Abstract
Problem statement: The problem of scheduling n jobs on m machines with each job having specific machine route has been researched over the decade. The Job Shop Scheduling (JSS) is one of the hardest combinatorial optimization problems. Each resource can process at most one job at a time. Approach: This study proposes a new approach to solve a Job Shop Scheduling problem with the help of integrating Genetic Algorithm (GA) and Tabu Search (TS). After an initial schedule is obtained the GA, the result is given as an input to TS to improve the status of the initial schedule. The objective of this study is to minimize the makespan, process time and the number of iterations. This approach achieves a better result with the help of efficient chromosome representation, powerful crossover strategies and neighborhood strategies. Results: This research resolves the allocation of operation to different machine and the sequence of operation based on machine sequence. Job Scheduling is the process of completing jobs over a time with allocation of shared resources. It is mainly used in manufacturing environment, in which the jobs are allocated to various machines. Jobs are the activities and a machine represents the resources. It is also used in transportation, services and grid scheduling. Conclusion/Recommendations: The result and performance of the proposed work is compared with the other conventional algorithm and it is also testing using standard benchmark problems.
Highlights
Meta-heuristics is used to solve with the computationally hard optimization problems
Metaheuristics are used as a standalone approach for solving hard combinatorial optimization problems
Job is represented by each gene in chromosome and the job sequence in a schedule based on the position of the gene
Summary
Meta-heuristics is used to solve with the computationally hard optimization problems. Metaheuristics consist of a high level algorithm that guides the search using other particular methods. Metaheuristics are used as a standalone approach for solving hard combinatorial optimization problems. The standalone approach is drastically changed and attention of researchers has shifted to consider another type of high level algorithms, namely hybrid algorithms. There are at least two issues has to be considered while combining the more than one metaheuristics: (a) how to choose the meta-heuristic methods to combine and (b) how to combine the chosen heuristic methods into new hybrid approaches. There are no theoretical foundations for these issues For the former, different classes of search algorithms can be considered for the purposes of hybridization, such as exact methods, simple heuristic methods and meta-heuristics. Our hybrid approach combines Genetic Algorithms (GAs) and Tabu Search (TS) methods. Implementation of the HGATS to the JSSP is given with the algorithm using the proposed method and the experimental results and a discussion of the proposed method are given and a conclusion and future enhancement is given
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