Abstract
Due to NP-Hard nature of the Job Shop Scheduling Problems (JSP), exact methods fail to provide the optimal solutions in quite reasonable computational time. Due to this nature of the problem, so many heuristics and meta-heuristics have been proposed in the past to get optimal or near-optimal solutions for easy to tough JSP instances in lesser computational time compared to exact methods. One of such heuristics is genetic algorithm (GA). Representations in GA will have a direct impact on computational time it takes in providing optimal or near optimal solutions. Different representation schemes are possible in case of Job Scheduling Problems. These schemes in turn will have a higher impact on the performance of GA. It is intended to show through this paper, how these representations will perform, by a comparative analysis based on average deviation, evolution of solution over entire generations etc.
Highlights
Scheduling is a decision-making process which deals with allocation of resources to tasks over given time-periods and its goal is to optimize one or more objective functions
It is assumed that problems describing numerical data are binary encoded and the number of steps involved in solving these increases exponentially with increase in length of string and computational time will be enormously large and treated to be hard problems
Reproduction probability used in our experiment is 0.1 Parents in our experiment are selected from two groups sorted out based on fitness value
Summary
Scheduling is a decision-making process which deals with allocation of resources to tasks over given time-periods and its goal is to optimize one or more objective functions. Being essentially a combinatorial optimization problem, job shop scheduling has caught the attention of researchers in the last so many years for optimized. Combinatorial optimization problems can be classified as easy and hard. Problems which are polynomialy solvable with limited number of variables are treated easy and are called P. It is assumed that problems describing numerical data are binary encoded and the number of steps involved in solving these increases exponentially with increase in length of string and computational time will be enormously large and treated to be hard problems. Job scheduling problems belong to this category and are termed NP-Hard [1]. In the practical manufacturing environment, the scale of job shops is generally much larger than that of JSSP bench mark instances considered in theoretical research
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