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Genetic Algorithms with Feasible Operators for Solving Job Shop Scheduling Problem

310 DOI: http://dx.doi.org/10.37398/JSR.2020.640157 Abstract: Job scheduling is one of the key activities performed in industries for manufacturing planning. In job scheduling, each job that contains various operations is allocated to one of the available machines for processing. Each job has a duration and each machine can handle only one operation at a time. An efficient allocation of jobs is mandatory for decreasing the makespan and idle time of the machines. In Job Shop Scheduling (JSS), the operations of the jobs are ordered. Genetic algorithm (GA) is a popular heuristic algorithm investigated to solve different scheduling problems. This paper presents feasibility preserving solution representation, initialization and operators for solving job shop scheduling problem. Proposed GA obtained best known results with good success rate for Lawrence (1984) datasets. Experiments show fast convergence of GA towards best solution. Hybridization of GA with local search or repair operator is required to obtain best solution with better success rate.