Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

COOPER-framework: A Unified Standard Process for Non-parametric Projects

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the ‘COOPER-framework’ a comprehensive model for carrying out non-parametric projects. The framework consists of six interrelated phases: Concepts and objectives, On structuring data, Operational models, Performance comparison model, Evaluation, and Result and deployment. Each of the phases describes some necessary steps a researcher should examine for a well defined and repeatable analysis. The COOPER-framework provides for the novice analyst guidance, structure and advice for a sound non-parametric analysis. The more experienced analyst benefits from a check list such that important issues are not forgotten. In addition, by the use of a standardized framework non-parametric assessments will be more reliable, more repeatable, more manageable, faster and less costly.DEA, non-parametric efficiency, unified standard process, COOPER-framework.

Brand strategy scope and advertising spending: The more the better?

Given the need to justify business management expenses, firms are very interested in measuring marketing performance. The objective of this article is to analyze mass media advertising investment from an efficient point of view in hotel chains. To accomplish the objective, this article applies a two-stage double bootstrap data envelopment analysis to the monetary resources allocated to the different advertising media by the main companies in the Spanish hotel sector. The authors further investigate the determinants of hotel advertising efficiency in terms of the number of brands in the hotel portfolio and the combination of advertising media used (i.e. Internet advertising). The results show a certain level of waste of advertising spending by hotel chains and that both brand portfolio scope and Internet advertising positively affect efficiency.This work was partially supported by the Spanish Ministry of Science, Innovation and Universities under research project INTETUR (RTI2018-099467-B-I00)

Value Efficiency Analysis for Incorporating Preference Information in Data Envelopment Analysis

We develop a procedure and the requisite theory for incorporating preference information in a novel way in the efficiency analysis of Decision Making Units. The efficiency of Decision Making Units is defined in the spirit of Data Envelopment Analysis (DEA), complemented with Decision Maker's preference information concerning the desirable structure of inputs and outputs. Our procedure begins by aiding the Decision Maker in searching for the most preferred combination of inputs and outputs of Decision Making Units (for short, Most Preferred Solution) which are efficient in DEA. Then, assuming that the Decision Maker's Most Preferred Solution maximizes his/her underlying (unknown) value function at the moment when the search is terminated, we approximate the indifference contour of the value function at this point with its possible tangent hyperplanes. Value Efficiency scores are then calculated for each Decision Making Unit comparing the inefficient units to units having the same value as the Most Preferred Solution. The resulting Value Efficiency scores are optimistic approximations of the true scores. The procedure and the resulting efficiency scores are immediately applicable to solving practical problems

Practical Aspects of Value Efficiency Analysis

In this paper, we consider practical aspects for measuring Value Efficiency in Envelopment Analyis. Value efficiency is an efficiency concept that takes into account the decision maker's preferences.It was developed by Halme, Joro, Korhonen, Salo and Wallenius [1998]. The decision maker is asumed to compare alternatives using an implicitly known value function which reaches its maximum at the most preferred point on the efficient frontier. The unknown value function is assumed to be pseudoconcave and strictly increasing for outputs and strictly decreasing for inputs. The purpose of value effiiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. Because the value function is unknown, the indifference contours cannot be defined precisely. Value efficiency analysis never results in more pessimistic evaluation than in the case of a known function. To carry out value efficiency analysis, we have to locate the most preferred solution of the decision maker. In practice, this phase cannot be too complicated. We propose a few alternative ways to locate it and discuss the use of those ways in practical application

DEA-Based Incentive Regimes in Health-Care Provision

A major challenge to legislators, insurance providers and municipalities will be how to manage the reimbursement of health-care on partially open markets under increasing fiscal pressure and an aging population. Although efficiency theoretically can be obtained by private solutions using fixed-payment schemes, the informational rents and production distortions may limit their implementation. The healthcare agency problem is characterized by (i) a complex multi-input multi-output technology, (ii) information uncertainty and asymmetry, and (iii) fuzzy social preferences. First, the technology, inherently nonlinear and with externalities between factors, yield parametric estimation difficult. However, the flexible production structure in Data Envelopment Analysis (DEA) offers a solution that allows for the gradual and successive refinement of potentially nonconvex technologies. Second, the information structure of healthcare suggests a context of considerable asymmetric information and considerable uncertainty about the underlying technology, but limited uncertainty or noise in the registration of the outcome. Again, we shall argue that the DEA dynamic yardsticks (Bogetoft, 1994, 1997, Agrell and Bogetoft, 2001) are suitable for such contexts. A third important characteristic of the health sector is the somewhat fuzzy social priorities and the numerous potential conflicts between the stakeholders in the health system. Social preferences are likely dynamic and contingent on the disclosed information. Similarly, there are several potential hidden action (moral hazard) and hidden information (adverse selection) conflicts between the different agents in the health system. The flexible and transparent response to preferential ambiguity is one of the strongest justifications for a DEA-approach. DEA yardstick regimes have been successfully implemented in other sectors (electricity distribution) and we present an operalization of the power-parameter p in an pseudo-competitive setting that both limits the informational rents and incites the truthful revelation of information. Recent work (Agrell and Bogetoft, 2002) on strategic implementation of DEA yardsticks is commented in the healthcare context, where social priorities change the tradeoff between the motivation and coordination functions of the yardstick. The paper is closed with policy recommendations and some areas of further work.Data Envelopment Analysis, regulation, health care systems, efficiency, Health Economics and Policy,

A Multiple Criteria Framework to Evaluate Bank Branch Potential Attractiveness

Remarkable progress has occurred over the years in the performance evaluation of bank branches. Even though financial measures are usually considered the most important in assessing branch viability, we posit that insufficient attention has been given to other factors that affect the branches’ potential profitability and attractiveness. Based on the integrated used of cognitive maps and MCDA techniques, we propose a framework that adds value to the way that potential attractiveness criteria to assess bank branches are selected and to the way that the trade-offs between those criteria are obtained. This framework is the result of a process involving several directors from the five largest banks operating in Portugal, and follows a constructivist approach. Our findings suggest that the use of cognitive maps systematically identifies previously omitted criteria that may assess potential attractiveness. The use of MCDA techniques may clarify and add transparency to the way trade-offs are dealt with. Advantages and disadvantages of the proposed framework are also discussed.

Measuring the Efficiency of Residential Real Estate Brokerage Firms

This article measures overall, allocative, technical, pure technical and scale efficiency levels for a sample of residential real estate brokerage firms using data envelopment analysis, a linear-programming technique. The results suggest that real estate brokerage firms operate inefficiently. Inefficiencies are primarily a function of sub-optimal input allocations and the failure to operate at constant returns to scale rather than from poor input utilization. Regression analysis is employed to determine which firm and/or market characteristics affect efficiency levels. The results show that increasing firm size increases efficiency while choosing to franchise, adding an additional multiple listing service and increasing operating leverage decreases firm performance.