scholarly journals A Performance Study for the Multi-objective Ant Colony Optimization Algorithms on the Job Shop Scheduling Problem

2015 ◽  
Vol 132 (14) ◽  
pp. 1-8 ◽  
Author(s):  
I.D.I.D. Ariyasingha ◽  
T.G.I. Fernando
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Performance Study ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Ant Colony Optimization Algorithms

scholarly journals Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Wang ◽  
Jingcao Cai ◽  
Ming Li ◽  
Zhihu Liu
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Computational Time ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP) plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO) has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO) is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

Implementation of an Ant Colony Optimization technique for job shop scheduling problem

2006 ◽  
Vol 28 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Jun Zhang ◽  
Xiaomin Hu ◽  
X. Tan ◽  
J. H. Zhong ◽  
Q. Huang
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Technique ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Simulation Model of Ant Colony Optimization for the FJSSP

2011 ◽  
pp. 3468-3474 ◽  
Author(s):  
Li-Ning Xing ◽  
Ying-Wu Chen ◽  
Ke-Wei Yang
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Ant Colony ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

The job shop scheduling problem (JSSP) is generally defined as decision-making problems with the aim of optimizing one or more scheduling criteria. Many different approaches, such as simulated annealing (Wu et al., 2005), tabu search (Pezzella & Merelli, 2000), genetic algorithm (Watanabe, Ida, & Gen, 2005), ant colony optimization (Huang & Liao, 2007), neural networks (Wang, Qiao, &Wang, 2001), evolutionary algorithm (Tanev, Uozumi, & Morotome, 2004) and other heuristic approach (Chen & Luh, 2003; Huang & Yin, 2004; Jansen, Mastrolilli, & Solis-Oba, 2005; Tarantilis & Kiranoudis, 2002), have been successfully applied to JSSP. Flexible job shop scheduling problem (FJSSP) is an extension of the classical JSSP which allows an operation to be processed by any machine from a given set. It is more complex than JSSP because of the addition need to determine the assignment of operations to machines. Bruker and Schlie (1990) were among the first to address this problem.

Research of Multi-Objective Optimization Study for Job Shop Scheduling Problem Based on Grey Ant Colony Algorithm

2011 ◽  
Vol 308-310 ◽  
pp. 1033-1036 ◽  
Author(s):  
Ya Dong Fang ◽  
Fang Wang ◽  
Hui Wang
Keyword(s):  
Ant Colony Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Method ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Processing Order

In order to resolve Multi-objective job shop scheduling problem, an optimization method of many goals scheduling based on grey relation theory and ant colony algorithm is proposed. Firstly, this paper introduces the relevant mathematical theory. AHP and Grey relational analysis, and they are combined to solve the choice of pre-processing equipment under the multi-objective conditions. What's more, ant colony algorithm is discussed to solve problem of processing order for machine. The effectiveness of multi-objective algorithm for job shop scheduling problem is verified through applying example.

Sign in / Sign up

Export Citation Format

Share Document