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This paper presents a robust tool for ranking MHE prior to selecting the best one. The proposed ranking methodology is based on a new key performance indicator, identified as market weight scheme (MWS). The MWS of each MHE reflects the market's importance of its performance criteria and it is built through an ingenious integration of the most resonated appreciative (MRA) model with the ordered weighted averaging (OWA) operator. The flexibility of MRA is exploited to customize the weight profiles of the data envelopment analysis (DEA) peer-evaluations, whereas the OWA aggregation process is run to produce the associated MWS for a pre-set decision maker's (DM) subjective judgment towards the MHE market stability. Thus, the novelty of the present study is threefold. (1) The MHE is selected from a collective market perspective rather than individualistic manufacturer's viewpoint. (2) The ranking process is based on the aggregation of weight profiles instead of cross-efficiency (CE) scores, as traditionally done in DEA-CE approaches. (3) The DM's subjectivism is integrated into the assessment process through an optimism measure that mimics the market stability. The application of the proposed methodology on a sample of 27 robots asserts its robustness as a selection tool. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate agricultural sustainability as a collective issue involving multiple rather than individual farms. Through the utilization of energy consumption as a proxy, we propose a novel methodology that evaluates the impact of farm consolidations on agricultural sustainability while accounting for resource preferences. Our approach incorporates the ordered weighted averaging (OWA) operator within an inverse data envelopment analysis (IDEA) model to identify post-merger farms that meet preset efficiency targets. We employ a DEA cross-efficiency (DEA-CE) procedure to select merger plans that maximize agricultural sustainability for each preference scenario. By analysing a case study of 43 tomato greenhouse farms in Biskra, northern Algeria, our findings demonstrate that mergers can significantly enhance agricultural sustainability, surpassing the potential of individual farms by a factor of over 15. Additionally, the adoption of the most sustainable merger plan can lead to energy savings of more than 69%. Irrespective of the preference scenario, substantial energy savings in machinery, fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed. These results emphasize the strategic role of merger processes in promoting agricultural sustainability and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

In this paper, we present a multi-faceted approach for ranking dispatching rules (DRs) in multi-objective dynamic flow shop scheduling systems using data envelopment analysis (DEA). The merits of the proposed DEA-based approach stem in its ability to (1) integrate explicitly, under the same DEA framework, desirable and undesirable performance criteria of DRs without a priori normalisation or aggregation; (2) guarantee that the best DR preserves its benchmarking status regardless of the production scenario; (3) circumvent potential occurrence of multiple efficient DRs through embedding ordered weighted averaging (OWA) under DEA cross evaluation to produce aggregate ranking scores for the DRs. The evaluation of the new ranking approach is conducted using 18 data instances of 20 DRs each. The results reveal that, whatever the OWA optimism level, the preferred DR shifts away from the Shortest Processing Time (SPT) rule to the Cost Over Time (COVERT) rule as due-date tightness becomes relaxed, which appears consistent with known performance expectations of these DRs under such settings. To demonstrate a possible implementation of these results to support decision making in operations scheduling, we present a basic adaptive rule that switches automatically between the preferred rules based on real-time due-date tightness and machine utilisation levels. [ABSTRACT FROM AUTHOR]

This paper presents a new application of Inverse data envelopment analysis (InvDEA) for strategic decision making: mergers & acquisitions (M&A) in the agricultural sector. Given a set of agricultural farms, the decision maker is interested in estimating the potential gains that are likely to result from the merger of two or more farms, as well as the redistribution of inputs among the merging farms, for an efficiency target set a priori for the post-merger farm. Using a sample of greenhouse (GH) farms from the Batinah region (Oman), an InvDEA approach is applied to investigate pairwise consolidations among GH farms and determine the level of inputs required for a merger to achieve full efficiency. Moreover, a DEA-based approach is introduced for selecting the best partners of a merger based on pertaining potential gains. The results highlight the importance of mergers as a strategic option for an efficient management of resources in the agricultural sector, especially scarce resources, like water and electricity. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel multi-period data envelopment analysis (MDEA) model that attempts to circumvent the limitations of the existing MDEA models. The proposed global MDEA model is essentially based on major modifications of fundamental DEA axioms to enable a decision making unit (DMU), defined with inputs and outputs of period t, to be evaluated within the production possibility set (PPS) of another period l, t ≠ l. Building on the properties of the global MDEA model, we also introduce a global productivity index, identified as Global Progress and Regress index (GPRI), that render possible the evaluation of a DMU's extent of progress or regress over multi-period time horizons under variable returns to scale (VRS) production technologies. This lifts the restrictions to two successive periods and constant returns to scale (CRS) of existing productivity indices. The most salient features of the new MDEA model as well as the GPRI are highlighted using an application that involves a real-life sample of 25 bank branches considered over 4 years. [ABSTRACT FROM AUTHOR]