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Models for Planning and Budgeting in Higher Education

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Handbook of Operations Research and Management Science in Higher Education

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 309))

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Abstract

The budget of an organization reflects its plan in monetary terms over a given period—usually for a year. This chapter is devoted to planning and budgeting in higher education (HE). We present various budgeting procedures, such as incremental budgeting, along with costing methods in HE institutions (HEIs). A variety of optimization models with budget constraints and bounds on the allocations to organizational units are given, where the bounds are a result of the planning process and past budgets. We present linear and nonlinear objective functions (quadratic), with and without constraints, intertwined with a simulation scheme in an HEI. The optimization models are presented in a single and multilevel hierarchy, and over time. Moreover, several aggregative long-run financial models are presented. We conclude that the quadratic model fits HE better than the linear model since it provides allocations around the midpoints of the upper and lower bounds of the allocations rather than the extreme linear model’s allocation. We determine that the quadratic model procedure is preferred, as its optimal solution is intuitive and does not require mathematical formulation and skills. Moreover, the relationship between the mathematical models and known budgeting procedures are analyzed, and we conclude that the optimization/simulation scheme described here results in a combination of several budgeting procedures—as actually happens in practice.

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Correspondence to Zilla Sinuany-Stern .

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Appendices

Appendix 1: Summary of the Literature on Planning and Budgeting in HE

Author (year)

Method used

Type of planning/budgeting

HE area

Levels HE hierarchy

Country

Balderston and Weathersby (1972)

PPBS

Program planning & budgeting

University

USA

Weathersby and Balderston (1972)

    

Sinuany-Stern (1983a)

Break-even aggregate, income vs. expense equations

Increment budgeting

Long-run planning

University

USA

Sinuany-Stern (1983b)

Simulation, system analysis approach

Budgeting

Costing

University

USA

Sinuany-Stern (1984a)

Budget planning procedure; Projections & Scenario analysis

Budgeting & planning

Financial planning model

Multi-campus university

USA

Sinuany-Stern (1984b)

Network optimization linear with bounds and budget constraint

Budgeting

Budget allocation over time

Multi-campus university

USA

Brandeau et al. (1987)

Set of equations predicting incremental change in expenses & revenues by dept.

RCB & increment budget for 5 years

Budgeting & planning, cost (in a private university)

Medical school by department

USA

Whalen (1991)

Decentralized management of HEI

Responsibility-center budgeting

HEI

USA

Banerjee and Igbaria (1993)

Questionnaire/ survey

Computer capacity planning

National

USA

Sinuany-Stern and Yelin (1993)

Regression

Hardware resources (CPU, memories, etc.)

University

Israel

Sinuany-Stern et al. (1994)

Data envelopment analysis (DEA)

Efficiency and planning academic departments

University

Israel

Thomas (1999)

System of resource allocation

Formula-based budgeting

HEI

UK

Priest et al. (2002)

Review

Incentive-based budgeting

Pub. Univ.

General

Kao et al. (2003)

Decision support allocation model via circulation data mining

Library acquisition budget

Decision-making, budget allocation

University library

Taiwan

Adekanmbi and Boadi (2008)

Questionnaire to study libraries budgeting

Budgeting

Library resource acquisition

Libraries at 6 teaching colleges

Botswana

Nicholls (2009)

Markov models

Planning & benchmarking doctoral progression

University

Australia

Zierdt (2009)

Responsibility-centered budgeting

HEI

USA

Trusheim and Rylee (2011)

Cohort-survival, percentages by type of student

Enrollment prediction & tuition income

University

USA

Frank (2012)

Formula mainly by discipline per student

Formula budgeting

State allocation

National

Israel

Tang and Yin (2012)

Grey models better than exponential smoothing

Education expenditure & student enrollment

National

USA

Agboola and Adeyemi (2013)

Ratio method & fixed annual percent

Student forecast for faculty planning

National

Nigeria

Ekanem (2014)

Questionnaire on ZBB—Descriptive statistics

Zero-based budgeting (ZBB)

To verify staff opinion on ZBB

University

Nigeria

Cheporov and Cheporova (2015)

Time equations activity-based costing

Formula budgeting

Student/faculty ratio is too low

University

Ukraine

Sweeney et al. (2016)

Educational data mining, regression, factorization machine

Strategy planning

Student planning—Retention, by grade prediction

National

USA

Davidovitch et al. (2016)

