Simulation Based Multi-objective Extremal Optimization Algorithm for Electronic Reconnaissance Satellites Scheduling Problem

2009 ◽  
Author(s):  
Xiaojun Huang ◽  
Huilin Wang ◽  
Manhao Ma ◽  
Jianjun Li
Keyword(s):  
Optimization Algorithm ◽  
Scheduling Problem ◽  
Extremal Optimization ◽  
Multi Objective ◽  
Simulation Based

scholarly journals An Application of the Harmony-Search Multi-Objective (HSMO) Optimization Algorithm for the Solution of Pump Scheduling Problem

2016 ◽  
Vol 162 ◽  
pp. 494-502 ◽  
Author(s):  
Francesco De Paola ◽  
Nicola Fontana ◽  
Maurizio Giugni ◽  
Gustavo Marini ◽  
Francesco Pugliese
Keyword(s):  
Optimization Algorithm ◽  
Harmony Search ◽  
Scheduling Problem ◽  
Multi Objective ◽  
Pump Scheduling

scholarly journals Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092523 ◽  
Author(s):  
Xiaoyu Wen ◽  
Xinyu Li ◽  
Liang Gao ◽  
Kanghong Wang ◽  
Hao Li
Keyword(s):  
Process Planning ◽  
Optimization Algorithm ◽  
Honey Bees ◽  
Processing Time ◽  
Intelligent Manufacturing ◽  
Scheduling Problem ◽  
Integrated Process ◽  
Due Date ◽  
Planning And Scheduling ◽  
Multi Objective

The new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing. Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. At present, the research on integrated process planning and scheduling is insufficient to consider the multi-objective and uncertain characteristics widely existing in real manufacturing environment. Therefore, multi-objective integrated process planning and scheduling problem with uncertain processing time and due date is addressed in this article. The mathematical model of multi-objective uncertain integrated process planning and scheduling problem with uncertain processing time and fuzzy due date is established based on fuzzy set, in which the calculation method of uncertainty measurement objective is designed. An effective modified honey bees mating optimization algorithm has been designed to solve the proposed model. Queens set is constructed to maintain the non-dominated solutions found in the optimization process. The calculation method of mating probability between drone and queen bee based on Euclidean distance is designed. Fuzzy operators were utilized to evaluate fitness, judge the non-dominated relationship, and decode the scheduling solution. Different instances were designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain integrated process planning and scheduling.

scholarly journals A multi-objective multi-agent optimization algorithm for the multi-skill resource-constrained project scheduling problem with transfer times

2021 ◽  
Author(s):  
Amirhossein Hosseinian ◽  
Vahid Baradaran
Keyword(s):  
Optimization Algorithm ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Multi Objective ◽  
Proposed Model ◽  
Multi Agent ◽  
Project Scheduling Problem ◽  
Transfer Times

This paper addresses the Multi-Skill Resource-Constrained Project Scheduling Problem with Transfer Times (MSRCPSP-TT). A new model has been developed that incorporates the presence of transfer times within the multi-skill RCPSP. The proposed model aims to minimize project’s duration and cost, concurrently. The MSRCPSP-TT is an NP-hard problem; therefore, a Multi-Objective Multi-Agent Optimization Algorithm (MOMAOA) is proposed to acquire feasible schedules. In the proposed algorithm, each agent represents a feasible solution that works with other agents in a grouped environment. The agents evolve due to their social, autonomous, and self-learning behaviors. Moreover, the adjustment of environment helps the evolution of agents as well. Since the MSRCPSP-TT is a multi-objective optimization problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used in different procedures of the MOMAOA. Another novelty of this paper is the application of TOPSIS in different procedures of the MOMAOA. These procedures are utilized for: (1) detecting the leader agent in each group, (2) detecting the global best leader agent, and (3) the global social behavior of the MOMAOA. The performance of the MOMAOA has been analyzed by solving several benchmark problems. The results of the MOMAOA have been validated through comparisons with three other meta-heuristics. The parameters of algorithms are determined by the Response Surface Methodology (RSM). The Kruskal-Wallis test is implemented to statistically analyze the efficiency of methods. Computational results reveal that the MOMAOA can beat the other three methods according to several testing metrics. Furthermore, the impact of transfer times on project’s duration and cost has been assessed. The investigations indicate that resource transfer times have significant impact on both objectives of the proposed model

An improved multi-objective population-based extremal optimization algorithm with polynomial mutation

2016 ◽  
Vol 330 ◽  
pp. 49-73 ◽  
Author(s):  
Guo-Qiang Zeng ◽  
Jie Chen ◽  
Li-Min Li ◽  
Min-Rong Chen ◽  
Lie Wu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Population Based ◽  
Extremal Optimization ◽  
Multi Objective ◽  
Polynomial Mutation
Sign in / Sign up

Export Citation Format

Share Document