model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NPhard problem, an improved genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption for implementing a feasible scheduling. Finally, a case study of production scheduling problem for metalworking workshop in a plant is simulated. The experimental resultsshow the relationship between the makespan and the energy consumption is apparently conflicting. Moreover, an energy saving decision is performed in a feasible scheduling. Using the decision method, there can be a significant potential to minimize energy consumption while complying with the conflicting relationship.

We present a two-step synthesis process to produce hierarchical ZnO nanoarchitectures that involves the preparation of ZnO nanosheet arrays by the pyrolysis of the precursor Zn5(OH)8Cl2 electrodeposited on conductive glass substrates, followed by the aqueous chemical growth (ACG) of dense ZnO single-crystalline nanowires on the surfaces of the primary ZnO nanosheets. The dye-sensitized solar cell (DSSC) based on the hierarchical ZnO nanowire−nanosheet architectures showed a power conversion efficiency of 4.8%, which is nearly twice as high as that of the DSSC constructed using a photoanode of bare ZnO nanosheet arrays. The better photovoltaic performance of hierarchical ZnO nanoarchitecture DSSC was due to a better dye loading and light harvesting as a consequence of the enlargement of the internal surface area within the photoanode. Moreover, the improved performance for the DSSC with the hierarchical ZnO nanowire−nanosheet architectures may be also ascribed to more light scattering behavior through extending the optical path length within the photoanode so as to increase the light harvesting. The results demonstrate potential application of the hierarchical ZnO nanoarchitectures derived from ZnO nanosheet arrays for highly efficient DSSCs.

We develop a singular stochastic control model for pricing variable annuities with the guaranteed minimum withdrawal benefit. This benefit promises to return the entire initial investment, with withdrawals spread over the term of the contract, irrespective of the market performance of the underlying asset portfolio. A contractual withdrawal rate is set and no penalty is imposed when the policyholder chooses to withdraw at or below this rate. Subject to a penalty fee, the policyholder is allowed to withdraw at a rate higher than the contractual withdrawal rate or surrender the policy instantaneously. We explore the optimal withdrawal strategy adopted by the rational policyholder that maximizes the expected discounted value of the cash flows generated from holding this variable annuity policy. An efficient finite difference algorithm using the penalty approximation approach is proposed for solving the singular stochastic control model. Optimal withdrawal policies of the holders of the variable annuities with the guaranteed minimum withdrawal benefit are explored. We also construct discrete pricing formulation that models withdrawals on discrete dates. Our numerical tests show that the solution values from the discrete model converge to those of the continuous model.