Zero-base budgeting—Dealing with conflicting objectives

While Zero-Base Budgeting (ZBB) has received considerable attention in the public sector, virtually all the studies dealing with the system have neglected or given cursory attention to the problem of multiple conflicting objectives. This paper presents a goal programming approach as a systematic means to develop the ZBB process for multiple objectives. This approach allows the administrators to more realistically portray the decision environment as well as their judgment in their budgetary planning models, thus making the budgeting an effective and pragmatic way to implement the planning and decision making process. This study demonstrates a goal programming based ZBB system in the public sector based on real-world data.     

报酬方案和资源分配对预算松弛影响的实验研究:基于调整型资源分配方式的一个检验

本文基于管理控制理论,探索如何发挥预算的行为驱动及组织学习作用。运用实验方法研究了不同报酬方案下,对前期预算松弛调整的竞争性资源分配与控制方式对预算松弛的影响。研究结果显示,一方面通过模拟市场竞争诱导下属提交真实的预算目标,优化资源分配;另一方面对预算参与人的松弛行为实施有效控制,二者的交互作用可以有效降低预算松弛,体现了预算控制的双重优化作用。     

A GOAL PROGRAMMING MODEL FOR ALLOCATING HUMAN RESOURCES FOR THE GOOD LABORATORY PRACTICE REGULATIONS

The Food and Drug Administration's final Good Laboratory Practice regulation will have a far-reaching effect on testing laboratories that are conducting nonclinical laboratory safety tests. This paper develops a goal programming decision model suitable for setting objectives, meeting budgetary and operational constraints, planning personnel utilization, and evaluating different proposals for allocating laboratory personnel to implement a program.     

A nested-compliance table policy for emergency medical service systems under relocation

The goal of Emergency Medical Service (EMS) systems is to provide rapid response to emergency calls in order to save lives. This paper proposes a relocation strategy to improve the performance of EMS systems. In practice, EMS systems often use a compliance table to relocate ambulances. A compliance table specifies ambulance base stations as a function of the state of the system. We consider a nested-compliance table, which restricts the number of relocations that can occur simultaneously. We formulate the nested-compliance table model as an integer programming model in order to maximize expected coverage. We determine an optimal nested-compliance table policy using steady state probabilities of a Markov chain model with relocation as input parameters. These parameter approximations are independent of the exact compliance table used. We assume that there is a single type of medical unit, single call priority, and no patient queue. We validate the model by applying the nested-compliance table policies in a simulated system using real-world data. The numerical results show the benefit of our model over a static policy based on the adjusted maximum expected covering location problem (AMEXCLP).     

PARTICIPATION IN BUDGETARY DECISION MAKING,TASK DIFFICULTY,LOCUS OF CONTROL,AND EMPLOYEE BEHAVIOR: AN EMPIRICAL STUDY*

This study investigates the relationship between participation in budgetary decision making and attitude (toward job and employer), taking into account the effects of task difficulty and locus of control on participation and attitude. Managers from two companies participated in the study. Attitude was found to be related positively to participation and negatively to task difficulty. Implications of the results are discusses.     

OSHA''s Permissible Exposure Limits: Regulatory Compliance Versus Health Risk

Workplace exposures to airborne chemicals are regulated in the U.S. by the Occupational Safety and Health Administration (OSHA) via the promulgation of permissible exposure limits (PELs). These limits, usually defined as eight-hour time-weighted average values, are enforced as concentrations never to be exceeded. In the case of chronic or delayed toxicants, the PEL is determined from epidemiological evidence and/or quantitative risk assessments based on long-term mean exposures or, equivalently, cumulative lifetime exposures. A statistical model was used to investigate the relation between the compliance strategy, the PEL as a limit never to be exceeded, and the health risk as measured by the probability that an individual's long-term mean exposure concentration is above the PEL. The model incorporates within-worker and between-worker variability in exposure, and assumes the relevant distributions to be log-normal. When data are inadequate to estimate the parameters of the full model, as it is in compliance inspections, it is argued that the probability of a random measurement being above the PEL must be regarded as a lower bound on the probability that a randomly selected worker's long-term mean exposure concentration will exceed the PEL. It is concluded that OSHA's compliance strategy is a reasonable, as well as a practical, means of limiting health risk for chronic or delayed toxicants.     

Will the Occupational Safety and Health Administration's Proposed Standards for Occupational Exposure to Respirable Crystalline Silica Reduce Workplace Risk?

