Constraint programming for stochastic inventory systems under shortage cost

One of the most important policies adopted in inventory control is the replenishment cycle policy. Such a policy provides an effective means of damping planning instability and coping with demand uncertainty. In this paper we develop a constraint programming approach able to compute optimal replenishment cycle policy parameters under non-stationary stochastic demand, ordering, holding and shortage costs. We show how in our model it is possible to exploit the convexity of the cost-function during the search to dynamically compute bounds and perform cost-based filtering. Our computational experience show the effectiveness of our approach. Furthermore, we use the optimal solutions to analyze the quality of the solutions provided by an existing approximate mixed integer programming approach that exploits a piecewise linear approximation for the cost function.     

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.     

On the manpower allocation within matrix organization: A fuzzy linear programming approach

In this study, we provide a framework of fuzzy linear programming model for the function management division dealing with manpower allocation problem within matrix organization. The proposed model reveals how the function management division seeks a minimized cost and satisfies with the requirement of subordinate functional departments as far as possible under limited manpower and project budget. Numerical example illustrates that an adjustable parameter in the proposed model can be employed by the function management division to generate many different alternatives and grasp the degree of satisfaction of each subordinate functional department.     

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.     

Production planning via scenario modelling

Several Linear Programming (LP) and Mixed Integer Programming (MIP) models for the production and capacity planning problems with uncertainty in demand are proposed. In contrast to traditional mathematical programming approaches, we use scenarios to characterize the uncertainty in demand. Solutions are obtained for each scenario and then these individual scenario solutions are aggregated to yield a nonanticipative or implementable policy. Such an approach makes it possible to model nonstationarity in demand as well as a variety of recourse decision types. Two scenario-based models for formalizing implementable policies are presented. The first model is a LP model for multi-product, multi-period, single-level production planning to determine the production volume and product inventory for each period, such that the expected cost of holding inventory and lost demand is minimized. The second model is a MIP model for multi-product, multi-period, single-level production planning to help in sourcing decisions for raw materials supply. Although these formulations lead to very large scale mathematical programming problems, our computational experience with LP models for real-life instances is very encouraging.     

A Method of Estimating Craft Manpower Requirements at Industry Level

Accurate estimation of future craft manpower requirements is essential to an industry if shortage of manpower on the one hand and unemployment on the other are to be avoided. Such estimation should allow for the fact that there is a range of likely outcomes, should permit modification with the passage of time and should be objective.This article proposes a method, based on studies of the printing industry, of obtaining a probabilistic forecast of future manpower requirements by treating changes in individual technical and economic factors as random variables and combining them in a statistical model. The implications of this are then examined for various policies, using a cost utility model and calculations of the expected value of unemployment resulting from different levels of recruitment.     

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.     

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.     

The Organization of Interaction Between Oil Refineries

The paper deals with the appropriate form of interaction between two refineries with different demand patterns. This problem can be formulated as finding a decentralized solution of linear programming problems linked by buying and selling activities. The complete problem is first solved for central values of product demands and costs and revenues. The structure of the basis then determines the organization of the interaction in terms of which unit sets quantities and which prices, or whether centralized decisions should be made. If, for expected values of product demand and costs, the structure of the basis is the same then the related organization of trading can be used for day-to-day transactions. For a well-known oil refinery model it is found that, for fairly large demand variations, decentralized interaction is effective, but that the structure of the basis changes easily with crude price variations, and that simulations did not converge for these variations.     

Modelling recruitment training in mathematical human resource planning

This paper deals with mathematical human resource planning; more specifically, it suggests a new model for a manpower‐planning system. In general, we study a k‐classed hierarchical system where the workforce demand at each time period is satisfied through internal mobility and recruitment. The motivation for this work is based on various European Union incentives, which promote regional or local government assistance programs that could be exploited by firms not only for hiring and training newcomers, but also to improve the skills and knowledge of their existing personnel. In this respect, in our augmented mobility model we establish a new ‘training/standby’ class, which serves as a manpower inventory position for potential recruits. This class, which may very well be internal or external to the system, is incorporated into the framework of a non‐homogeneous Markov chain model. Furthermore, cost objectives are employed using the goal‐programming approach, under different operating assumptions, in order to minimize the operational cost in the presence of system's constraints and regulations. Copyright © 2002 John Wiley & Sons, Ltd.     

Attainable and Maintainable Structures in Markov Manpower Systems with Pressure in the Grades

Some of the problems of attaining and maintaining structures with given values of a quantity called ‘pressure’, which can be generated in the grades of a manpower system as a result of delays in expected promotions, have been considered in this paper. The research is focused on determining structures with given values of the total size and pressures and which can be attained and maintained by fixed recruitment policies.     

3.1. OR in banking

In this paper, we describe a deterministic multiperiod capacity expansion model in which a single facility serves the demand for many products. Potential applications for the model can be found in the capacity expansion planning of communication systems as well as in the production planning of heavy process industries. The model assumes that each capacity unit simultaneously serves a prespecified (though not necessarily integer) number of demand units of each product. Costs considered include capacity expansion costs, idle capacity holding costs, and capacity shortage costs. All cost functions are assumed to be nondecreasing and concave. Given the demand for each product over the planning horizon, the objective is to find the capacity expansion policy that minimizes the total cost incurred. We develop a dynamic programming algorithm that finds optimal policies. The required computational effort is a polynomial function of the number of products and the number of time periods. When the number of products equals one, the algorithm reduces to the well-known algorithm for the classical dynamic lot size problem.     

