Tailor-made additives can prove an effective method to prolong the lifetime of metastable forms of pharmaceutical compounds by surface stabilization. Pyrazinamide (PZA) is a pharmaceutical compound with four polymorphic forms. The high temperature γ form, which can be produced by spray drying or sublimation growth, is metastable at room temperature and transforms within days when produced by sp...

In performance evaluation, it is important to identify both the efficient frontier and the critical measures. Data envelopment analysis (DEA) has been proven an effective tool for estimating the efficient frontiers, and the optimized DEA weights may be used to identify the critical measures. However, due to multiple DEA optimal weights, a unique set of critical measures may not be obtained for ...

This paper surveys recently developed analytical methods for studying the sensitivity of DEA results to variations in the data. The focus is on the stability of classification of DMUs (Decision Making Units) into efficient and inefficient performers. Early work on this topic concentrated on developing solution methods and algorithms for conducting such analyses after it was noted that standard ...

The DMU-package for Analyzing Multivariate Mixed Models has been developed over a period of more than 25 years. This paper gives an overview of new features and the recent developments around the DMU-package, including: Genomic prediction (SNPBLUP, G-BLUP and “One-Step”), Genome-wide association studies, Survival models and double hierarchical generalized linear mixed models.

Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different mult...

In this paper we first (trivially) show that data envelopment analysis can be viewed as maximising the sum of the efficiencies of the decision-making units (DMU's) in the organisation. Building upon this we present nonlinear models for: (a) allocating fixed costs to DMU's and (b) allocating resources to DMU's. Simultaneous to allocating resources output targets are also decided for each DMU. Nu...

Data envelopment analysis (DEA), as originally proposed, is a methodology for evaluating the relative efficiencies of a set of homogeneous decision-making units (DMUs) in the sense that each uses the same input and output measures (in varying amounts from one DMU to another). In some situations, however, the assumption of homogeneity among DMUs may not apply. As an example, consider the case wh...

Data envelopment analysis (DEA) is a mathematical programming method for calculatingefficiency of decision making units (DMU). In calculating the efficiency score of unitsthrough DEA we may come up with some efficient units. But the question is among theseefficient units which of them is better. As we know, it is possible to rank inefficient unitsthrough efficiency score; however, for ranking e...

This paper gives a new application of DEA to evaluate the scheduling solutions of parallel processing. It evaluates the scheduling solutions of parallel processing using the non-convex DEA model, FDH model. By introducing each solution of parallel processing scheduling as a DMU with some relevant inputs and outputs this paper shows that how the most efficient schedule(s) can be identified.

In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The purpose of this paper is ra...