scholarly journals An Ant Colony Optimization Algorithm for Flexible Job Shop Scheduling Problem

10.5772/9425 ◽  
2010 ◽  
Author(s):  
S. G. ◽  
N. Jawahar ◽  
B. S.
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Ant Colony Optimization Algorithm ◽  
Shop Scheduling

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.

scholarly journals Improved Whale Algorithm for Solving the Flexible Job Shop Scheduling Problem

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 384 ◽  
Author(s):  
Fei Luan ◽  
Zongyan Cai ◽  
Shuqiang Wu ◽  
Tianhua Jiang ◽  
Fukang Li ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Whale Optimization Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Whale Optimization

In this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP, the conversion method between the whale individual position vector and the scheduling solution is firstly proposed. Secondly, a resultful initialization scheme with certain quality is obtained using chaotic reverse learning (CRL) strategies. Thirdly, a nonlinear convergence factor (NFC) and an adaptive weight (AW) are introduced to balance the abilities of exploitation and exploration of the algorithm. Furthermore, a variable neighborhood search (VNS) operation is performed on the current optimal individual to enhance the accuracy and effectiveness of the local exploration. Experimental results on various benchmark instances show that the proposed IWOA can obtain competitive results compared to the existing algorithms in a short time.

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