The Occupational Safety and Health Administration (OSHA) is developing regulations to amend existing standards for occupational exposure to respirable crystalline silica by establishing a new permissible exposure limit as well as a series of ancillary provisions for controlling exposure. This article briefly reviews OSHA's proposed regulatory approach and the statutory authority on which it is based. It then evaluates OSHA's preliminary determination of significant risk and its analysis of the risk reduction achievable by its proposed controls. It recognizes that OSHA faces multiple challenges in devising a regulatory approach that reduces exposures and health risks and meets its statutory goal. However, the greatest challenge to reducing risks associated with silica exposure is not the lack of incentives (for either employers or employees) but rather lack of information, particularly information on the relative toxicity of different forms of silica. The article finds that OSHA's proposed rule would contribute little in the way of new information, particularly since it is largely based on information that is at least a decade old—a significant deficiency, given the rapidly changing conditions observed over the last 45 years. The article concludes with recommendations for alternative approaches that would be more likely to generate information needed to improve worker health outcomes.     

The falling tide algorithm: A new multi-objective approach for complex workforce scheduling

We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective function, we can easily obtain a coarse solution where only the system constraints (i.e. hard constraints) are satisfied and an ideal objective-value vector where each single goal (i.e. each soft constraint) reaches its optimal value. The coarse solution is generally unusable in practise, but it can act as an initial point for the subsequent meta-heuristic search to speed up the convergence. Also, the ideal objective-value vector is, of course, usually unachievable, but it can help a multi-criteria search method (i.e. compromise programming) to evaluate the fitness of obtained solutions more efficiently. By incorporating three distance metrics with changing weight vectors, we propose a new time-predefined meta-heuristic approach, which we call the falling tide algorithm, and apply it under a multi-objective framework to find various compromise solutions. By this approach, not only can we achieve a trade off between the computational time and the solution quality, but also we can achieve a trade off between the conflicting objectives to enable better decision-making.     

OPTIMIZING ORGANIZATIONAL GOALS IN ASSIGNING FACULTY TEACHING SCHEDULES

The administration of higher education, as with other administrative environments, has become increasingly complex. This paper presents a mathematical model which addresses one of the administrative tasks concerning administrators in all institutions, the establishment of faculty teaching schedules. The assignment technique utilized by the model is linear programming with two special characteristics: goal programming and mixed-integer programming. The goal programming characteristic refers to the provision of explicit stack variables to take on values representing deviations from assignment criteria that may result in resolving conflicts which arise from interactive administrative priorities. The mixed-integer programming characteristic refers to a requirement of the model that certain variables take on only integer values if they appear in the final solution. The model is demonstrated using two different sets of preference orderings for goal achievement.     

Attracting talent through diversity at the top: The impact of TMT diversity and firms' efforts to promote diversity on employer attractiveness

At a time where firms encounter a “race for talent”, it is crucial for many MNCs to present themselves as attractive employers. Failing to position themselves in the international labor market can reduce firms' ability to acquire valuable international human capital, thereby generating disadvantageous organizational effects. Against this backdrop, drawing on signaling theory and employer branding literature, our paper aims to shed light on the association between nationality diversity in the executive suite and MNCs' employer attractiveness. Our lines of argument build on the notion that top management team composition can affect MNCs' efforts to promote diversity among their employees. This focus on diversity, in turn, can affect MNCs' employer attractiveness. Examining firms from various European countries, we find that top management team nationality diversity is positively associated with firms' employer attractiveness for foreign job seekers. We also show that a firm's efforts to promote diversity mediate the relationship between TMT nationality diversity and employer attractiveness. We therefore advance the academic debate on diversity as an employer branding tool and a means to enhance employer attractiveness. In practical terms, we also provide valuable insights for firms wishing to transform into (more) diverse entities.     

ON FUZZY GOAL PROGRAMMING*

This paper illustrates how the goal programming problem with fuzzy goals having linear membership functions may be formulated as a single goal programming problem. Also, a previously defined method for dealing with fuzzy weights for each of the goals is re-examined.     

A goal programming approach to production planning for flexible manufacturing systems

The increased use of flexible manufacturing systems (FMS) reduces production time variability as the fixed processing time of automated systems replaces the variable processing time of human resources in production systems. This reduction in the variability permits the application of deterministic, constrained optimization models that recognize the conflicting multi-objective nature of resource utilization in an FMS environment. One such multi-objective optimization model is linear goal programming. Unlike most linear programming models, linear goal programming is designed to handle conflicting multi-objective problems. A goal programming model is developed a and presented in this paper as an aid in FMS planning. An application is also presented to illustrate the informational benefits of using the proposed model.     

