Recruitment Effects on Manpower Structures

Recruitment is one of the dynamics of manpower systems that can usually be most effectively controlled, always assuming that there is at any time an adequate supply of recruits to a system. In this situation, recruitment can be fixed to meet immediate demands, or it can be part of long-term planning programmes designed perhaps to alleviate a skewness in the length of service profile without reducing the strength of the system greatly. In general, recruitment levels will necessarily be connected with wastage and promotion in a system as well as with the desired growth of the system. The process of determining manpower-planning policies, hereunder recruitment levels, is open to a variety of options with regard to the underlying assumptions that are made: observed experience can be assumed to continue; promotion policies can be adjusted and the consequences estimated; recruitment levels can be allowed to meet immediate demands but with the restriction of some maximum level; or recruitment levels are pre-fixed on the basis of some perhaps even arbitrary management desires. Each of these options and each accompanying recruitment policy will affect the internal structure of the system, with regard to both rank and length of service profiles. This paper employs established projection and promotion models for hierarchical manpower systems to consider recruitment policies and their effects on internal structures. Various policy models are outlined and results presented for a particular application.     

A stochastic manpower planning model under varying class sizes

Solution related to different types of manpower planning problems arising in different industries and organizations are very much helpful for proper planning and implementation of different objectives. Previously those type of problems are mostly solved under the deterministic set up. Gradually several scientists have developed different types of stochastic models appropriate for solving such types of problems. The present study is an attempt to develop a stochastic manpower planning model under the set up where the classes are of varying sizes and promotion occurs only on the basis of seniority. The work of second author was supported by a research fellowship from Council of Scientific and Industrial Research (Sanction No. 9/28(611)/2003-EMR-I), India.     

On the Asymptotic Behaviour of Age Distributions in Manpower Systems

The literature on supply models for manpower planning shows that an important consideration is the size of the discrepancy between the age or length of service distribution of the population and the age distribution which would be reached if present policies were continued indefinitely. In the present paper we study the asymptotic behaviour of the age distribution in any manpower system. An application to a decision problem in a university system is given.     

On the equilibrium personnel structure in the presence of vertical and horizontal mobility via multivariate Markov chains

The present paper employs the Multivariate Homogeneous Markov System (MHMS) in the context of Markov manpower planning modelling. The system is regulated by an embedded multivariate Markov process that allows us to distinguish employees’ mobility patterns that take place either within or among the existing divisions (departments) of an organization. The motivation behind this step arises from the generalization of univariate Markov manpower planning models in which the organization is considered a single (probably hierarchical) group and from the fact that departmental mobility is actually common in most realistic establishments. The first part of the paper presents the functional relations of the MHMS governing intra/inter-departmental transitions. Using these functional forms, we proceed by studying the system’s equilibrium behaviour. This asymptotic analysis reveals the inherent tendencies of the system to reach the limiting structures of specific forms and properties under conditions imposed in the long run.     

A model for promotion rate control in hierarchical manpower systems

In optimization models of hierarchical manpower systems, thenumbers promoted from each of the grades in a time period arenormally considered as decision variables. As a result, promotionrates, defined in terms of the proportions of staff promoted,can vary substantially from period to period in these models.Policies of this type may be unacceptable in practice becauseof their adverse impact on staff morale. In this paper, a mixedinteger programming (MIP) manpower planning model is developedfor determining minimum-cost manpower policies in which promotionrates remain stable while satisfying specified manpower requirementsover the planning period. In this MIP model, promotion ratesare considered as decision variables by using binary variables,and the model is solved by using an iterative procedure. Theuse of the approach is illustrated with representative datafor a military system.     

Approximations for interactive Markov chains in discrete and continuous time

A class of models called interactive Markov chains is studied in both discrete and continuous time. These models were introduced by Conlisk and serve as a rich class for sociological modeling, because they allow for interactions among individuals. In discrete time, it is proved that the Markovian processes converge to a deterministic process almost surely as the population size becomes infinite. More importantly, the normalized process is shown to be asymptotically normal with specified mean vector and covariance matrix. In continuous time, the chain is shown to converge weakly to a diffusion process with specified drift and scale terms. The distributional results will allow for the construction of a likelihood function from interactive Markov chain data, so these results will be important for questions of statistical inference. An example from manpower planning is given which indicates the use of this theory in constructing and evaluating control policies for certain social systems.     

