An Improved Ant Colony Optimization for Flexible Job Shop Scheduling Problems

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.

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Simulation Model of Ant Colony Optimization for the FJSSP

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch551 ◽

2011 ◽

pp. 3468-3474 ◽

Cited By ~ 1

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.

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Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

Mathematical and Computational Applications ◽

10.3390/mca24030080 ◽

2019 ◽

Vol 24 (3) ◽

pp. 80 ◽

Cited By ~ 1

Author(s):

Prasert Sriboonchandr ◽

Nuchsara Kriengkorakot ◽

Preecha Kriengkorakot

Keyword(s):

Differential Evolution ◽

Job Shop ◽

Job Shop Scheduling ◽

Differential Evolution Algorithm ◽

Flexible Job Shop ◽

Scheduling Problems ◽

Flexible Job Shop Scheduling ◽

Shop Scheduling ◽

Relative Errors ◽

Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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