Thomas R. Sexton

Bio: Thomas R. Sexton is an academic researcher from Stony Brook University. The author has contributed to research in topic(s): Data envelopment analysis & Inefficiency. The author has an hindex of 21, co-authored 57 publication(s) receiving 2992 citation(s). Previous affiliations of Thomas R. Sexton include State University of New York System.

Data Envelopment Analysis: Critique and Extensions

Abstract: This chapter points out serious shortcomings in DEA's treatment of price efficiency, illustrates the dangers of misspecification errors in DEA, and suggests extentions of the basic DEA formulation that address these shortcomings.

Network DEA: efficiency analysis of organizations with complex internal structure

Abstract: DEA models treat the DMU as a "black box." Inputs enter and outputs exit, with no consideration of the intervening steps. Consequently, it is difficult, if not impossible, to provide individual DMU managers with specific information regarding the sources of inefficiency within their DMUs. We show how to use DEA to look inside the DMU, allowing greater insight as to the sources of organizational inefficiency. Our model applies to DMUs that consist of a network of Sub-DMUs, some of which consume resources produced by other Sub-DMUs and some of which produce resources consumed by other Sub-DMUs. Our Network DEA Model allows for either an input orientation or an output orientation, any of the four standard assumptions regarding returns to scale in any Sub-DMU, and adjustments for site characteristics in each Sub-DMU. We demonstrate how to incorporate reverse quantities as inputs, intermediate products, or outputs. Thus, we can apply the Network DEA Model presented here in many managerial contexts. We also prove some theoretical properties of the Network DEA Model.By applying the Network DEA Model to Major League Baseball, we demonstrate the advantages of the Network DEA Model over the standard DEA Model. Specifically, the Network DEA Model can detect inefficiencies that the standard DEA Model misses. Perhaps of greatest significance, the Network DEA Model allows individual DMU managers to focus efficiency-enhancing strategies on the individual stages of the production process.

Two-Stage DEA: An Application to Major League Baseball

Abstract: We show how to use DEA to model DMUs that produce in two stages, with output from the first stage becoming input to the second stage. Our model allows for any orientation or scale assumption. We apply the model to Major League Baseball, demonstrating its advantages over a standard DEA model. Our model detects inefficiencies that standard DEA models miss, and it can allow for resource consumption that the standard DEA model counts towards inefficiency. Additionally, our model distinguishes inefficiency in the first stage from that in the second stage, allowing managers to target inefficient stages of the production process.

Optimizing Single Vehicle Many-to-Many Operations with Desired Delivery Times: I. Scheduling

Abstract: A set of n customers is given. Each customer has a desired point of pickup, a desired point of delivery and a desired time of delivery. The problem is to determine the order of pickup and delivery and the times of pickup and delivery of these n customers by a single vehicle in order to minimize total customer inconvenience. Here, a mathematical programming formulating of this problem is subjected to Benders' decomposition procedure. The result is a heuristic routing and scheduling algorithm which is shown to produce high quality solutions in reasonable computation time by testing on moderately sized real data bases from both Gaithers-burg, Maryland, and Baltimore, Maryland. This study is divided into two parts, the first detailing the scheduling analysis and the second focusing on the routing component.

The methodology of data envelopment analysis

Abstract: Data envelopment analysis is a linear programming–based method that has clear advantages over competing approaches. However, its own limitations should not be overlooked.

Algorithms for the vehicle routing and scheduling problems with time window constraints

Abstract: This paper considers the design and analysis of algorithms for vehicle routing and scheduling problems with time window constraints. Given the intrinsic difficulty of this problem class, approximation methods seem to offer the most promise for practical size problems. After describing a variety of heuristics, we conduct an extensive computational study of their performance. The problem set includes routing and scheduling environments that differ in terms of the type of data used to generate the problems, the percentage of customers with time windows, their tightness and positioning, and the scheduling horizon. We found that several heuristics performed well in different problem environments; in particular an insertion-type heuristic consistently gave very good results.

Estimation and inference in two-stage, semi-parametric models of production processes

Abstract: Many papers have regressed non-parametric estimates of productive efficiency on environmental variables in two-stage procedures to account for exogenous factors that might affect firms’ performance. None of these have described a coherent data-generating process (DGP). Moreover, conventional approaches to inference employed in these papers are invalid due to complicated, unknown serial correlation among the estimated efficiencies. We first describe a sensible DGP for such models. We propose single and double bootstrap procedures; both permit valid inference, and the double bootstrap procedure improves statistical efficiency in the second-stage regression. We examine the statistical performance of our estimators using Monte Carlo experiments.

Data envelopment analysis (DEA) - Thirty years on

Abstract: This paper provides a sketch of some of the major research thrusts in data envelopment analysis (DEA) over the three decades since the appearance of the seminal work of Charnes et al. (1978) [Charnes, A., Cooper, W.W., Rhodes, E.L., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444]. The focus herein is primarily on methodological developments, and in no manner does the paper address the many excellent applications that have appeared during that period. Specifically, attention is primarily paid to (1) the various models for measuring efficiency, (2) approaches to incorporating restrictions on multipliers, (3) considerations regarding the status of variables, and (4) modeling of data variation.

Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in taiwan

Abstract: The efficiency of decision processes which can be divided into two stages has been measured for the whole process as well as for each stage independently by using the conventional data envelopment analysis (DEA) methodology in order to identify the causes of inefficiency. This paper modifies the conventional DEA model by taking into account the series relationship of the two sub-processes within the whole process. Under this framework, the efficiency of the whole process can be decomposed into the product of the efficiencies of the two sub-processes. In addition to this sound mathematical property, the case of Taiwanese non-life insurance companies shows that some unusual results which have appeared in the independent model do not exist in the relational model. In other words, the relational model developed in this paper is more reliable in measuring the efficiencies and consequently is capable of identifying the causes of inefficiency more accurately. Based on the structure of the model, the idea of efficiency decomposition can be extended to systems composed of multiple stages connected in series.