K. S. Sivaraman ¹, S. Gautam ¹, S. Sarvesh ¹, Archit Khullar ¹, A. Baskar ¹, Shriram K. Vasudevan ¹

Affiliations
1 Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (University), Coimbatore, IN

Abstract

Objective: This research aims at formulating a method to categorise a given class of objects by obtaining a weighted matrix computed as explained below. Methods/Analysis: The method deployed can be branched into two phases: Training and Testing. In the first phase, a set of images of the concerned objects are taken. By set of images, one can refer to images of different objects, or different positions of the same object. The features are then, extracted for these input images and stored in the database as vectors. Any computation hence forth, is performed using these vectors. In testing stage, the algorithm uses its knowledge to identify the input image to a specified class. Findings: Our method is computationally inexpensive since all the calculations are performed on the basic grounds of matrix operations. This method is not just limited to the domain of object recognition alone. Any real-time entity that can be statistically represented in a vector form can be deployed. All that is required of the application is that the range of vectors is defined so as to obtain the minimum components and maximum components, individually. Once this is obtained, the algorithm will be sufficient to identify any input and will accordingly determine the category to which it belongs. The only challenge identified is that the range of vectors obtained from the input data for various categories must not overlap. That being the case will result in multiple hits or in simpler words, will give an incorrect result. Further work can be implemented on how to make the algorithm independent of this dependency. Also, the algorithm improves the results through various illumination and scaling conditions and this has been discussed in results and analysis section.Even with the existing methods to recognize an object, this algorithm can be combined to categorize or classify objects. Conclusion/Application: The proposed algorithm successfully classifies the input image into one of the trained categories by identifying the features followed by computing these obtained features as prescribed the given algorithm.

Keywords

Geometrical Modelling, Object Detection, Object Identification, Object Recognition, Weighted Matrix.

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A. Baskar ¹

Affiliations
1 Apollo Engineering College, Poonamallee, Chennai - 602 105, Tamilnadu, IN

Abstract

Background/Objectives: Permutation flow shop scheduling deals with finding a sequence and makespan for the jobs, the same for each machine, so that all the jobs are to be finished at the earliest. This paper tries to improve the makespan. Methods/Statistical Analysis: Using different initial partial sequences, job insertion technique of NEH algorithm is used in combination with simulation for a fixed number of runs. The popular NEH algorithm is considered as the parent algorithm to find the initial solution, and the makespan is minimized in two stages of simulation. 120 numbers of benchmark problems proposed by Taillard have been used. Codes were generated in MATLAB 2008a, and run in an i5 PC with 4 GB RAM. Findings: The computational results show that in about 67% of the cases, the makespan is minimized. The improvement is up to 4.67%. One way ANOVA has been carried out to assess the means of the out puts using MINITAB software. In the analysis, the F value is small (0.41) and the P value is > 0.05 (0.661) and hence, the Null Hypothesis is accepted. Application/Improvements: In addition to the improved makespan, another advantage is that, additional sequences are obtained which can help in efficient scheduling or re-scheduling the jobs. It is also simple, easy to understand and use. Further improvements are possible by changing the initial partial sequences.

Keywords

Makespan, NEH Algorithm, Permutation Flow Shop, Scheduling, Simulation

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A. Baskar ¹, M. Anthony Xavior ², B. Dhanasakkaravarthi ¹

Affiliations
1 Panimalar Institute of Technology, Chennai - 600123, Tamil Nadu, IN
2 SMBS, VIT University, Vellore – 632014, Tamil Nadu, IN

Abstract

Objectives: This paper analyzes the impact of a few new initial partial sequences on the makespan in a permutation flow shop scheduling problem. Taillard benchmark problems are used for the purpose of validation. Methods/Statistical Analysis: The popular NEH heuristic considers the first two jobs as its initial partial sequence after arranging them in descending order of their total processing times. The algorithms using different partial sequences are coded in MATLAB and a total of 120 number problem instances are used for the analysis which fall under twelve sets of 10 instances each. One-way ANOVA has been conducted for validating the results. Findings: It has been found that the initial partial sequences other than the first two jobs considered by the original NEH can also yield better makespans. Also, initial ordering of jobs according to the decreasing order of the average processing time and standard deviation of the processing times proposed by in. perform relatively better. In all the cases, job insertion technique is proved to be more powerful. The random algorithm that uses the job insertion technique do perform well with a deviation of 3.4342% which is better than many other known simple algorithms. The ANOVA confirms that the variants are statically not different from the NEH algorithm. But, it shows that a few variants perform better than the NEH for the Taillard benchmark problems. Application/Improvements: The results can be used as a seed solution and could be improved using metaheuristics. Further, the authors are working on other benchmarks and using tie breaking rules to know the impact..

Keywords

Initial Partial Sequence, Makespan, NEH Heuristic, Permutation Flow Shop, Scheduling

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