A Model for Planning Resource Requirements in Health Care Organizations

In this paper we present a general model and solution methodology for planning resource requirements (i.e., capacity) in health care organizations. To illustrate the general model, we consider two specific applications: a blood bank and a health maintenance organization (HMO). The blood bank capacity planning problem involves determining the number of donor beds required and determining the size of the nursing and support staff necessary. Capacity must be sufficient to handle the expected number of blood donors without causing excessive donor waiting times. Similar staff, equipment, and service level decisions arise in the HMO capacity planning problem. To determine resource requirements, we develop an optimization/queueing network model that minimizes capacity costs while controlling customer service by enforcing a set of performance constraints, such as setting an upper limit on the expected time a patient spends in the system. The queueing network model allows us to capture the stochastic behavior of health care systems and to measure customer service levels within the optimization framework.     

Turning Over Turnover

Abstract

Human service programs often have major problems ensuring that all direct-care staff positions are filled and keeping staff members after they are hired. Work schedules may have an effect on maintaining the longevity of staff. The purpose of this study was to evaluate the effects of a revised direct-care staff-scheduling system in community homes serving adults with developmental disabilities. The revised direct-care staffing system, which used shift-style schedules, was compared on a variety of measures to existing staffing systems and to a staffing model using married couples and adaptations of the Teaching-Family Model (called Family-Teaching homes). The revised staff-scheduling system was evaluated before and after its implementation and then compared to the Family-Teaching homes using direct-care employee measures of: (1) turnover; (2) position vacancies; (3) number of different employees paid within a pay period; (4) average hourly wages; (5) number of hours worked; (6) average cost for employees per person served; and (7) employee satisfaction. The various staffing approaches were also compared on measures of parent/legal guardian satisfaction and overall program quality. The revised staff-scheduling system was superior when compared to the previous staff-scheduling system on measures of turnover (43% less), vacancies (57% fewer), number of direct-care employees involved in the care of the persons served (13% fewer), and hourly wages (8% more). All of this was achieved without affecting staff-to-client ratios. While these changes were positive, the Family-Teaching homes were associated with significantly better direct-care staff stability and lower costs.     

A Multiperiod Audit Staff Planning Model Using Multiple Objectives: Development and Evaluation*

The audit staff planning problem, a specific type of manpower planning problem, has been modeled using goal programming and, more recently, multiple objective linear programming. Prior studies developed single-period models and did not go beyond the model building stage. This study develops a multiperiod audit staff planning model and evaluates the model using a test application involving actual decision makers (partners in public accounting firms). The multiperiod model includes seven objectives to be optimized: profit (to be maximized), late completion of work, work declined, staff augmentation, staff reduction, underutilization of the work force, and shortfall in meeting professional development targets (all to be minimized). Over a four-quarter planning horizon with one “busy season,” the model is subject to constraints with respect to the projected audit work load, ability to substitute personnel and to perform interim audit work, available staff hours (including overtime limitations), supervisory requirements, and professional development targets. Results of the test application showed that the model was capable of producing a range of values for each objective. The participants were exposed to much of that range when making their decisions. The results also showed that all objectives were important and that participants were consistent in choosing their preferred level of each objective over several runs of the model. These results and the reactions of the participants demonstrate that the model is usable by actual decision makers and has potential for a number of specific applications.     

A Simulation Metamodel for Response-Time Planning*

Field service management continues to be a major challenge for many service organizations as companies are required to provide more service with less resources. Especially in information intense environments, short response times for service calls are essential to avoid disruptions to a business office or production facility. Managers must regularly assess their manpower needs, and ensure that their allocation and operational decisions lead to the best service at the lowest cost. Xerox Corporation provides copiers, duplicators, and printing systems to an international market. Xerox operates a service network consisting of over 30,000 trained service personnel, each allocated to service regions by the service planning staff. Response-time planning involves many challenging problems: requirements planning at the national level, allocation planning at the district level, and operational planning at the team level. Customer service is critical and is commonly measured by response time—the time that elapses from when a service call is placed to when a service engineer begins service. This paper discusses how Xerox Corporation has used simulation models and metamodels to improve response-time planning and field service operations.     

Manpower planning and individual goals

Manpower planning is often considered as a subsystem within industrial organizations, containing a hierarchy of levels. In this article it is suggested that manpower planning has to reconcile the organization's profitability and employees' individual needs and goals. An attempt is made to analyse some recent manpower planning studies with special regard to the consideration of individual goals and their treatment. In conclusion, the authors propose an approach to considering individual needs and goals in manpower planning systems.     