Regression

Criteria for faculty incentive for teaching & research excellence

University

USA

Soares et al. (2016)

General evaluation

Activity-based costing for community college

HEIs

USA

Philbin & Mallo, 2016

MSP (managing successful programs)

Strategic planning

Investing in new research facility

University in cooperation

United Kingdom

Steinþórsdóttir et al. (2016)

Case study

Budgeting

Gender needs

University

Iceland

De La Torre et al. (2016)

Mix integer linear programming (MILP)

Planning

Long-term faculty size & composition

Public universities

Spain

Gorbunov et al. (2017)

Simulation model, using AnyLogic (deterministic)

Private cloud vs. university’s own server

Planning future info. Sys. Load for ed. purposes, facility planning

University

Russia

Bogomolova et al. (2017)

Correlation between economic factors and budgeting of science

Planning & budgeting

Budgeting science in universities

National

USA and Russia

Kenno and Sainty (2017)

Heuristic inquiry and content analysis

Activity-based budgeting

Resource allocation

University

Canada

Xiao and Chankong (2017) simulation

System dynamics

Demand & supply forecast of students (medical talents)

National

China

Hamid et al. (2018)

Survey listing space by type of facility (labs & classroom) by government standards

Space capacity, facility, & efficiency

Facility planning, performance strategy

Public universities

Malaysia

Walter (2018)

Case study to promote strategic cooperation of library users

Strategic planning

Facility planning, strategy

University library

USA

Bogomolova et al. (2018)

Optimization model—Quadratic l

Budgeting

Resource allocation

Universities, national

Russia

Magnanti and Natarajan (2018)

Discrete optimization

Allocating students to multidisciplinary projects

School of engineering

Singapore

Aviso et al. (2019)

P-graph model

Human resource

Planning, resourcing, quality

National

Philippines

Garcia (2019)

Multi-objective max-flow

Scenario analyses

Optimal resource allocation

College of business

USA

Myers (2019)

Moral hazard mathematical program

Responsibility-centered budgeting

University

USA & Canada

Oude Vrielink et al. (2019)

Timetabling

Planning

Course, exam, class, faculty

HEI

General review

Smith (2019)

Review

HE accounting & budgeting

HEI

General

Henry-Moss et al. (2019)

Online survey

Lactation facility for breast feeding

Service campus facilities for women, better accessibility

National Community colleges

USA

Wardhani et al. (2019)

Regression, employee’s questionnaire methods on good governance in budgeting & planning

Participation & budget control importance

Strategy, planning, budgeting

University

Indonesia

  1. Source Google scholar (assessed during 2020)

Appendix 2: Example for Linear and Quadratic Budget Allocation Modes

Assume we have 6 schools in a university. The first 3 columns of Table 9.4 present the number of students, the upper and lower bounds of the allocations to the schools, where the lower bounds are based on cost analyses, where the upper bounds are the requests of the schools. The schools are ordered according to their number of students.

Table 9.4 Linear and quadratic budget allocation under bounds and limited budgeta

Under budget constraint and bounds constraints: the linear model allocates to the first four schools their upper bound and to the other schools, with a lower number of students, only their lower bound is allocated, as shown in the fifth column, according to model 1c in Table 9.1. First, the lower bounds are allocated to each school, afterwards the first schools get addition to their upper bound are reached till the budget is finished, the rest of the schools do not get any additional budget beyond their lower bound.

While the quadratic model allocates between the bounds according to model 2c in Table 9.2 not exactly in the middle of the midpoint, as shown in the seventh column. After initial allocations in the midpoints, the leftover added to each school proportionally to their number of students.

When there are no budget constraints, but there are bound constraints, then in the linear model, each school receives its upper bound, according to model 1d, as shown in Column 6.

While the quadratic model allocation to each school is in the midpoint between the bounds, as shown in Column 8. To simplify the calculation the president of the university can set the budget to be allocated according to the eighth column with a total of $73,800,000, and leave the leftover $200,000 (74,000,000 − 73,800,000) for unforeseen costs.

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Sinuany-Stern, Z. (2021). Models for Planning and Budgeting in Higher Education. In: Sinuany-Stern, Z. (eds) Handbook of Operations Research and Management Science in Higher Education. International Series in Operations Research & Management Science, vol 309. Springer, Cham. https://doi.org/10.1007/978-3-030-74051-1_9

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