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.

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.

scholarly journals A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem

2019 ◽  
Vol 33 ◽  
pp. 2262-2271 ◽  
Author(s):  
Junwen Ding ◽  
Zhipeng Lü ◽  
Chu-Min Li ◽  
Liji Shen ◽  
Liping Xu ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
State Of The Art ◽  
Computational Time ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Population-based evolutionary algorithms usually manage a large number of individuals to maintain the diversity of the search, which is complex and time-consuming. In this paper, we propose an evolutionary algorithm using only two individuals, called master-apprentice evolutionary algorithm (MAE), for solving the flexible job shop scheduling problem (FJSP). To ensure the diversity and the quality of the evolution, MAE integrates a tabu search procedure, a recombination operator based on path relinking using a novel distance definition, and an effective individual updating strategy, taking into account the multiple complex constraints of FJSP. Experiments on 313 widely-used public instances show that MAE improves the previous best known results for 47 instances and matches the best known results on all except 3 of the remaining instances while consuming the same computational time as current state-of-the-art metaheuristics. MAE additionally establishes solution quality records for 10 hard instances whose previous best values were established by a well-known industrial solver and a state-of-the-art exact method.

Sign in / Sign up

Export Citation Format

Share Document