Application of fuzzy goal programming technique to land use planning in agricultural system

This paper presents how fuzzy goal programming can be efficiently used for modelling and solving land-use planning problems in agricultural systems for optimal production of several seasonal crops in a planning year. In the model formulation of the problem, utilization of total cultivable land, supply of productive resources, aspiration levels of various production of crops as well as the total expected profit from the farm are fuzzily described. In the decision-making situation, minimization of the under-deviational variables of the membership goals with highest membership value (unity) as their achievement levels defined for the membership functions of the fuzzy goals of the problem on the basis of the priorities of importance of achieving the aspired levels of the fuzzy goals to the extent possible is considered. As a study region, the District Nadia, West Bengal, India is taken into account. To expound the potential use of the approach, the model solution is compared with the existing cropping plan of the District as well as a solution of the problem obtained by using the additive fuzzy goal programming model studied by Tiwari et al. (Fuzzy sets and systems 24(1987)27.) previously.     

Personnel policy and manpower planning in banking

Forces of rapid change and strong competition are now operating in the world. Banking with its rapidly changing technology must keep pace with these forces but it must at the same time provide secure and continuous employment for staff. Personnel policy now encompasses manpower planning which is defined as ‘A strategy to acquire, utilize, retain and develop people by formulating and implementing sound manpower policies agreed with employees and their unions’.     

Scheduling of Multi‐skilled Staff Across Multiple Locations

We address the problem of assigning airline customer service agents (CSAs) to tasks related to departing flights, such as selling tickets and collecting boarding cards, at an international terminal of a large airport. The airline specifies minimum and target levels of staff and required (or desired) types and levels of skills for each location in each time period. The assignment problem is complicated by staff heterogeneity, time required for moves between locations, and lunch and rest‐break requirements. We present a mixed‐integer formulation that considers both staffing shortages and skills mismatches and show that the problem is NP‐hard. We derive valid inequalities that tighten the bounds within a branch‐and‐cut procedure, enabling us to obtain near‐optimal solutions for problems of realistic size very quickly. We also present a generalization to simultaneously optimize shift starting times and task assignments, which can aid in longer term workforce planning. Finally, we utilize our procedure to obtain managerial insights regarding the benefits of flexibility derived from more highly skilled staff, allowing more frequent moves, and choices of shift starting times. We also demonstrate the benefits of our procedure vs. a heuristic that mimics what an experienced scheduler might choose.     

A quantitative model for manpower decision making

Today's society is continually influenced by various changes, requiring managers to plan for manpower requirements. An approach that can be used by firms to solve one of the most pressing of today's employment problems is examined. In effect, the problem becomes one of determining an optimal work force composition based on both sociological and skill components. The problem is viewed as one of establishing rank ordered priorities among multiple conflicting manpower objectives. This study discusses some of the internal and external constraints faced by the firm and suggests a particular technique, goal programming, to facilitate the manpower decision-making process. The technique is examined under two different circumstances to provide some indication of the model's flexibility.     

A simulation-based dynamic operator assignment strategy considering machine interference - a case study on integrated circuit chip moulding operations

A simulation-based dynamic operator assignment strategy is proposed considering machine interference in solving an integrated circuit moulding operation problem. The strategy iteratively solves a floating manpower operator-assignment problem between simulation-based diagnostic study and operator assignment decision. Given current-state information, it provides a means for system performance evaluation. It searches a satisfactory solution for both cycle time and service level performances by using floating manpower method. A structured experimental design is used to illustrate the effectiveness of the proposed methodology. Empirical results show promise for the proposed methodology in solving a practical application. Finally, the system implementation constraints from both hardware and management problems are addressed.     

A GOAL PROGRAMMING MODEL FOR OUTPUT PLANNING

This paper describes a goal programming procedure for determining satisfactory output plans for a work center. The situation being modeled is one in which work center inputs are known but vary significantly across time periods. Input levels are fixed relative to a given master production schedule, and output levels can be varied only within certain prescribed limits, at least in the short term. The similarity of the output planning problem to the more familiar aggregate planning problem is noted and discussed.     

A Behavioral Analysis of Goal Setting

Goal setting is a promising strategy for improving performance in organizational settings. This paper reviews goal setting from a behavior analytic perspective, discussing the function of goals and a number of key variables related to goal setting: goal difficulty, feedback on goal attainment and participation in goal setting. The advantages of utilizing behavior analytic methodology, such as objective measurement, clear specification of independent variables, intensive experimental designs and a behavioral theoretical framework are presented, along with suggestions for future research in the area.     

