Grammatoula Papaioannou

Data envelopment analysis (DEA) is an optimization-based methodology used for the assessment of efficiency of organizations. Traditional DEA models are based on the vectors of inputs (resources) and outputs (products or services) of the observed organizations. DEA models are often enhanced by additional value judgements stated as tradeoffs between inputs and outputs or as dual weight restrictions. In the paper “Proper and consistent production trade-offs in models of data envelopment analysis”, Podinovski and Papaioannou showed that such additional value judgments may be contradictory (not proper) and develop analytical and computational tests for their identification. Any value judgments that are not proper indicate an error in their assessment. The developed approach is equally applicable in multicriteria decision analysis in which the tradeoffs between criteria are specified imprecisely by a set of linear inequalities stated in terms of criterion weights. Not proper tradeoffs result in the weights of some criteria (identifiable by the developed computational approach) being equal to zero, which effectively excludes such criteria from the analysis.

Free disposal hull (FDH) is a nonparametric model of production technology based on the single assumption of free disposability of all inputs and outputs. In this paper, we consider multicomponent production technologies in which every decision making unit (DMU) consists of several parallel component processes that can in principle operate independently of each other, provided they have sufficient resources. An example is universities viewed as DMUs, with their departments or groups of departments viewed as component processes. Each component process uses its own set of inputs and an unknown part of the shared inputs in order to produce its own set of outputs and an unknown part of the shared outputs. We allow combinations of component processes taken from different DMUs in order to construct new hypothetical DMUs, and refer to the resulting model of technology as the multicomponent FDH (MFDH) model. We further develop a larger, and mathematically nontrivial, variant of MFDH for the case in which we can specify certain bounds on the proportions in which shared inputs and outputs are allocated to component processes. We use an illustrative example in the context of universities to demonstrate the increasing discriminatory power of the new MFDH models over the standard FDH models in the multicomponent setting.

This paper examines the effect of different types of collaboration on the level of Postharvest Food Losses (PHFL) and the proportion of low-quality peaches produced using a unique data-set of Greek peach producers. Quantile regression techniques are adopted to estimate the effects at different points of the conditional distribution of our variables of interest. The findings of this study suggest that high levels of collaboration between producers and cooperatives are associated with both low levels of PHFL and a low proportion of low-quality peaches. We also find that specific types of collaboration, such as ‘goal congruence’, can play a significant role in reducing PHFL and improving the quality of peach production at the extremes of the distribution. Important policy implications regarding collaborative practices and systems that can be implemented to reduce PHFL and boost a producer’s performance together with sustainability credentials are drawn from this study.

This paper presents extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously and part machine utilisation is considered. More specifically, an integration of inter-cell movements of parts and machine set-up costs within the objective function, and also a combination of machine set-up costs associated with parts revisiting a cell when the part machine operation sequence is taken into account are examined and an enhanced model is formulated. Based upon this model’s requirements, an initial three stage approach is proposed and a tabu search iterative procedure is designed to produce a solution. The initial approach consists of the allocation of machines to cells, the allocation of parts to machines in cells and the evaluation of the objective function’s value. Special care has been taken when allocating parts to machine cells as part machine operation sequence is preserved making the system more complex but more realistic. The proposed tabu search algorithm integrates short term memory and an overall iterative searching strategy where two move types, single and exchange, are considered. Computational experiments verified both the algorithm’s robustness where promising solutions in reasonably short computational effort are produced and also the algorithm’s effectiveness for large scale data sets.