Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

To verify the availability of the improved quantum genetic algorithm in solving the scheduling engineering personnel problem, the following work has been carried out: the characteristics of the scheduling engineering personnel problem are analyzed, the quantum encoding method is proposed, and an improved quantum genetic algorithm is applied to address the issue. Taking the low efficiency and the bad performance of the conventional quantum genetic algorithm into account, a universal improved quantum genetic algorithm is introduced to solve the scheduling engineering personnel problem. Finally, the examples are applied to verify the effectiveness and superiority of the improved quantum genetic algorithm and the rationality of the encoding method

Quantum annealing for vehicle routing and scheduling problems

Metaheuristic approaches to solving combinatorial optimization problems have many attractions. They sidestep the issue of combinatorial explosion; they return good results; they are often conceptually simple and straight forward to implement. There are also shortcomings. Optimal solutions are not guaranteed; choosing the metaheuristic which best fits a problem is a matter of experimentation; and conceptual differences between metaheuristics make absolute comparisons of performance difficult. There is also the difficulty of configuration of the algorithm - the process of identifying precise values for the parameters which control the optimization process. Quantum annealing is a metaheuristic which is the quantum counterpart of the well known classical Simulated Annealing algorithm for combinatorial optimization problems. This research investigates the application of quantum annealing to the Vehicle Routing Problem, a difficult problem of practical significance within industries such as logistics and workforce scheduling. The work devises spin encoding schemes for routing and scheduling problem domains, enabling an effective quantum annealing algorithm which locates new solutions to widely used benchmarks. The performance of the metaheuristic is further improved by the development of an enhanced tuning approach using fitness clouds as behaviour models. The algorithm is shown to be further enhanced by taking advantage of multiprocessor environments, using threading techniques to parallelize the optimization workload. The work also shows quantum annealing applied successfully in an industrial setting to generate solutions to complex scheduling problems, results which created extra savings over an incumbent optimization technique. Components of the intellectual property rendered in this latter effort went on to secure a patent-protected status

Operation cost reduction in unit commitment problem using improved quantum binary PSO algorithm

Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC is used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm

An Attempt to Set Standards for Studying and Comparing the Efficiency of Round Robin Algorithms

With the advent of the need for interactive systems, the urgent need for time-sharing systems has emerged. Round-robin algorithms have emerged to achieve time-sharing. The degree of performance of time-sharing systems depends largely on the length of the time slice in the round-robin algorithms. The length of the time slice affects the measuring criteria of the performance of the algorithms. Researchers suggested and are continuing suggesting algorithms in order to obtain the best values for the time slice. Adopting one algorithm over another in a system and for a class of applications requires choosing the best performing algorithm. This research is an attempt to develop an objective approach for accurate comparison between algorithms. For the sake of objectivity in comparison, five algorithms similar in their general characteristics were chosen; Modified Median Round Robin Algorithm(MMRRA), A New Median-Average Round Robin Scheduling Algorithm(NMARR), An Improved Round Robin Scheduling Algorithm with Varying Time Quantum (IRRVQ), A Modified Round Robin CPU Scheduling Algorithm with Dynamic Time Quantum (RRDT), Improved Round Robin Algorithm with Progressive Dynamic Quantum (IRRPDQ). The results showed that the outperformance of an algorithm over a group of algorithms according to a specific criterion is not permanent and fixed in value, and that resorting to statistical measures is the best way to clarify the degree of performance of the algorithms