Abstract
Job shop scheduling problem is typically a NP-Hard problem. In the recent past efforts put by researchers were to provide the most generic genetic algorithm to solve efficiently the job shop scheduling problems. Less attention has been paid to initial population aspects in genetic algorithms and much attention to recombination operators. Therefore authors are of the opinion that by proper design of all the aspects in genetic algorithms starting from initial population may provide better and promising solutions. Hence this paper attempts to enhance the effectiveness of genetic algorithm by providing a new look to initial population. This new technique along with job based representation has been used to obtain the optimal or near optimal solutions of 66 benchmark instances which comprise of varying degree of complexity.
Highlights
Scheduling is one of the most critical issues in planning and managing of manufacturing activities
According to the authors a systematic approach to modify the simple genetic algorithm would be to initiate the modification in initializing the population itself which helps in lowering the make span and with remaining two operators, it may be further reduced
With the random job selection for initial population followed by job based representation scheme adopted, the study was conducted with 50 generations and a population size of 1000.Mutation probability varies with 0.1 to 0.9 values dynamically and elite population size is 20%
Summary
Scheduling is one of the most critical issues in planning and managing of manufacturing activities. In most of the cases it is reasonable to use a technique which may yield a near optimal solution requiring a lesser amount of time compared to the methods listed above This has given rise to the use of heuristics, meta-heuristics or hybrid search algorithms Shifting bottleneck procedure, tabu search, ant colony etc.) by many researchers in the recent past These algorithms have potential to find high quality solutions in a reasonable computational time. Genetic algorithms were first successfully applied by Davis in 1985 [8] Scheduling rules such as Shortest Processing Time (SPT), Most Work Remaining (MWKR) were used by Zhou and Feng [9], in his proposed hybrid heuristics GA for JSSP. It may be noted that simple genetic algorithm essentially consists of mainly three aspects and needs critical observation They are initialization, cross over and mutation operations. According to the authors a systematic approach to modify the simple genetic algorithm would be to initiate the modification in initializing the population itself which helps in lowering the make span and with remaining two operators, it may be further reduced
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