高校人力资源管理中的模糊综合评判

人力资源管理是高校管理的重要组成部分。本简要介绍了高校人力资源的状况，讨论了模糊综合评判理论在人力资源管理中的应用。     

人力资源绩效考核中的分析模型与意义

在大型企业对人力资源绩效考核时，被考核人员众多，如何更快，更好，更准确地做出评价是人事部门面临的一个重大问题。本利用人事考核中的原始信息，建立了两个模糊数学模型，不仅能用计算机处理得到考核等级，而且产出更有意义的分析结果，为领导，人事部门预测，决策提供重要依据。     

企业人力资源管理问题及解决对策

人力资源管理问题会直接影响企业的长远发展。因此,本文首先分析人力资源管理存在的对策,接着从员工管理、组织管理及福利管理三个方面进行论述,以期为人力资源管理提供借鉴和帮助。     

规划人口的动态灰色预测

本文运用灰色系统理论，提出了规划人口规模的灰色预测方法，并通过对某市人口的预测，验证了该方法的预测结果是较为符合实际的．     

核心人力资源对企业动态能力影响分析

企业动态能力是企业获取和发展可持续竞争优势的主要来源.面对当今激烈的市场竞争和不断变化的外部环境,如何培育企业的动态能力,已成为国内外学者所关注的热点问题.大量的研究表明,构建企业动态能力的关键要素在于企业的核心人力资源.因此,主要侧重于从核心入力资源管理的角度分析企业动态能力与核心人力资源的关系,包括核心人力资源如何影响动态能力的形成,以及在动态的环境下,核心人力资源应该怎样配合企业的总体战略提升企业竞争力提高企业的绩效.     

BP-GA混合优化策略在人力资源战略规划中的应用

采用混合优化策略训练神经网络,进而实现地区人力资源数据的时间序列预测.神经网络,尤其是应用反向传播(back propagation,简称BP)算法训练的神经网络,被广泛应用于预测中.但是BP神经网络训练速度慢、容易陷入局部极值.遗传算法(genetic algorithm,简称GA)具有很好的全局寻优性.因而提出将BP和GA结合起来的混合优化策略训练神经网络,来实现人力资源数据预测.与BP算法相比,数值计算结果表明预测精度高、速度快,为地区人力资源数据的时间序列预测研究提供了一条新的途径.     

人力资源管理在地勘单位管理中的重要性

目前,地勘单位仍旧属于是事业单位性质,对地勘单位来说,人力资源管理对其人才的保证有直接影响,其工作做得好与坏,直接关系地勘单位的发展,在地勘单位管理中有非常重要的作用。     

Markov链在人力资源供给预测中的应用

本文利用Ｍａｒｋｏｖ链建立模型,对组织各类人员的供给作出定量预测,为组织的人事部门或人力资源管理部门根据职位人员需求安排合适的人选提供决策的依据。文中介绍的模型不仅可以预测组织中各类人员的供给人数,而且可以预测组织中各类人员的分布。对于人力资源管理部门如何用人和用好人有一定的指导意义和应用价值     

基于预测精度的多目标组合预测优化模型研究

考虑了组合预测精度的数学期望和预测精度的标准差这两个指标 ,建立了多目标规划组合预测最优化模型 ,并给出其数学规划的解法 .最后进行实例分析 ,结果令人满意 .该模型能反映不同时间序列预测方法有效性     

GM(1,1)模型在粮食产量预测中的应用

本文根据灰色系统理论，利用安徽省１９８８—１９９５年粮食产量资料，运用ＧＭ（１，１）灰色预测模型方法，对“九五”期间安徽省粮食产量进行动态预测，为实现“九五”规划中的粮食产量提供科学的理论依据．     

Introducing cost-plus-loss analysis into a hierarchical forestry planning environment

