一般梯形模糊逼近算子及其在模糊运输问题中的应用

研究运输成本信息为一般模糊数的模糊运输问题.首先,在保持一般模糊数的核不变的条件下,建立一般模糊数与一般梯形模糊数的距离最小优化模型,通过求解模型得到一般模糊数的一般梯形模糊逼近算子,并给出该逼近算子具有的性质如数乘不变性、平移不变性、连续性等.然后利用该逼近算子将一般模糊运输信息表转换成一般梯形模糊运输信息表,再根据已有GFLCM和GFMDM算法得到模糊运输问题的近似最优解,最后给出具体算例分析说明方法的有效性和合理性.     

Optimal investment with transaction costs based on exponential utility function: a parabolic double obstacle problem

This paper concerns optimal investment problem with proportional transaction costs and finite time horizon based on exponential utility function. Using a partial differential equation approach, we reveal that the problem is equivalent to a parabolic double obstacle problem involving two free boundaries that correspond to the optimal buying and selling policies. Numerical examples are obtained by the binomial method.     

多态系统部件维修成本评估分析

多态系统是可靠性理论中的一种重要系统,是指部件和系统具有多个状态。随着部件的劣化,系统的性能随之下降。为了提高系统的性能,部件需要进行一定的维修。本文考虑部件状态转移率与停留时间的关系,利用重要度理论来分析部件维修成本的变化规律,得出对系统维修成本影响最大的部件。首先,基于维修成本的函数关系,给出了重要度的表达式;其次,针对典型串联系统和并联系统,随着时间推移,给出维修成本的变化规律;最后算例仿真验证了提出方法的有效性和正确性。     

Transaction activity and bitcoin realized volatility

We study the predictive value of transaction activity in the bitcoin network for the realized volatility of bitcoin returns constructed by high-frequency data. As an alternative modeling approach to the popular linear heterogeneous autoregressive model, we provide out-of-sample forecasts for realized volatility of bitcoin returns employing machine learning algorithms, and in particular by Random Forests. Our findings reveal that on-blockchain transaction activity does improve the out-of-sample forecast accuracy at all the forecast horizons considered.     

Reinforcement learning versus heuristics for order acceptance on a single resource

Order Acceptance (OA) is one of the main functions in business control. Accepting an order when capacity is available could disable the system to accept more profitable orders in the future with opportunity losses as a consequence. Uncertain information is also an important issue here. We use Markov decision models and learning methods from Artificial Intelligence to find decision policies under uncertainty. Reinforcement Learning (RL) is quite a new approach in OA. It is shown here that RL works well compared with heuristics. It is demonstrated that employing an RL trained agent is a robust, flexible approach that in addition can be used to support the detection of good heuristics.     

Partial Pooling in Tandem Lines with Cooperation and Blocking

For a tandem line of finite, single-server queues operating under the production blocking mechanism, we study the effects of pooling several adjacent stations and the associated servers into a single station with a single team of servers. We assume that the servers are cross-trained (so that they can work at several different stations) and that two or more servers can cooperate on the same job. For such a system, we provide sufficient conditions on the service times and sizes of the input and output buffers at the pooled station under which pooling will decrease the departure time of each job from the system (and hence increase the system throughput). We also show that pooling decreases the total number of jobs in the system at any given time and the sojourn time of each job in the system if the departure time of each job from the system is decreased by pooling and there is an arrival stream at the first station. Moreover, we provide sufficient conditions under which pooling will improve the holding cost of each job in the system incurred before any given time, and extend our results to closed tandem lines and to queueing networks with either a more general blocking mechanism or probabilistic routing. Finally, we present a numerical study aimed at quantifying the improvements in system performance obtained through pooling and at understanding which stations should be pooled to achieve the maximum benefit. Our results suggest that the improvements gained by pooling may be substantial and that the bottleneck station should be among the pooled stations in order to obtain the greatest benefit. AMS subject classification: 90B22     

Application of average dynamic programming to inventory systems

We show the existence ofaverage cost (AC-) optimal policy for an inventory system withuncountable state space; in fact, the AC-optimal cost and an AC-optimal stationary policy areexplicitly computed. In order to do this, we use a variant of thevanishing discount factor approach, which have been intensively studied in recent years but the available results not cover the inventory problem we are interested in.The work of the first author (OVA) was partially supported by Fondo del Sistema de Investigación del Mar de Cortéz under grant SIMAC/94/CT-005. The work of the second author (RMdO) was partially supported by Consejo Nacional de Ciencia y Tecnologia (CONACyT) under grant 0635P-E9506.     

On the evaluation of fixed permutations as strategies in stochastic scheduling

A general model is proposed for the stochastic version of the single-machine allocation problem. Sufficient conditions are given to ensure that there is an optimal strategy given by a fixed permutation of the job set. Additional results are given for an important special case of the general model involving simple jobs. The paper concludes with material concerning the evaluation of fixed permutations as strategies under conditions more general than the sufficient conditions mentioned above.     

考虑最佳入口温度的多股流换热器网络循环优化

针对于多股流换热器网络的应用优势,本文在衍生网络优化方法基础上,提出了考虑最佳入口温度的多股流换热器循环优化方法,即利用多股流换热器网络小面积换热器合并而不产生固定投资费用这一特点,将优化结果中存在公用工程的流体返回到另一种流体的入口并与其入口流体进行循环优化。结果表明,这种优化方法能够进一步发挥多股流换热器网络的优点,得到年综合费用更小、运行费用更低的网络结构。     

Scaling and long-range dependence in option pricing III: A fractional version of the Merton model with transaction costs

A model for option pricing of fractional version of the Merton model with ‘Hurst exponent’ H being in [1/2,1) is established with transaction costs. In particular, for H(1/2,1) the minimal price C_min(t,S_t) of an option under transaction costs is obtained, which displays that the timestep δt and the ‘Hurst exponent’ H play an important role in option pricing with transaction costs.