An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches

In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches.

Normalizing Book Citations in Google Scholar: A Hybrid Cited-side Citing-side Method

Purpose: To design and test a method for normalizing book citations in Google Scholar.Design/methodology/approach: A hybrid citing-side, cited-side normalization method was developed and this was tested on a sample of 285 research monographs. The results were analyzed and conclusions drawn.Findings: The method was technically feasible but required extensive manual intervention because of the poor quality of the Google Scholar data. Research limitations: The sample of books was limited and also all were from one discipline —business and management. Also, the method has only been tested on Google Scholar, it would be useful to test it on Web of Science or Scopus.Practical limitations: Google Scholar is a poor source of data although it does cover a much wider range citation sources that other databases. Originality/value: This is the first method that has been developed specifically for normalizing books which have so far not been able to be normalized.

方向规模收益与方向规模弹性

规模收益(Returns to Scale)是组织绩效分析所关心的一个重要问题.它可以帮助决策者决策是应该扩大还是减少组织规模,从而提高组织运行的绩效.经济学中传统规模收益的定义是基于生产要素按相同比例变化而引起的产出的变化率(径向变化).但对于多投入多产出的非传统生产过程(例如公共部门)来说,往往并不是按相同比例扩张各类投入要素.在这种情况下,如何描述生产过程规模变化的过程中的规模收益情况是一个重要的问题.针对这种现象,在帕累托(Pareto)偏好下,从全局和局部视角定义了方向规模收益和方向规模弹性的概念,并给出具体函数表达.

基于DEA的生物领域研究所规模收益分析

本文旨在分析2011年中国科学院15个生物领域研究所的规模收益变化情况。首先,本文提出分析所用的投入产出指标:人员、经费、SCI论文、高质量论文和研究生培养;其次,本文采用杨国梁(2012)提出的基于DEA的方向规模收益、最优投入方向以及"方向阻塞"效应的判断方法深入分析中科院生物领域研究所的方向规模收益、最优投入方向以及"方向阻塞"效应。基于以上分析,我们发现:一是通过规模收益分析,可以找到各研究所规模收益递增(递减、不变)的区域,以及投入进一步增加和减少的最优方向,作为研究所调整其组织规模的决策依据之一;二是通过"方向阻塞"效应的分析,我们发现部分研究所存在"阻塞"效应,同时在某些方向上存在"方向阻塞"效应。在这种情况下,进一步增加投入会导致产出的降低,因此研究所需要深入挖掘导致"阻塞"效应的原因,避免进一步扩大投入的规模,才能实现科技资源合理配置和高效利用。

等同论文数(EPN)：学术论文质量评估的新指标

现有的学术论文质量评估指标主要基于论文的引文数,关注论文产出的影响。本文从经济学价值理论出发,聚焦于学术论文创作过程,提出通过测量学术论文中包含的等同工作量,实现对学术论文质量的评估,从而提出一系列基于等同论文数的指标,例如X指标等。实例研究表明,基于等同论文数的系列指标具有较大的优越性。

Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment

Smart manufacturing in the“Industry 4.0”strategy promotes the deep integration of manufacturing and information technologies,which makes the manufacturing system a ubiquitous environment.However,the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers.To deal with this challenge,this study focuses on the real-time hybrid flow shop scheduling problem(HFSP).First,the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed,and its scheduling problem is described.Second,a real-time scheduling approach for the HFSP is proposed.The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the real-time status in the hybrid flow shop.With the scheduling rule,the priorities of the waiting job are calculated,and the job with the highest priority will be scheduled at this decision time point.A group of experiments are performed to prove the performance of the proposed approach.The numerical experiments show that the real-time scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances.Therefore,the contribution of this study is the proposal of a real-time scheduling approach,which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.

Stochastic leaderefollower DEA models for two-stage systems

Data envelopment analysis(DEA)is a non-parametric approach for measuring the relative efficiencies of peer decision making units(DMUs).In recent years,it has been widely used to evaluate two-stage systems under different organization mechanisms.This study modifies the conventional leaderefollower DEA models for two-stage systems by considering the uncertainty of data.The dual deterministic linear models are first constructed from the stochastic CCR models under the assumption that all components of inputs,outputs,and intermediate products are related only with some basic stochastic factors,which follow continuous and symmetric distributions with nonnegative compact supports.The stochastic leaderefollower DEA models are then developed for measuring the efficiencies of the two stages.The stochastic efficiency of the whole system can be uniquely decomposed into the product of the efficiencies of the two stages.Relationships between stochastic efficiencies from stochastic CCR and stochastic leaderefollower DEA models are also discussed.An example of the commercial banks in China is considered using the proposed models under different risk levels.

Time-consistent investment and reinsurance strategies for insurers under multi-period mean-variance formulation with generalized correlated returns

The existing literature on investment and reinsurance is limited to the study of continuous-time problems,while discrete-time problems are always ignored by re-searchers.In this study,we first discuss a multi-period investment and reinsurance opti-mization problem under the classical mean-variance framework.When the asset returns with a serially correlated structure,the time-consistent investment and reinsurance strategies are acquired via backward induction.In addition,we propose an alternative time-consistent mean-variance optimization model that contrasts with the classical mean-variance model,and the corresponding optimal strategy and value function are also derived.We find that the investment and reinsurance strategies are both independent of the current wealth for the above two optimization problems,which coincides with the conclusion presented in the continuous-time problems.Most importantly,the above in-vestment strategies with serially correlated structures are both conditional mean-based strategies,rather than unconditional ones.Finally,we compare the investment and rein-surance strategies suggested above based on the simulation approach,to shed light on which investment-reinsurance strategies are more suitable for insurers.