Cost-plus-loss analysis of data for forestry planning has often been carried out for highly simplified planning situations. In this study, we suggest an advance in the cost-plus-loss methodology that aims to capture the hierarchical structure and iterative nature of planning by the large forest owner. The simulation system that is developed to simulate the planning process of the forest owner includes the tactical and operational levels of a continuous planning process. The system is characterized by annual re-planning of the tactical plan with a planning horizon of ten year and with the option to reassess data for selected stands before operational planning. Operational planning is done with a planning horizon of two years and the first year of the plan is considered to have been executed before moving the planning process one year forward. The annual cycle is repeated 10 times, simulating decisions made over a ten-year time horizon. The optimizing planning models of the system consider wood flow requirements, available harvest resources, seasonal variation of ground conditions and spatiality. The data used are evaluated according to standard procedures in cost-plus-loss analysis. Results from a test case indicate high decision losses when planning at both levels is based on the type of data prevalent in the stand databases of Swedish companies. The losses can be reduced substantially if higher-quality data are introduced before operational planning. In summary, the results indicate that the method makes it possible to analyze where in the planning process one needs better data and their value.     

Harvest planning in the Brazilian sugar cane industry via mixed integer programming

This work addresses harvest planning problems that arise in the production of sugar and alcohol from sugar cane in Brazil. The planning is performed for two planning horizons, tactical and operational planning, such that the total sugar content in the harvested cane is maximized. The tactical planning comprises the entire harvest season that averages seven months. The operational planning considers a horizon from seven to thirty days. Both problems are solved by mixed integer programming. The tactical planning is well handled. The model for the operational planning extends the one for the tactical planning and is presented in detail. Valid inequalities are introduced and three techniques are proposed to speed up finding quality solutions. These include pre-processing by grouping and filtering the distance matrix between fields, hot starting with construction heuristic solutions, and dividing and sequentially solving the resulting MIP program. Experiments are run over a set of real world and artificial instances. A case study illustrates the benefits of the proposed planning.     

Production planning with uncertainty in the quality of raw materials: a case in sawmills

Motivated by sawmill production planning, this paper investigates multi-period, multi-product (MPMP) production planning in a manufacturing environment with non-homogeneous raw materials, and consequently random process yields. A two-stage stochastic program with recourse is proposed to address the problem. The random yields are modelled as scenarios with stationary probability distributions during the planning horizon. The solution methodology is based on the sample average approximation (SAA) scheme. The stochastic sawmill production planning model is validated through the Monte Carlo simulation. The computational results for a real medium capacity sawmill highlight the significance of using the stochastic model as a viable tool for production planning instead of the mean-value deterministic model, which is a traditional production planning tool in many sawmills.     

Delay resistant line planning with a view towards passenger transfers

The line planning step, as part of the public transportation planning process, is an elementary problem. When generating public transportation systems in a conventional fashion, the line planning problem is one of the first to solve. Hence, subsequent problems rely on the solution of the line planning problem. Line planning has been studied from various perspectives and is understood very well. Still, the effect of this planning step on to the next ones has only received minor attention. In this paper, we study the effect of transfers on the delay resistance and propose a line planning model which provides a good basis for a delay resistant transportation system. To this end, the concept of preferable paths from the direct travelers line planning model is further extended. The model includes the routing of passengers in order to minimize passenger transfers. A column generation approach is shown to properly solve the proposed model. As such, this is the first line planning model which detailedly routes the passengers and is still tractable on realistically sized instances. Finally, it is shown that minimizing the passenger transfers at the line planning stage contributes to an increasing delay resistance in the public transportation system.     

A Crop Planning Problem with Fuzzy Random Profit Coefficients

The main purpose of this paper is to present a crop planning problem for agricultural management under uncertainty. It is significant that agricultural managers assign their limited farmlands to cultivation of which crops in a season. This planning is called the crop planning problem and influences their incomes for the season. Usually, the crop planning problem is formulated as a linear programming problem. But there are many uncertain factors in agricultural problems, so future profits for crops are not certain values. A linear programming model with constant profit coefficients may not reflect the environment of decision making properly. Therefore, we propose a model of crop planning with fuzzy profit coefficients, and an effective solution procedure for the model. Furthermore, we extend this fuzzy model, setting the profit coefficients as discrete randomized fuzzy numbers. We show concrete optimal solutions for each models.     