A NOTE ON DECISION-MAKING CRITERIA FOR ALGORITHM SELECTION: REDUCING GOAL PROGRAMMING COMPUTATIONAL EFFORT

Most linear goal programming (LPG) algorithms are based on a simplex-type solution method that begins with an initial simplex tableau with solution-set variables (basic variables) consisting of all-negative deviational variables or all-positive deviational variables. This note (1) demonstrates how computational solution effort can be reduced by selecting the appropriate all-negative or all-positive deviational variable algorithm and (2) describes a procedure that can be used to aid decision makers in selecting the appropriate algorithm for all types of applied goal programming (GP) models. Results of this study reveal the procedure as accurate and providing computational time savings when applied to a large sample of GP problems.     

The Effects of Health Insurance and Self‐Insurance on Retirement Behavior

This paper provides an empirical analysis of the effects of employer‐provided health insurance, Medicare, and Social Security on retirement behavior. Using data from the Health and Retirement Study, we estimate a dynamic programming model of retirement that accounts for both saving and uncertain medical expenses. Our results suggest that Medicare is important for understanding retirement behavior, and that uncertainty and saving are both important for understanding the labor supply responses to Medicare. Half the value placed by a typical worker on his employer‐provided health insurance is the value of reduced medical expense risk. Raising the Medicare eligibility age from 65 to 67 leads individuals to work an additional 0.074 years over ages 60–69. In comparison, eliminating 2 years worth of Social Security benefits increases years of work by 0.076 years.     

A Game–Theoretic View of the Fiscal Theory of the Price Level

The goal of this paper is to probe the validity of the fiscal theory of the price level by modelling explicitly the market structure in which households and the government make their decisions. I describe the economy as a game, and I am thus able to state precisely the consequences of actions that are out of the equilibrium path. I show that there exist government strategies that lead to a version of the fiscal theory, in which the price level is determined by fiscal variables alone. These strategies are however more complex than the simple budgetary rules usually associated with the fiscal theory, and the government budget constraint cannot be merely viewed as an equilibrium condition.     

A survey of goal programming

In this paper the applications of goal programming to industrial and economic planning are reviewed, and it is shown how the linear programming framework can be extended to include a series of goals (or objectives). A planning method is suggested in which the decision maker is presented with a list of all the possible attainable goals from which he picks the most appropriate set.     

QUADRATIC PREFERENCES AND GOAL PROGRAMMING

One of the shortcomings of goal programming lies in its linearity, assumption, specifically in the objective function. This assumption compels one to work with constant marginal utilities and rates of substitution. In this paper a quadratic preference function, which is more consistent with economic theory and reality, is formulated and introduced into goal programming. In an effort to facilitate the understanding of the proposed procedure, two illustrative examples—one with symmetric preferences and the other with asymmetric preferences, both applied to the objective function—are solved and compared with a goal programming solution.     

ON FUZZY GOAL PROGRAMMING—SOME COMMENTS

This paper pertains to goal programming with fuzzy goals and fuzzy priorities. Hannan [1], in his paper on fuzzy goal programming, alludes to the difficulty of handling fuzzy priorities and further notes that a method that this author proposed [2] may lead to incorrect results. In this note, the general problem of goal programming with fuzzy priorities is reexamined, along with the solution to the specific example presented in my original paper [2]. It is shown that the method for handling fuzzy priorities originally proposed by this author does indeed capture the relative importance of goals.     

Graphic Feedback,Performance Feedback,and Goal Setting Increased Staff Compliance With a Data Collection Task at a Large Residential Facility

Because of budgetary constraints, human service organizations frequently expect consultants to recommend methods to enhance staff performance that require little or no additional funding. The current study utilized a multiple baseline across settings design to evaluate and compare the effects of a single intervention and a packaged intervention including graphic feedback and goal setting on approximately 200 direct care staff as a group, to improve the data collection procedure at a residential treatment facility. The single intervention showed some improvement, whereas the packaged intervention yielded substantial improvements and was maintained.     

An integer programming framework for critical elements detection in graphs

This study presents an integer programming framework for minimizing the connectivity and cohesiveness properties of a given graph by removing nodes and edges subject to a joint budgetary constraint. The connectivity and cohesiveness metrics are assumed to be general functions of sizes of the remaining connected components and node degrees, respectively. We demonstrate that our approach encompasses, as special cases (possibly, under some mild conditions), several other models existing in the literature, including minimization of the total number of connected node pairs, minimization of the largest connected component size, and maximization of the number of connected components. We discuss computational complexity issues, derive linear mixed integer programming (MIP) formulations, and describe additional modeling enhancements aimed at improving the performance of MIP solvers. We also conduct extensive computational experiments with real-life and randomly generated network instances under various settings that reveal interesting insights and demonstrate advantages and limitations of the proposed framework.