TUNINGS OF PARAMETERS AND PHEROMONE UPDATE STRATEGY IN ANT COLONY OPTIMIZATION

2010 ◽  
Vol 09 (01) ◽  
pp. 73-83
Author(s):  
A. TAMILARASI
Keyword(s):  
Ant Colony Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Parameter Tuning ◽  
Ant Colony ◽  
Shop Floor ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Update Strategy

Scheduling is considered to be a major task to improve the shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the Ant Colony Optimization meta heuristic to job shop problem. The main characteristics of this model are positive feedback and distributed computation. The settings of parameter values have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy and also to analyze the quality of the solution for different values of the parameters. In this paper, we present statistical analysis for parameter tuning and we compare the quality of obtained solutions by the proposed method with the competing algorithms given in the literature for well known benchmark problems in job 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.

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