Corporate manpower planning

In this paper the necessity of corporate manpower planning is discussed and the growing interest in it within many organizations. In particular medium and long term forecasting in rather large and structured organizations are explained and the need for tools to get a survey of the evolution of their personnel strength. These tools should help management and staff-members of a planning or personnel department to design alternative policies concerning promotions, recruitments, etc.We will present a planning method which satisfies these requirements. The interactive manpower planning system FORMASY presents forecasts concerning the evolution of the personnel strength and makes it possible to assess the impacts of alternative policies. This planning system is based on a Markov model and is being used on a general purpose computer in several Dutch organizations, both industrial and governmental.FORMASY has proved its value for practical manpower planning which will be explained by means of a case-study with the Royal Dutch Airforce.     

Tactical Manpower Planning via Programming Under Uncertainty

This paper presents models for different types of manpower pooling policies. A multi-grade manpower system with mutually exclusive skills is considered. The work load imposed is a random variable characterized by the known joint distribution of the number of jobs to be performed and of the time to do a job. The basic models are developed as a tool for manpower planning in the jobbing workshops of an oil company. The resulting "two-stage programmes under uncertainty" are shown to reduce to mixed-integer linear programmes. The models are then generalized to permit their use in a larger class of manpower planning problems.     

Optimal recruitment strategies in a multi-level manpower planning model

Markov manpower planning models have extensively been analysed in the past in order to find an optimal personnel strategy for which the stocks of the manpower system evolve towards desirable ones. So far, those models do not take into account interactions among different organizational decision levels. In this paper, a multi-level manpower planning model is presented that considers, besides the desirable stock vector at overall level, proposals for the departmental stocks from lower organizational levels. Attainability of the stock vectors at departmental level is examined under control by recruitment and interdepartmental transitions. A multi-level optimization algorithm is presented to determine an optimal recruitment strategy resulting in attainable and acceptable stocks that are a compromise between the proposal from the top and the proposals from the departments.     

Algorithms for improving efficiency of discrete Markov chains

Sampling from an intractable probability distribution is a common and important problem in scientific computing. A popular approach to solve this problem is to construct a Markov chain which converges to the desired probability distribution, and run this Markov chain to obtain an approximate sample. In this paper, we provide two methods to improve the performance of a given discrete reversible Markov chain. These methods require the knowledge of the stationary distribution only up to a normalizing constant. Each of these methods produces a reversible Markov chain which has the same stationary distribution as the original chain, and dominates the original chain in the ordering introduced by Peskun [11]. We illustrate these methods on two Markov chains, one connected to hidden Markov models and one connected to card shuffling. We also prove a result which shows that the Metropolis-Hastings algorithm preserves the Peskun ordering for Markov transition matrices.     

Balancing attainability,desirability and promotion steadiness in manpower planning systems

A Markov manpower planning model with fixed internal transition probabilities, enables assessing the feasibility to attain the most desirable personnel structure. In case the desirable personnel structure is not attainable under control by recruitment, the internal personnel flows can be modified while not disrupting the career progression expectations. This paper introduces the promotion steadiness degree to quantify the personnel policy deviation from the career progression expectations. As a result, this paper focuses on a model that balances three criteria, that is, the desirability degree, the attainability degree and the promotion steadiness degree, formulated by fuzzy membership functions. A new set of instances is introduced, and the algorithms are evidenced in a set of experiments.     

A stochastic interactive model for the diffusion of information

Two important generalizations of information diffusion models are the presence of stochastic effects and the possibility of arbitrary patterns of influence among individuals. A Markov random fields model includes both of these features. Under very weak assumptions, there is a unique equilibrium distribution of information patterns for given stochastic (local) interactions among a finite population. This has implications for policies to influence the transmission of information. The dynamic behavior of a special and simple case of the model tends to approximate the standard (logistic) diffusion curve. For an infinite population, uniqueness of equilibrium distributions may fail; some sufficient conditions to ensure uniqueness are given.     

A Manpower Planning Study

The paper describes a technique which has been applied in practice to predict the movements of the staff of a large undertaking over the next 20 years. The exercise shows the future promotion pattern and the recruitment required to fill the posts which become vacant as a result of promotion or wastage.By feeding in alternative assumptions about the future demand for staff in each department, corresponding to different levels of business activity, it is possible to see in advance which situations are likely to give rise to the greatest staff problems.It is thought that the same basic technique could be used for manpower planning in other firms.     

Evaluation of staffing policies in Markov manpower systems and their extension to organizations with outsource personnel

This paper takes up the problem of the evaluation of manpower policies followed in an organization, and examines it in relation to the career growth facilitation afforded by the system to its members. The method proposes and constructs a quantitative measure of effectiveness for manpower policies based on the career growth facilitation afforded to the members, which can then be used for comparative evaluation of different policies. The models are then extended to organizations which outsource work through outsource manpower, and which seek to control the blend of internal and outsource manpower. The models and analyses developed herein will be of relevance to all manpower systems, and in particular, to organizations which outsource work, and those that attempt to achieve desired blends of the outsource staff with their own.     