Dynamic server allocation for unstable queueing networks with flexible servers

This paper is concerned with the dynamic assignment of servers to tasks in queueing networks where demand may exceed the capacity for service. The objective is to maximize the system throughput. We use fluid limit analysis to show that several quantities of interest, namely the maximum possible throughput, the maximum throughput for a given arrival rate, the minimum arrival rate that will yield a desired feasible throughput, and the optimal allocations of servers to classes for a given arrival rate and desired throughput, can be computed by solving linear programming problems. We develop generalized round-robin policies for assigning servers to classes for a given arrival rate and desired throughput, and show that our policies achieve the desired throughput as long as this throughput is feasible for the arrival rate. We conclude with numerical examples that illustrate the points discussed and provide insights into the system behavior when the arrival rate deviates from the one the system is designed for.     

Optimal inventory management for a retail chain with diverse store demands

Item demands at individual retail stores in a chain often differ significantly, due to local economic conditions, cultural and demographic differences and variations in store format. Accounting for these variations appropriately in inventory management can significantly improve retailers’ profits. For example, it is shown that having greater differences across the mean store demands leads to a higher expected profit, for a given inventory and total mean demand. If more than one inventory shipment per season is possible, the analysis becomes dynamic by including updated demand forecasts for each store and re-optimizing store inventory policies in midseason. In this paper, we formulate a dynamic stochastic optimization model that determines the total order size and the optimal inventory allocation across nonidentical stores in each period. A generalized Bayesian inference model is used for demands that are partially correlated across the stores and time periods. We also derive a normal approximation for the excess inventory from the previous period, which allows the dynamic programming formulation to be easily solved. We analyze the tradeoffs between obtaining information and profitability, e.g., stocking more stores in period 1 provides more demand information for period 2, but does not necessarily lead to higher total profit. Numerical analyses compare the expected profits of alternative supply chain strategies, as well as the sensitivity to different distributions of demand across the stores. This leads to novel strategic insights that arise from adopting inventory policies that vary by store type.     

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.     

On the development of manpower number and recruitment in the civil service

We present a model for manpower planning which describes the dependence between stocks, flows and age distributions. An unbalanced age distribution results in the following conflict: either a recruitment is chosen which produces minimal deviations from a given stock. Then the unbalanced age distribution will be reproduced. Or a recruitment is selected which produces a balanced age distribution, but then oscillations of the stock will occur until a stationary age distribution will be reached. A mixture of these two recruitment policies is possible.

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Zusammenfassung Das vorliegende Personalplanungsmodell beschreibt den Zusammenhang zwischen Beständen, Personalflüssen und Altersstrukturen. Eine unausgewogene Altersverteilung des Personalbestandes führt zu einem Zielkonflikt: Entweder werden Einstellungen so vorgenommen, daß Abweichungen von einem stabilen Soll-Bestand minimal werden, dann aber bleibt die Altersverteilung unausgewogen. Oder Einstellungen werden so gewählt, daß eine stabile Altersstruktur erreicht wird, dann aber schwankt der Gesamtbestand, bis eine stabile Altersverteilung erreicht wird. Mischungen zwischen diesen Einstellungsstrategien sind möglich.

     

Stochastic models for air cargo terminal manpower supply planning in long‐term operations

In this research, based on two deterministic‐demand planning models, we established two long‐term stochastic‐demand planning models by incorporating the stochastic disturbances of manpower demands that occur in actual operations. The models are formulated as mixed integer linear programs that are solved using a mathematical programming solver. To compare the performance of the two stochastic‐demand and two deterministic‐demand planning models under the stochastic demands that occur in actual operations, we further develop a simulation‐based evaluation method. Finally, we perform numerical tests using real operating data from a Taiwan air cargo terminal. The preliminary results show that the stochastic models could be useful for planning air cargo terminal manpower supply. Copyright © 2008 John Wiley & Sons, Ltd.     

Multi-stage stochastic programming models for provisioning cloud computing resources

We focus on the resource provisioning problem of a cloud consumer from an Infrastructure-as-a-Service type of cloud. The cloud provider offers two deployment options, which can be mixed and matched as appropriate. Cloud instances may be reserved for a fixed time period in advance at a smaller usage cost per hour but require a full commitment and payment for the entire contract duration. In contrast, on-demand instances reflect a pay-as-you-go policy at a premium. The trade-off between these two options is rooted in the inherent uncertainty in demand and price and makes it attractive to complement a base reserved capacity with on-demand capacity to hedge against the spikes in demand. This paper provides several novel multi-stage stochastic programming formulations to enable a cloud consumer to handle the cloud resource provisioning problem at a tactical level. We first formulate the cloud resource provisioning problem as a risk-neutral multi-stage stochastic program, which serves as the base model for further modeling variants. In our second set of models, we also incorporate a certain concept of system reliability. In particular, chance constraints integrated into the base formulation require a minimum service level met from reserved capacity, provide more visibility into the future available capacity, and smooth out expensive on-demand usage by hedging against possible demand fluctuations. An extensive computational study demonstrates the value of the proposed models by discussing computational performance, gleaning practical managerial insights from the analysis of the solutions of the proposed models, and quantifying the value of the stochastic solutions.     

公务员招聘的优化模型

对“公务员招聘”问题进行了深入探讨,运用模糊数学和运筹学理论,先建立了招聘人员的模糊多属性综合评价模型,解决了用人单位择优录取公务员的问题;然后建立了0-1整数规划模型解决了人力资源的优化配置问题,从而给出了一种科学有效的公务员招聘的方法.     

An innovative approach for strategic capacity portfolio planning under uncertainties

This research studies multi-generation capacity portfolio planning problems under various uncertainty factors. These uncertainty factors include price uncertainties, demand fluctuation and uncertain product life cycle. The objective of this research is to develop an efficient algorithm that generates capacity portfolio policies robust to aforementioned uncertainties.