Operational Research and Corporate Planning in Railway Management

The current corporate planning process in Westrail is described and the roles played by operational research and mathematical modelling identified. They do not include forecasting the future. Instead of the "predict and prepare paradigm" a planning approach that tries to design the future has been adopted. The organisation is described in terms of how it has evolved, what it now is, and the key features of its environment. The three stages of planning are described-strategic, tactical and operational. These provide the means for management control of the railway. Environmental assessments linked to goal setting activities are incorporated within the planning process. The significance of operational research in planning, in particular mathematical modelling, is also described. Instead of finding problems to which existing techniques can be applied, O.R. is used to provide effective operational and planning systems as a means of assisting management to design the future for the organisation.     

Planning Under Uncertainty: 1. The Inflexibility of Methodologies

The inflexibility of plans and planning has been much criticised. While there are certain material factors in the structures of developed societies which promote such inflexibility, the criticism, if substantiated, clearly represents a major failing of planning methodology. In this paper the two most prominent planning methodologies—rational comprehensive and incrementalist—are reviewed, and found to incorporate inflexibility in distinctive ways into their procedures. Other approaches to planning which stress the importance of flexibility and attempt to make it operational are then critically surveyed; from these certain desirable characteristics of a methodology for flexible planning are distilled. Finally, the urgency of the problem is illustrated with examples both from theorists of planning and from the practice of planning in a wide range of application areas. (An alternative methodology is proposed in a companion paper.)     

Knowledge-based manpower planning

The capacity performance of knowledge-based systems differs essentially from the one of traditional systems. The result is that already today these systems are employed in a number of application areas, and that the intensity of research and development in this field is very high.In this paper the development of a knowledge-based manpower planning system (KBMS) is described. The two main components of this system are a knowledge-based system developed for management planning and a model designed for manpower planning in personnel leasing companies. This model was realized as a KBMPS by applying the knowledge-based system, which was developed for management planning.     

Information resource planning for a health-care system using an AHP-based goal programming method

This paper presents a goal programming (GP) model which aids in allocating a health-care system's information resources pertinent to strategic planning. The model is developed based on the data obtained from a major health-care system in the United States. The overall objective is to design and evaluate a model for effective information resource planning in a health-care system. The proposed model: (1) utilizes a GP approach to reflect the multiple, conflicting goals of the health-care system; (2) employs a GP solution process to reflect multi-dimensional aspects of the resource allocation planning; and (3) allows for some degree of flexibility of decision-making with respect to resource allocation. The goals are decomposed and prioritized with respect to the corresponding criteria using the analytic hierarchy process (AHP). The model result is derived and discussed. This GP model facilitates decision-making planning process and managerial policy in health-care information resources planning and similar planning settings.     

A stochastic programming approach for planning horizons of infinite horizon capacity planning problems

Planning horizon is a key issue in production planning. Different from previous approaches based on Markov Decision Processes, we study the planning horizon of capacity planning problems within the framework of stochastic programming. We first consider an infinite horizon stochastic capacity planning model involving a single resource, linear cost structure, and discrete distributions for general stochastic cost and demand data (non-Markovian and non-stationary). We give sufficient conditions for the existence of an optimal solution. Furthermore, we study the monotonicity property of the finite horizon approximation of the original problem. We show that, the optimal objective value and solution of the finite horizon approximation problem will converge to the optimal objective value and solution of the infinite horizon problem, when the time horizon goes to infinity. These convergence results, together with the integrality of decision variables, imply the existence of a planning horizon. We also develop a useful formula to calculate an upper bound on the planning horizon. Then by decomposition, we show the existence of a planning horizon for a class of very general stochastic capacity planning problems, which have complicated decision structure.