Conference Papers

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    A modified Ant Colony optimization algorithm with load balancing for job shop scheduling
    (IEEE Computer Society help@computer.org, 2013) Chaukwale, R.; Kamath S․, S.S.
    The problem of efficiently scheduling jobs on several machines is an important consideration when using Job Shop scheduling production system (JSP). JSP is known to be a NP-hard problem and hence methods that focus on producing an exact solution can prove insufficient in finding an optimal resolution to JSP. Hence, in such cases, heuristic methods can be employed to find a good solution within reasonable time. In this paper, we study the conventional ACO algorithm and propose a Load Balancing ACO algorithm for JSP. We also present the observed results, and discuss them with reference to the conventional ACO. It is observed that the proposed algorithm gives better results when compared to conventional ACO. © 2013 IEEE.
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    Hybrid heuristic shifting bottleneck procedure for parallel-machine job-shop scheduling using GPU
    (Institute of Electrical and Electronics Engineers Inc., 2015) Vilasagarapu, S.; Guddeti, G.
    In this paper, we implement the Parallel-Machine Job Shop Scheduling Problem (JSSP) using the modified shifting bottleneck procedure along with the heuristic Tabu search algorithm. JSSP has several real-time applications such as product manufacturing units, real-world train scheduling problem etc., Since JSSP is an NP hard problem, an optimal solution may not exist hence with the help of heuristic algorithm we try to find the approximate solution. Experimental results demonstrate that the modified shifting bottleneck procedure (SBP) shows better results than the existing SBP. And also with the help of meta-heuristic algorithm, the JSSP for larger instances can be solved easily. Results also demonstrate that GPU based Tabu search is on an average 1.8 times faster than CPU based Tabu search. © 2015 IEEE.
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    Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization
    (Springer Verlag service@springer.de, 2017) Dani, V.; Sarswat, A.; Swaroop, V.; Domanal, S.; Guddeti, G.R.M.
    Job Shop Scheduling problem has wide range of applications. However it being a NP-Hard optimization problem, always finding an optimal solution is not possible in polynomial amount of time. In this paper we propose a heuristic approach to find near optimal solution for Job Shop Scheduling Problem in predetermined amount of time using Cat Swarm Optimization. Novelty in our approach is our non-conventional way of representing position of cat in search space that ensures advantage of spatial locality is taken. Further while exploring the search space using randomization, we never explore an infeasible solution. This reduces search time. Our proposed approach outperforms some of the conventional algorithms and achieves nearly 86% accuracy, while restricting processing time to one second. © 2017, Springer International Publishing AG.