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This paper addresses the scheduling of machines, an Automated Guided Vehicle (AGV) and two robots in a Flexible Manufacturing System (FMS) formed in three loop layouts, with objectives to minimize the makespan, mean flow time and mean tardiness. The scheduling optimization is carried out using Sheep Flock Heredity Algorithm (SFHA) and Artificial Immune System (AIS) algorithm. AGV is used for carrying jobs between the Load/Unload station and the machines. The robots are used for loading and unloading the jobs in the machines, and also used for transferring jobs between the machines. The algorithms are applied for test problems taken from the literature and the results obtained using the two algorithms are compared. The results indicate that SFHA performs better than AIS for this problem.

A B S T R A C T This paper addresses the scheduling of machines, an Automated Guided Vehicle (AGV) and two robots in a Flexible Manufacturing System (FMS) formed in three loop layouts, with objectives to minimize the makespan, mean flow time and mean tardiness. The scheduling optimization is carried out using Sheep Flock Heredity Algorithm (SFHA) and Artificial Immune System (AIS) algorithm. AGV is used for carrying jobs between the Load/Unload station and the machines. The robots are used for loading and unloading the jobs in the machines, and also used for transferring jobs between the machines. The algorithms are applied for test problems taken from the literature and the results obtained using the two algorithms are compared. The results indicate that SFHA performs better than AIS for this problem.

A new evolutionary computation algorithm, Superbug algorithm, which simulates evolution of bacteria in a culture, is proposed. The algorithm is developed for solving large scale optimization problems such as scheduling, transportation and assignment problems. In this work, the algorithm optimizes machine schedules in a Flexible Manufacturing System (FMS) by minimizing makespan. The FMS comprises of four machines and two identical Automated Guided Vehicles (AGVs). AGVs are used for carrying jobs between the Load/Unload (L/U) station and the machines. Experimental results indicate the efficiency of the proposed algorithm in its optimization performance in scheduling is noticeably superior to other evolutionary algorithms when compared to the best results reported in the literature for FMS Scheduling.

Problem statement: This study proposes a new idea for generation of minimized test suite in the test case generation using the mutant gene algorithm, which not only identifies the best test cases but also reduces the number of test cases generated, selects test cases optimally there improving the performance in testing of software. Test cases are generated by using branch coverage algorithm and a coverage table is created for verifying branch coverage. Approach: The process of minimization was done through Mutant gene algorithm. Mutant gene algorithm combined both the mutation testing process and genetic algorithm. Initially a number of chromosomes were generated in random order. Mutation score was used for finding fitness function. The fitness function was found for all the randomly generated chromosomes by applying the mutant score to the function. Rank based selection was used for selecting the chromosomes. After the selection of the chromosomes one-point crossover was performed. A population of chromosomes obtained, which was given as the input for the next iteration. Large iterations were performed to obtain the best test case with higher fitness value, it was the end condition. Results: Between the measured iterations the value of the mutant score remained constant. The results of the experiments showed that the minimization process was competitive with other methods and even outperforms them for complex cases. Conclusion: The whole generation and minimization process was fully automated; redundant explorations of test case were avoided, resulting in efficient generation of test cases.