scholarly journals Research on Multi-operation Joint Movement Neighborhood Structure of Job Shop Scheduling Problem

2020 ◽  
Vol 56 (13) ◽  
pp. 192
Author(s):  
ZHAO Shikui
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Joint Movement ◽  
Job Shop Scheduling Problem ◽  
Neighborhood Structure ◽  
Shop Scheduling

scholarly journals A memetic algorithm for minimizing the makespan in the Job Shop Scheduling problem

2017 ◽  
Vol 26 (44) ◽  
pp. 111 ◽  
Author(s):  
Henry Lamos-Díaz ◽  
Karin Aguilar-Imitola ◽  
Yuleiny Tatiana Pérez-Díaz ◽  
Silvia Galván-Núñez
Keyword(s):  
Optimization Problem ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Search Space ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Neighborhood Structure ◽  
Selection Probability ◽  
Shop Scheduling

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem cataloged as type NP-Hard. To solve this problem, several heuristics and metaheuristics have been used. In order to minimize the makespan, we propose a Memetic Algorithm (MA), which combines the exploration of the search space by a Genetic Algorithm (GA), and the exploitation of the solutions using a local search based on the neighborhood structure of Nowicki and Smutnicki. The genetic strategy uses an operation-based representation that allows generating feasible schedules, and a selection probability of the best individuals that are crossed using the JOX operator. The results of the implementation show that the algorithm is competitive with other approaches proposed in the literature.

scholarly journals A Two-Stage Optimization Algorithm for Multi-objective Job-Shop Scheduling Problem Considering Job Transport

2020 ◽  
Vol 53 (6) ◽  
pp. 915-924
Author(s):  
Jianfeng Ren ◽  
Chunming Ye ◽  
Yan Li
Keyword(s):  
Large Scale ◽  
Ant Colony Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Polar Coordinates ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Neighborhood Structure ◽  
Two Stage ◽  
Shop Scheduling

This paper solves the job-shop scheduling problem (JSP) considering job transport, with the aim to minimize the maximum makespan, tardiness, and energy consumption. In the first stage, the improved fast elitist nondominated sorting genetic algorithm II (INSGA-II) was combined with N5 neighborhood structure and the local search strategy of nondominant relationship to generate new neighborhood solutions by exchanging the operations on the key paths. In the second stage, the ant colony algorithm based on reinforcement learning (RL-ACA) was designed to optimize the job transport task, abstract the task into polar coordinates, and further optimizes the task. The proposed two-stage algorithm was tested on small, medium, and large-scale examples. The results show that our algorithm is superior to other algorithms in solving similar problems.

Memory Based Lamarckian Evolutionary Algorithm for Job Shop Scheduling Problem

2011 ◽  
Vol 21 (12) ◽  
pp. 3082-3093
Author(s):  
Zhu-Chang XIA ◽  
Fang LIU ◽  
Mao-Guo GONG ◽  
Yu-Tao QI
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling
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