This paper is focused on the analysis of different heuristic cost functions and introduces a new statement of this function.
After calculating all values G, H1, H2, we will calculate the value of the heuristic cost function F by formula (1).
By extending SPT to address dynamic traffic scenarios, we also propose two heuristic algorithms, Cost-based SPT (CB_SPT) and Wavelength-based SPT (WB_SPT).
An option is to extend this heuristic (at cost of additional complexity) by looking several steps ahead, that is, considering more than one critical bottleneck link at each iteration.
The introduced knowledge based system delivers a treatment sequence based on heuristics and cost estimation.
In recognition of this, we argue that a first step towards a learned optimizer is to understand the classical components, such as plan space parametrization, search heuristics, and cost modeling, as statistical learning processes.
For improving assembly line performance, different approaches are used by researchers which includes use of lean techniques, classical mathematical models, process simulation using commercial software's, meta-heuristic approach, cost based approach, integrated approach, etc.
They proposed a real-time image-inpainting algorithm based on appearance and spatial cost functions, heuristic optimization of the cost functions, and multi-resolution optimization [10].
Furthermore, there is not a great difference among heuristic and optimal costs.
Our results show that compared to heuristic methods, the cost of monitoring mechanism can be reduced significantly.