A multi-stage stochastic supply network design problem with financial decisions and risk management

In this paper, a multi-period supply chain network design problem is addressed. Several aspects of practical relevance are considered such as those related with the financial decisions that must be accounted for by a company managing a supply chain. The decisions to be made comprise the location of the facilities, the flow of commodities and the investments to make in alternative activities to those directly related with the supply chain design. Uncertainty is assumed for demand and interest rates, which is described by a set of scenarios. Therefore, for the entire planning horizon, a tree of scenarios is built. A target is set for the return on investment and the risk of falling below it is measured and accounted for. The service level is also measured and included in the objective function. The problem is formulated as a multi-stage stochastic mixed-integer linear programming problem. The goal is to maximize the total financial benefit. An alternative formulation which is based upon the paths in the scenario tree is also proposed. A methodology for measuring the value of the stochastic solution in this problem is discussed. Computational tests using randomly generated data are presented showing that the stochastic approach is worth considering in these types of problems.     

Manpower allocation in the criminal investigation process

A model describing the optimal allocation of manpower for the investigation department of the Israeli police force is described. A technique for estimating police officials preferences is given. It is believed that the approach and the methodology used can be applied in various manpower allocation problems and can serve as a good case study.     

A Stochastic Goal Program for Employee Scheduling*

Deterministic goal programs for employee scheduling decisions attempt to minimize expected operating costs by assigning the ideal number of employees to each feasible schedule. For each period in the planning horizon, managers must first determine the amount of labor that should be scheduled for duty. These requirements are often established with marginal analysis techniques, which use estimates for incremental labor costs and shortage expenses. Typically, each period in the planning horizon is evaluated as an independent epoch. An implicit assumption is that individual employees can be assigned to schedules with as little as a single period of work. If this assumption violates local work rules, the labor requirements parameters for the deterministic goal program may be suboptimal. As we show in this research, this well-known limitation can lead to costly staffing and scheduling errors. We propose an employee scheduling model that overcomes this limitation by integrating the labor requirements and scheduling decisions. Instead of a single, externally determined staffing goal for each period, the model uses a probability distribution for the quantity of labor required. The model is free to choose an appropriate staffing level for each period, eliminating the need for a separate goal-setting procedure. In most cases this results in better, less costly decisions. In addition, the proposed model easily accommodates both linear and nonlinear under- and overstaffing penalties. We use simple examples to demonstrate many of these advantages and to illustrate the key techniques necessary to implement our model. We also assess its performance in a study of more than 1,700 simulated stochastic employee scheduling problems.     

PLANNING FOR TRUCK FLEET SIZE IN THE PRESENCE OF A COMMON-CARRIER OPTION

This paper considers a class of network optimization problems in which certain directed arcs must be covered by a set of cycles. Our study was motivated by a distribution planning problem of a commercial firm that had to make deliveries over several origin-destination pairs (directed arcs) and that could service any demand arc by using a vehicle in its own fleet or by paying a common carrier. The problem is to determine an optimal fleet size and the resulting vehicle routes while satisfying maximum route-time restrictions. We formulate the problem, describe some approximate solution strategies, and discuss important implementation issues.     

Traffic‐Based Labor Planning in Retail Stores

Staffing decisions are crucial for retailers since staffing levels affect store performance and labor‐related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor‐planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data‐driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.     

Improved Linear Programming Models for Discriminant Analysis*

Discriminant analysis is an important tool for practical problem solving. Classical statistical applications have been joined recently by applications in the fields of management science and artificial intelligence. In a departure from the methodology of statistics, a series of proposals have appeared for capturing the goals of discriminant analysis in a collection of linear programming formulations. The evolution of these formulations has brought advances that have removed a number of initial shortcomings and deepened our understanding of how these models differ in essential ways from other familiar classes of LP formulations. We will demonstrate, however, that the full power of the LP discriminant analysis models has not been achieved, due to a previously undetected distortion that inhibits the quality of solutions generated. The purpose of this paper is to show how to eliminate this distortion and thereby increase the scope and flexibility of these models. We additionally show how these outcomes open the door to special model manipulations and simplifications, including the use of a successive goal method for establishing a series of conditional objectives to achieve improved discrimination.     

Probabilistic-Based Structural Specifications

The construction industry is both the largest civilian industry and the most unique in terms of fragmentation and its approach to controlling risk. Structural specifications establish nominal safety factors and design criteria for engineered structures such as buildings, bridges, dams, and power facilities. The goal is satisfactory strength capacity to safely and economically meet the demands of structural performance. Structures must resist the highly uncertain effects of combined gravity, earthquakes, wind, and snow loads supported by natural and human-made materials. The public's expectation is that structures should last a long time and have low lifetime risk of structural failure. Historically, specifications have contained safety factors which evolved from past experiences and provided adequate safety or low risk. Due to pressures of economy and the need to optimize structural performance, structural specifications have recently been developed and accepted by industry based on structural reliability and risk-assessment principles. This paper reviews developments of probabilistic applications in structural specifications including specification format, database, implementation examples, target risk levels, present research activities, and future goals for establishing optimal risk-design procedures.