No abstract

A search for the decay K 0 S → μ + μ − is performed, based on a data sample of proton-proton collisions corresponding to an integrated luminosity of 3 fb −1 , collected by the LHCb experiment at centre-of-mass energies of 7 and 8 TeV. The observed yield is consistent with the background-only hypothesis, yielding a limit on the branching fraction of B(K 0 S → μ + μ − ) < 0.8 (1.0) × 10 −9 at 90% (95%) confidence level. This result improves the previous upper limit on the branching fraction by an order of magnitude.

As an important optimisation problem with a strong engineering background, stochastic flow shop scheduling with uncertain processing time is difficult because of inaccurate objective estimation, huge search space, and multiple local minima, especially NP-hardness. As an effective meta-heuristic, genetic algorithms (GAs) have been widely studied and applied in scheduling fields, but so far seldom for stochastic cases. In this paper, a hypothesis-test method, an effective methodology in statistics, is employed and incorporated into a GA to solve the stochastic flow shop scheduling problem and to avoid premature convergence of the GA. The proposed approach is based on statistical performance and a hypothesis test. It not only preserves the global search ability of a GA, but it can also reduce repeated searches for those solutions with similar performance in a statistical sense so as to enhance population diversity and achieve better results. Simulation results based on some benchmarks demonstrate the feasibility and effectiveness of the proposed method by comparison with traditional GAs. The effects of some parameters on the performance of the proposed algorithms are also discussed.

We discuss a scheduling problem for a twomachine robotic flow-shop with a bounded intermediate station and robots which is realistic in FMCs (flexible manufacturing cells). The problem asks to minimize the total weighted completion time. It is NP-hard. In this paper, we propose a heuristic algorithm based on GA (Genetic Algorithm) which is applicable to the problem, and which allows not only permutation, but also non-permutation schedules, because the latter has possibility to improve the former for this objective function. It is shown by numerical experiment that the proposed method is more effective than existing heuristics, and that there are some situations where the non-permutation scheduling is better than the permutation one.

Genetic algorithms (GAs) have been widely applied for many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of a GA is highly depend on its control parameters, but setting suitable parameters often involves tedious trial and error. Currently, setting optimal parameters is still a substantial problem and is one of the most important and promising areas for GAs. In this paper, the determination of optimal GA control parameters with limited computational effort and simulation replication constraints, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Then, the ordinal optimisation (OO) and the optimal computing budget allocation (OCBA) are applied to select the optimal GA control parameters, thereby providing a reasonable performance evaluation for hard flow shop scheduling problems. The effectiveness of the methodology is demonstrated by simulation results based on benchmarks.