基于成熟度理论的集团企业人力资源管理质量等级评价理论和方法研究

对企业的人力资源管理质量进行评价,有助于促进其人力资源管理质量的持续提升。本文以集团企业为例,基于成熟度理论,研究提出了人力资源管理质量概念框架、成熟度等级,从组织、员工和文化三元视角构建了由5大方面、14个维度、66项指标构成的集团企业人力资源管理质量评价OEC^⑤-66模型,给出了人力资源管理质量评价计算方法、质量等级判断标准,以期为集团企业人力资源管理质量的评价和提升提供科学参考。     

Performance comparisons for maintained items

Most maintenance studies deal with the problem of maintenance-costs optimization, given the cost of the available maintenance actions and a cost objective function. The focus in this paper is on yet another relevant aspect namely the improvement in performance that results from a maintenance action in terms of performance criteria of interest. The evaluation of performance improvement requires comparisons of random quantities which is done by means of suitable probabilistic ordering notions. Different modelling situations are considered, and for each of them conditions are obtained on the life distributions of the present item, and the new one if of different type, that ensure performance improvement. Apart from providing a decision tool for maintenance application, the paper also contributes to clarifying relationships between (probabilistic) ordering notions and aging properties within the maintenance context.     

Ranking of Sports Teams via the AHP

This paper presents an application of the analytic hierarchy approach (AHP) to predict the ranking of the 16 soccer teams of the Israeli National League. Six criteria (attributes) were used for evaluating each team: the team's facility, the team's trainer, the players' level, the team's fans, the previous season's performance and the current performance. For each criterion a matrix of pairwise comparisons between teams was evaluated by a sports expert. The normalized eigenvector of each matrix scales the teams with regard to that criterion. The overall scaling is constructed by weighting the scales of all the criteria. The weight of the criteria are extracted from a judgemental matrix of pairwise comparisons between every two criteria. The normalized eigenvector of this matrix provides the criteria weights. Some comparisons to simpler ranking methods were made in order to validate the model.     

A sequential condition‐based repair/replacement policy with non‐periodic inspections for a system subject to continuous wear

This paper studies a condition‐based maintenance policy for a repairable system subject to a continuous‐state gradual deterioration monitored by sequential non‐periodic inspections. The system can be maintained using different maintenance operations (partial repair, as good as new replacement) with different effects (on the system state), costs and durations. A parametric decision framework (multi‐threshold policy) is proposed to choose sequentially the best maintenance actions and to schedule the future inspections, using the on‐line monitoring information on the system deterioration level gained from the current inspection. Taking advantage of the semi‐regenerative (or Markov renewal) properties of the maintained system state, we construct a stochastic model of the time behaviour of the maintained system at steady state. This stochastic model allows to evaluate several performance criteria for the maintenance policy such as the long‐run system availability and the long‐run expected maintenance cost. Numerical experiments illustrate the behaviour of the proposed condition‐based maintenance policy. Copyright © 2003 John Wiley & Sons, Ltd.     

Inventory control as a discrete system control for the fixed-order quantity system

This paper shows how to model a problem to find optimal number of replenishments in the fixed-order quantity system as a basic problem of optimal control of the discrete system. The decision environment is deterministic and the time horizon is finite. A discrete system consists of the law of dynamics, control domain and performance criterion. It is primarily a simulation model of the inventory dynamics, but the performance criterion enables various order strategies to be compared. The dynamics of state variables depends on the inflow and outflow rates. This paper explicitly defines flow regulators for the four patterns of the inventory: discrete inflow – continuous/discrete outflow and continuous inflow – continuous/discrete outflow. It has been discussed how to use suggested model for variants of the fixed-order quantity system as the scenarios of the model. To find the optimal process, the simulation-based optimization is used.     

Choosing realistic values of indifference,preference and veto thresholds for use with environmental criteria within ELECTRE

The ELECTRE III outranking model is particularly suited to aiding the choice between project alternatives on the basis of mainly environmental criteria. The model requires values of three criterion thresholds, the indifference threshold (q), the preference threshold (p) and the veto threshold (v). These allow the uncertainties inherent in the criteria valuations to be incorporated into the decision process. There is, at present, a high degree of subjectivity involved in determining these thresholds, which are expressed in terms of the error/uncertainty associated with the valuations of each of the criteria under scrutiny. If, however, the ELECTRE III outranking model is to be used within a formal environmental appraisal system, the thresholds which govern the outranking relationship of one project option over another must take account of the effect on human beings of the difference between any two criterion scores. The authors suggest a new method for applying the standard ELECTRE III model to decision-aid problems within the formal mechanism of environmental impact assessment. This involves a new, more comprehensive approach for specifying realistic limits for p, q and v, within the context of an environmental appraisal, where both criterion error/uncertainty and human sensitivity to differing levels of the criterion are taken into account. Threshold valuations for noise impacts from a highway project are used to illustrate the proposed method.     

Evaluating higher education teaching performance using combined analytic hierarchy process and data envelopment analysis

Evaluating higher education teaching performance is complex as it involves consideration of both objective and subjective criteria. The student evaluation of teaching (SET) is used to improve higher education quality. However, the traditional approaches to considering students’ responses to SET questionnaires for improving teaching quality have several shortcomings. This study proposes an integrated approach to higher education teaching evaluation that combines the analytical hierarchy process (AHP) and data envelopment analysis (DEA). The AHP allows consideration of the varying importance of each criterion of teaching performance, while DEA enables the comparison of tutors on teaching as perceived by students with a view to identifying the scope for improvement by each tutor. The proposed teaching evaluation method is illustrated using data from a higher education institution in Greece.     

Machine reliability and preventive maintenance planning for cellular manufacturing systems

The paper proposes a preventive maintenance (PM) planning model for the performance improvement of cellular manufacturing systems (CMS) in terms of machine reliability, and resource utilization. In a CMS, parts are processed by a group of interdependent machines, where machine reliability plays an important role in the performance improvement of the cell. Assuming that machine failure times follow a Weibull distribution, the proposed model determines a PM interval and a schedule for performing PM actions on each machine in the cell by minimizing the total maintenance cost and the overall probability of machine failures. The model uses a combined cost and reliability based approach, and optimizes maintenance costs by administering a group maintenance policy subject to a desirable machine reliability threshold. The study also proposes a CMS design model that integrates the above PM concepts into the design process. Illustrative examples are presented to demonstrate the applicability of the proposed approach.     

A structured solution framework for fuzzy minimum spanning tree problem and its variants under different criteria

This paper develops a unified and structured solution framework for the minimum spanning tree (MST) problem and its variants (e.g., constrained MST problem and inverse MST problem) on networks with fuzzy link weights. It is applicable to any additive decision criterion under fuzziness (e.g., expected value, value at risk, and conditional value at risk), for generalized cases that the link weights may be represented by arbitrary types of fuzzy variables. It also applies to the entropy criterion while the link weights are continuous fuzzy variables. Following the optimality conditions of the fuzzy MST under different decision criteria proved first in this paper, it is shown that the MST problem and its variants on a fuzzy network can be converted into equivalent deterministic counterparts on their corresponding crisp networks. Consequently, these problems can be effectively solved via their deterministic counterparts without fuzzy simulation, and meanwhile, the performance of the trees under a specified criterion is precisely measured. The accuracy and efficiency are both significantly improved compared with other fuzzy simulation-based approaches. Numerical examples illustrate the superiority of the proposed solution framework. Furthermore, some new theoretical conclusions on the MST problem under fuzziness are also presented.

     

Bi-criterion single-machine scheduling and due-window assignment with common flow allowances and resource-dependent processing times

This paper addresses single-machine scheduling and due-window assignment with common flow allowances and resource-dependent processing times. Due-window assignment with common flow allowances means that each job has a job-dependent due window, the start time and finish time of which are equal to its actual processing time plus individual job-independent parameters shared by all the jobs, respectively. The processing time of each job can be controlled by extra resource allocation as a linear function of the amount of a common continuously divisible resource allocated to the job. Two criteria are considered, where one criterion is an integrated cost consisting of job earliness, weighted number of tardy jobs, and due-window assignment cost, while the other criterion is the resource consumption cost. Four different models are considered for treating the two criteria. It is shown that the problem under the model where the two criteria are integrated into a single criterion is polynomially solvable, while the problems under the other three models are all NP-hard and an optimal solution procedure is developed for them. Two polynomially solvable cases are also identified and investigated. Finally, numerical studies with randomly generated instances are conducted to assess the performance of the proposed algorithms.     

Goal Programming in Preference Decomposition

In choosing the best alternative with respect to multiple criteria, one approach is to estimate the criterion weights that influence the preferences of all the alternatives presented. When decision makers are able to make preference judgements based on the alternative as a whole, preference decomposition methods attempt to determine part-worths which represent the contribution of the criterion levels to the overall preference values. For each alternative, the sum of the part-worths estimates its overall preference value. In this work, we use linear goal programming to determine the part-worths of all the criterion levels. Simulation experiments are conducted to compare the performances of linear goal programming and ordinary least squares regression in preference decomposition and to examine the effectiveness of including constraints on the parameters. Our simulated results suggest that the linear goal programming model with constrained parameters has better predictive power.     

Solving a tactical operating room planning problem by a column-generation-based heuristic procedure with four criteria

In some hospitals, an “open scheduling” strategy is applied to solve the operating room planning problem; i.e., surgeons can choose any workday for his surgical cases, and the staffing of anesthetists and nurses is adjusted to maximize the efficiency of operating room utilization. In this paper, we aim at obtaining an efficient operating program for an operating theatre with several multifunctional operating rooms by using this “open scheduling” strategy. First, a mathematical model is constructed to assign surgical cases to operating rooms within one week. This model complies with the availability of operating rooms and surgeons, and its objective is not only to maximize utilization of operating rooms, but to minimize their overtime cost. Then a column-generation-based heuristic (CGBH) procedure is proposed, where four different criteria are compared with each other so as to find a solution with the best performance. In addition, the best approximate solution, obtained by this CGBH procedure after running all the criteria proposed, is compared with the lower bound obtained by an explicit column generation (CG) procedure, LP, to evaluate the distance between the approximate solution obtained and the optimum one. Although no criterion, according to the experimental results, is found superior to all other three in both robustness and quality of the solution obtained, it is found that the best solution obtained among those four criteria is often very close to LP, which means that the proposed algorithm can obtain a near optimal solution. In one word, the CGBH procedure proposed in this paper can obtain an efficient assignment of the surgical cases if the other resources (anesthesia and nursing staff, equipment, beds in the recovery room and etc.) are well organized.     

Efficiency and equity in private and public education: A nonparametric comparison

We present a nonparametric approach for (1) efficiency and (2) equity evaluation in education. Firstly, we use a nonparametric (Data Envelopment Analysis) model that is specially tailored to assess educational efficiency at the pupil level. The model accounts for the fact that typically minimal prior structure is available for the behavior (objectives and feasibility set) under evaluation. It allows for uncertainty in the data, while it corrects for exogenous ‘environmental’ characteristics that are specific to each pupil. Secondly, we propose two multidimensional stochastic dominance criteria as naturally complementary aggregation criteria for comparing the performance of different school types (private and public schools). While the first criterion only accounts for efficiency, the second criterion also takes equity into consideration. The model is applied for comparing private (but publicly funded) and public primary schools in Flanders. Our application finds that no school type robustly dominates another type when controlling for the school environment and taking equity into account. More generally, it demonstrates the usefulness of our nonparametric approach, which includes environmental and equity considerations, for obtaining ‘fair’ performance comparisons in the public sector context.     

A programming method for determining which Paris metro stations should be renovated

Each year, Régie Autonome des Transport Parisiens RATP renovates a certain number of metro stations. The method used to determine which ones should be chosen for renovation, was reconsidered two years ago. A programming system, using updated data, now ranks by priority each year all the stations renovated. The ranking procedure uses Electre III. Each station is evaluated using seven criteria arising from the objectives and constraints of either the passengers or the RATP. For each criterion, indifference and preference thresholds are determined after establishing the relative importance of each criterion. A joint research contract was drawn up by Laboratoire d'Analyse et Modelisstion de Systémes pour l'Aide à la Décision (LANSADE, University of Paris) and, RATP for the implementation of the method. This study led to two critical developments in multicriteria methods:

• 1.— A method for obtaining the necessary information from RATP staff to quantify the relative importance of each criterion was rested; it led to the determination of the variation ranges of criteria ‘weights’.
• 2.— A method to appraise the impact of imperfect information — which can take several forms — on the ranking was developed; it was applied to 32 sets of values.

     

Prioritization of medical equipment for maintenance decisions

Clinical engineering departments in hospitals are responsible for establishing and regulating a Medical Equipment Management Program to ensure that medical devices are safe and reliable. In order to mitigate functional failures, significant and critical devices should be identified and prioritized. In this paper, we present a multi-criteria decision-making model to prioritize medical devices according to their criticality. Devices with lower criticality scores can be assigned a lower priority in a maintenance management program. However, those with higher scores should be investigated in detail to find the reasons for their higher criticality, and appropriate actions, such as ‘preventive maintenance’, ‘user training’, ‘redesigning the device’, etc, should be taken. In this paper,we also describe how individual score values obtained for each criterion can be used to establish guidelines for appropriate maintenance strategies for different classes of devices. The information of 26 different medical devices is extracted from a hospital's maintenance management system to illustrate an application of the proposed model.     

Introduction to a new weighting method — Hierarchy consistency analysis

This paper proposes a new methodology — Hierarchy Consistency Analysis (HCA). The approach to this methodology was developed owing to the fact that in the analysis of large-scale problems, a different viewpoint or specific points emphasis will result in different hierarchy structures. On the basis of different structures being created, the methodology is designed to combine these different structures, and estimate their compromised criterion weights so that all different hierarchy structures will have the same aggregated index. The HCA methodology will thus provide us with a comprehensive framework for solving hierarchy evaluation problems. Its merits include, amongst others, providing a simpler method for calculating the weight of criteria, providing a consistency index for monitoring DM judgment consistency, and delivering more types of hierarchy structure schemata to aid the decision maker in understanding and auditing the performance status of systems.     

灰色Verhulst模型的改进及其应用

针对灰色Verhulst模型的不足,讨论了灰色Verhulst模型的参数优化问题.首先,利用最小二乘原理给出了一种初值优化的改进模型.其次,在平均相对误差最小准则下,将Verhulst模型的参数优化转化为线性规划问题,然后利用粒子群优化算法估计Verhulst模型中的参数,得到另外一种改进模型.最后,给出了一个仿真实例,结果表明灰色Verhulst模型的改进方法是可行的和有效的,而且具有较高的拟合和预测精度.     

A Minimum-Cost Analysts Assignment Model for Asset Evaluation of Insolvent Saving and Loan Institutions

Audit staff planning has been a challenging problem for accounting, auditing and real estate firms. This paper presents a mathematical model and a solution methodology for determining the minimum-cost analysts assignment, when analysts should travel from geographically dispersed locations to evaluate assets of an insolvent Saving and Loan institution. Computational experiments with the solution algorithm on 27 randomly generated projects show (a) that the solution methodology efficiently generates an optimal solution, and (b) provides the decision maker with alternative next best plans through ex post sensitivity analysis. Although specific, variations of the model and algorithm presented here can be applied to a variety of audit staff assignment problems in accounting and real estate firms.     

Efficiency Bounds for Product Designs in Linear Models

We provide lower efficiency bounds for the best product design for an additive multifactor linear model. The A-optimality criterion is used to demonstrate that out bounds are better than the conventional bounds. Applications to other criteria, such as IMSE (integrated mean squared error) criterion are also indicated. In all the cases, the best product design appears to perform better when there are more levels in each factor but decreases when more factors are included. Explicit efficiency formulas for non-additive models are also constructed.     

An investigation of model selection criteria for neural network time series forecasting

Artificial neural networks (ANNs) have received more and more attention in time series forecasting in recent years. One major disadvantage of neural networks is that there is no formal systematic model building approach. In this paper, we expose problems of the commonly used information-based in-sample model selection criteria in selecting neural networks for financial time series forecasting. Specifically, Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) as well as several extensions have been examined through three real time series of Standard and Poor’s 500 index (S&P 500 index), exchange rate, and interest rate. In addition, the relationship between in-sample model fitting and out-of-sample forecasting performance with commonly used performance measures is also studied. Results indicate that the in-sample model selection criteria we investigated are not able to provide a reliable guide to out-of-sample performance and there is no apparent connection between in-sample model fit and out-of-sample forecasting performance.