Control aspects in proportionality Markov manpower systems

In this paper, the control aspects of a hierarchical organization under the influence of “proportionality” policies are analyzed. Proportionality policies are those that restrict the recruitment to every level of the hierarchy (except the bottom most level or base level) to be in strict proportion to the promotions into that level. Both long term and short term control analysis have been discussed. In long term control the specific roles of the parameters of the system with regard to control of the shape and size of the system have been analyzed and yield suitable control strategies. In short term control, the attainability of a target or goal structure within a specific time from a given initial structure has been analyzed and yields the required recruitment strategies. The theoretical analyses have been illustrated with computational examples and also with real world data.The control of such proportionality systems is then compared with that of the general systems (which do not follow such policies) with some significant conclusions. The control relations of such proportionality systems are found to be simpler and more practically feasible than those of general Markov systems, which do not have such restrictions. Such proportionality systems thus not only retain and match the flexibility of general Markov systems but also have the added advantage of simpler and more practically feasible controls. The proportionality policies hence act as an alternative and more practicably feasible means of control.     

Nonatomic total rewards Markov decision processes with multiple criteria

We consider a Markov decision process with an uncountable state space for which the vector performance functional has the form of expected total rewards. Under the single condition that initial distribution and transition probabilities are nonatomic, we prove that the performance space coincides with that generated by nonrandomized Markov policies. We also provide conditions for the existence of optimal policies when the goal is to maximize one component of the performance vector subject to inequality constraints on other components. We illustrate our results with examples of production and financial problems.     

A revenue management approach to address a truck rental problem

This paper describes an application of revenue management techniques and policies in the field of logistics and distribution. In particular, the problem of transportation operators, who offer products for hire, is considered. A product is a truck of a given capacity, which can be rented for one or several time periods, throughout a multi-period planning horizon. The logistic operator can satisfy the demand of a given product with trucks with a capacity greater than that initially required, that is an ‘upgrade’ can take place. In this context, the logistic operator has to decide whether to accept or reject a request and which type of truck should be used to address it. For this purpose, a dynamic programming (DP) formulation of the problem under consideration is devised. The ‘course of dimensionality’ leads to the necessity of introducing different mathematical programming models to represent the problem. The mathematical models we presented are an extension of the well-known approximations for the DP of traditional network capacity management analysis. Based on these models and exploiting revenue management concepts, primal and dual acceptance policies are developed and compared in a computational study.     

Performance Monitoring and Competence Assessment in Health Services

Health and health service monitoring is among the most promising research area today and the world work towards efficient and cost effective health care. This paper deals with monitoring health service performance using more than one performance outcome variable (multi-attribute processes), which is common in most health services. Although monitoring whether a health service changes or improves over time is important this is well covered in the current literature. Therefore this paper focuses on comparing similar health services in terms of their performance. The proposed procedure is based on an appropriate control chart. The paper deals with firstly the case when no risk adjustment is required because the health services being compared treat the same patient case-mix which does not vary over time. Secondly it deals with comparing health services where risk adjustment is required because the patient case-mix they service do differ because they service either very different geographical locations or service very different demographics of the same population. The technology developed in this paper could be used for example to assess and compare health practitioners’ competence over time, i.e. to decide if two doctors are equivalent in terms of their outcome performances. The waiting time random variable associated with the run length distribution of the control charts (as well as to competence testing) is studied using a Markov Chain embedding technique. Numerical results are provided that exhibit the value of the proposed procedures.     

A Markov chain Monte Carlo Expectation Maximization Algorithm for Statistical Analysis of DNA Sequence Evolution with Neighbor-Dependent Substitution Rates

The evolution of DNA sequences can be described by discrete state continuous time Markov processes on a phylogenetic tree. We consider neighbor-dependent evolutionary models where the instantaneous rate of substitution at a site depends on the states of the neighboring sites. Neighbor-dependent substitution models are analytically intractable and must be analyzed using either approximate or simulation-based methods. We describe statistical inference of neighbor-dependent models using a Markov chain Monte Carlo expectation maximization (MCMC-EM) algorithm. In the MCMC-EM algorithm, the high-dimensional integrals required in the EM algorithm are estimated using MCMC sampling. The MCMC sampler requires simulation of sample paths from a continuous time Markov process, conditional on the beginning and ending states and the paths of the neighboring sites. An exact path sampling algorithm is developed for this purpose.