Pês S. Beatriz ◽  
Lopez H. Rafael ◽  
Bauchspiess Adolfo
Keyword(s):  
Genetic Algorithm ◽  
System Identification ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Anthropic Principle ◽  
Optimization Process ◽  
Heuristic Optimization ◽  
The Universe ◽  
Made In

Heuristic optimization is an appealing method for solving some en- gineering problems, in which gradient information may not be available, or yet, when the problem presents many minima points. Thus, the goal of this paper is to present a new heuristic algorithm based on the Anthropic Prin- ciple, the Anthropic Principle Algorithm (APA). This algorithm is based on the following idea: the universe developed itself in the exact way to allow the existence of all current things, including life. This idea is very similar to the convergence in an optimization process. Arguing about the merit of the An- thropic Principle is not among the goals of this paper. This principle is treated only as an inspiration for heuristic optimization algorithms. In the final of the paper, some applications of the APA are presented. Classical problems such as Rosenbrock function minimization, system identification examples and min- imization of some benchmark functions are also presented. In order to vali- date the APA’s functionality, a comparison between the APA and the classic heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimiza- tion (PSO) is made. In this comparison, the APA presented better results in majority of tested cases, proving that it has a great potential for application in optimization problems.

scholarly journals An Optimal Allocation Strategy for Multienergy Networks Based on Double-Layer Nondominated Sorting Genetic Algorithms

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Min Mou ◽  
Da Lin ◽  
Yuhao Zhou ◽  
Wenguang Zheng ◽  
Jiongming Ruan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Double Layer ◽  
Optimization Model ◽  
Optimal Allocation ◽  
Environmental Benefits ◽  
Optimization Strategy ◽  
Allocation Strategy ◽  
Photovoltaic Panel ◽  
Wind Power Generator ◽  
Nondominated Sorting

Aiming at the problems of complex structures, variable loads, and fluctuation of power outputs of multienergy networks, this paper proposes an optimal allocation strategy of multienergy networks based on the double-layer nondominated sorting genetic algorithm, which can optimize the allocation of distributed generation (DG) and then improve the system economy. In this strategy, the multiobjective nondominated sorting genetic algorithm is adopted in both layers, and the second-layer optimization is based on the optimization result of the first layer. The first layer is based on the structure and load of the multienergy network. With the purpose of minimizing the active power loss and the node voltage offset, an optimization model of the multienergy network is established, which uses the multiobjective nondominated sorting genetic algorithm to solve the installation location and the capacity of DGs in multienergy networks. In the second layer, according to the optimization results of the first layer and the characteristics of different DGs (wind power generator, photovoltaic panel, microturbine, and storage battery), the optimization model of the multienergy network is established to improve the economy, reliability, and environmental benefits of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of different DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. The double-layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides effective guidance for the optimal allocation and reliable operation of multienergy networks.

scholarly journals Spatial Multi-Objective Land Use Optimization toward Livability Based on Boundary-Based Genetic Algorithm: A Case Study in Singapore

2020 ◽  
Vol 9 (1) ◽  
pp. 40 ◽  
Author(s):  
Kai Cao ◽  
Muyang Liu ◽  
Shu Wang ◽  
Mengqi Liu ◽  
Wenting Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Central Region ◽  
Optimization Model ◽  
Optimization Problem ◽  
Weighted Sum ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Future Direction

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have been successfully conducted, which can demonstrate the effectiveness of the spatial multi-objective land use optimization model developed in this research as well as the robustness and reliability of computer-generated solutions. In addition, the comparison between the computer-generated solutions and the two real planned scenarios has also clearly demonstrated that our generated solutions are much better in terms of fitness values. Lastly, the limitation and future direction of this research have been discussed.

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