新三板上市中财务合规性问题的研究

在我国中小企业经营发展过程中,新三板上市中财务合规是重点考虑问题,关系到企业的未来发展。为保证财务合规性问题不会影响企业挂牌,应该对此问题深层次分析,诸如内部控制问题、持续经营能力问题、财务管理基础问题等。同时对问题成因深入剖析,在遵循合法性、适应性、效用性原则的基础上,企业合理制定应对办法,保证问题在有效解决的同时,保持可持续发展进程的有序推进。

财务记账中原始凭证的合规性问题及其对策探讨

原始凭证是否合规,直接关系企业财务管理的效率与水平,在实际工作中,原始凭证容易出现不合规问题。为此,本文从财务记账的角度,对原始凭证的合规性问题进行了归纳和总结,并结合笔者多年的工作实践,提出了相关的对策建议,以期为提高企业财务管理水平提供有益参考和借鉴。

医院采购合同管理的合规风险识别与策略研究

医院采购合同管理是医疗机构运行的重要环节,其合规性问题严重影响医院的合法权益和运营效率。本文首先对医院采购合同管理的背景与意义进行深入阐述,明确其在医院运营管理中的关键作用;并通过对合规性问题的定义和特性分析,明确不规范的合同管理行为对医院采购效率和经济利益产生的负面影响。针对此类问题,本文提出了加强内部合规管理、完善合同管理体系、以及加强外部合规合同监管等解决策略,以期通过完善医院采购合同管理,提高医院的运营效率和经济效益。最后,本文通过综述和展望,对未来医院采购合同管理合规性问题的研究提出了建设性意见,为善待医院采购合同管理中的合规性问题,提供了一种具有参考意义的解决方向。

电子商务时代下审计信息化的合规问题分析

文章旨在探讨电子商务时代下审计信息化的合规问题。随着信息技术的快速发展,电子商务已经成为现代商业活动的主要形式。在这一背景下,审计也面临着从传统方式向信息化方式的转变,这引发了一系列合规性问题。文章首先分析了电子商务与审计的影响,接着从数据隐私、网络安全等几个方面探讨了电子商务审计信息化过程中的合规性挑战。通过深入剖析,可以更好地理解并应对电子商务时代下审计信息化所面临的合规性问题。

Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

Optimization of Portfolio of Stocks at ZSE through the Analysis of Historical Data

Decision-making of investors at the stock exchange can be based on the fundamental indicators of stocks, on the technical indicators, or can exist as a combination of these two methods. The paper gives emphasis to the domain of technical analysis. In the broader sense the technical analysis enables the dynamics of the expected future values of the shares estimation. This can be performed on the basis of the data on historical trends of the revenues, profits and other indicators from the balance sheet, but also on the basis of historical data on changes in the values of the shares. Companies generally belong to the different sectors that have different presumptions of development resulting from the global market trends, technology and other characteristic. Processing of historical data values of the outstanding shares of the Zagreb Stock Exchange （ZSE） is origination of this research. Investors are interested to know the estimation of future returns for the stocks as well as the size of the risk associated with the expected returns. Research task in this paper is finding the optimal portfolio at the ZSE based on the concept of dominant portfolio by Markowitz approach. The portfolio is created by solving non-linear programming problem using the common software tools. The results of obtained optimal portfolios contain relevant conclusions about the specifics of the shares as well as the characteristics of the industrial sectors but also provide a further knowledge about diverse sectors treatment at the stock exchange in a multi-year period.

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

With diversified requirements and varying manufacturing environments, the optimal production planning for a steel mill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming(MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome the weakness in solving the MINLP problem directly. The first one is to transform the original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the original model using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy.