An Effective Memetic Algorithm for the Distributed Integrated Scheduling of Tree-Structured Products

2021 ◽  
pp. 2150001
Author(s):  
Yilong Gao ◽  
Zhiqiang Xie ◽  
Qing Jia ◽  
Xu Yu
Keyword(s):  
Sampling Method ◽  
Memetic Algorithm ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Estimation Algorithm ◽  
Probability Models ◽  
Complex Products ◽  
Integrated Scheduling ◽  
Local Search Strategy ◽  
Representation Method

Aiming at the distributed integrated scheduling of complex products with tree structure, a memetic algorithm-based distributed integrated scheduling algorithm is proposed. Based on the framework of the memetic algorithm, the algorithm uses a distributed estimation algorithm for global search and performs a local search strategy based on the critical operation set for the current optimal solution obtained in each evolutionary generation. A bi-chain-based individual representation method is presented and a simple greedy insertion-based decoding method is given; two position-based probability models are built, which are used to describe the distribution of the operation priority and factory assignment, respectively. Based on the designed probability models, two learning-based updating mechanisms and an improved sampling method are given, which ensures that the population evolves towards a promising region. In order to enhance the searchability for the superior solutions, nine disturbance operators based on the critical operation set are presented. The parameters are determined by the design-of-experiment (DOE) test, and the effectiveness of the proposed algorithm is verified by comparative experiments.

scholarly journals Integrated scheduling algorithm based on the priority constraint table for complex products with tree structure

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098520
Author(s):  
Yilong Gao ◽  
Zhiqiang Xie ◽  
Xinyang Liu ◽  
Wei Zhou ◽  
Xu Yu
Keyword(s):  
Scheduling Algorithm ◽  
Tree Structure ◽  
Initial Population ◽  
Complex Products ◽  
Dynamic Priority ◽  
Integrated Scheduling ◽  
Intelligent Optimization Algorithms ◽  
Local Search Strategy ◽  
Encoding Method ◽  
Evolutionary Operators

Aiming at the existing intelligent optimization algorithms for solving the integrated scheduling problem of complex products with tree structure, there are problems of missing optimal solutions when designing encoding methods or generating infeasible offspring while designing evolutionary operators, an integrated scheduling algorithm based on the priority constraint table is proposed in this paper. A novel encoding method based on the dynamic priority constraint table is developed, which can guarantee the feasibility and completeness of the initial population individuals. For the legitimacy of the generated offspring individuals, two new different crossover and mutation methods are designed separately. The introduced evolutionary operators can avoid the detection and repairment of the infeasible individuals. An insertion-based greedy decoding method is also developed. In addition, based on the critical operations, a local search strategy is presented to enhance the search ability for the superior solutions. The feasibility and superiority of the proposed algorithm is verified by comparative experiments.

scholarly journals Integrated scheduling algorithm for multiple complex products with due date constraints

2021 ◽  
Vol 1748 ◽  
pp. 032030
Author(s):  
Xinkun Wang ◽  
Yuchuan Song ◽  
Yuji Zou ◽  
Weifei Guo ◽  
Yi Wang
Keyword(s):  
Scheduling Algorithm ◽  
Due Date ◽  
Complex Products ◽  
Integrated Scheduling

Integrated Scheduling Algorithm with Setup Time

2011 ◽  
Vol 213 ◽  
pp. 226-230
Author(s):  
Zhi Qiang Xie ◽  
Jing Yang ◽  
Yu Zheng Teng ◽  
Lan Lan
Keyword(s):  
Time Complexity ◽  
Scheduling Algorithm ◽  
Research Result ◽  
Setup Time ◽  
Scheduling Problem ◽  
Start Time ◽  
Complex Products ◽  
Integrated Scheduling

Aiming at the problem that there is no research result in the complex products processing and assemble integrated scheduling problem with setup time, this paper proposes strategy to resolve this problem. That is to determine scheduling sequence of procedures according to layer priority strategy, shorten time strategy and long path strategy. Then adopt algorithm of inserting setup time dynamically to determine the start time of procedures by scheduling sequence. As this algorithm avoids to move scheduled procedures many times after inserting setup time, the time complexity is only secondary. So this algorithm is simple and has high scheduling efficiency.

A multi-shop integrated scheduling algorithm with fixed output constraint

2021 ◽  
pp. 1-9
Author(s):  
Yingchun Xia ◽  
Zhiqiang Xie ◽  
Yu Xin ◽  
Xiaowei Zhang
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
Scheduling Algorithm ◽  
Processing Parameters ◽  
Scheduling Problem ◽  
Integrated Scheduling ◽  
Scheduling Scheme ◽  
Wide Range ◽  
Constraint Programming Model ◽  
Output Constraint

The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible.

Hookes-Jeeves-Based Variant of Memetic Algorithm

2016 ◽  
pp. 450-475
Author(s):  
Dipti Singh ◽  
Kusum Deep
Keyword(s):  
Genetic Algorithms ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Memetic Algorithms ◽  
Global Optimum ◽  
Benchmark Problems ◽  
New Variant ◽  
Optimum Solution ◽  
Global Optimum Solution

Due to their wide applicability and easy implementation, Genetic algorithms (GAs) are preferred to solve many optimization problems over other techniques. When a local search (LS) has been included in Genetic algorithms, it is known as Memetic algorithms. In this chapter, a new variant of single-meme Memetic Algorithm is proposed to improve the efficiency of GA. Though GAs are efficient at finding the global optimum solution of nonlinear optimization problems but usually converge slow and sometimes arrive at premature convergence. On the other hand, LS algorithms are fast but are poor global searchers. To exploit the good qualities of both techniques, they are combined in a way that maximum benefits of both the approaches are reaped. It lets the population of individuals evolve using GA and then applies LS to get the optimal solution. To validate our claims, it is tested on five benchmark problems of dimension 10, 30 and 50 and a comparison between GA and MA has been made.

scholarly journals The optimation of cow-calf beef cattle and paddy farming integration on farmer household

2020 ◽  
Vol 45 (2) ◽  
pp. 143-152
Author(s):  
T. Ekowati ◽  
E. Prasetyo ◽  
M. Handayani
Keyword(s):  
Beef Cattle ◽  
Sampling Method ◽  
Optimal Solution ◽  
Survey Method ◽  
Optimum Conditions ◽  
Sampling Frame ◽  
Total Respondent ◽  
Cow Calf ◽  
Quota Sampling ◽  
Input Prices

Farmer households generally operate food crops and livestock subsectors that have not fully implemented well, so an optimal farming has not been achieved. This study aimed to analyze optimation of cow-calf beef cattle and paddy farming integration and simulation changing in input prices and the usage of resources to the optimal model. Survey method was used in the research in Grobogan Regency by determining Wirosari District and Purwodadi District. Quota sampling method is used to determine the number of respondents without counting the population as a sampling frame. The number of respondents in each district was 40 farmers so the total respondent was 80 farmers. Data were analyzed using linear programing. Results showed that optimum conditions of integration were achieved in 0.45 ha land, 2.75 AU of cow-calf beef cattle with maximum income of IDR 52,112,440/year. The simulation results regarding in changing in input usege indicated that the addition of 0.25% land area gives a change in scale of cow-calf beef cattle by 0.018% and income of 14.78%. In conclusion, integration optimation was achieved on 0.4 5ha land, 2.75 UT cow-calf beef cattle and optimal solution simulations indicated that farmers have the ability to develop their farming.

The Scheduling Algorithm of Grid Task Based on Cloud Model

2010 ◽  
Vol 439-440 ◽  
pp. 1177-1183
Author(s):  
Shu Tao Gao
Keyword(s):  
Task Scheduling ◽  
Cloud Model ◽  
Excess Entropy ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Simulation Experiments ◽  
Cloud Droplets ◽  
Local Optimal Solution ◽  
Characteristic Values ◽  
Scheduling Optimization Algorithm

In this paper, a kind of grid task scheduling optimization algorithm based on cloud model is proposed with the characteristics of cloud model. With the target being the cloud droplets of the cloud model, this algorithm gets three characteristic values of cloud through the reverse cloud: expectations, entropy and excess entropy, and then obtains cloud droplets using the forward cloud algorithm by adjusting the values of entropy and excess entropy. After several iterations, it achieves the optimal solution of task scheduling. Theoretical analysis and results of simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources and avoids such problems as the local optimal solution of genetic algorithms and premature convergence caused by too much selection pressure with higher accuracy and faster convergence.

Sign in / Sign up

Export Citation Format

Share Document