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The purpose of this survey paper is to paint the landscape of research on the efficiency profiles of Islamic banks with a focus on Data Envelopment Analysis (DEA) studies. This paper uses a comprehensive systematic literature review to survey 80 relevant papers published between 2004 and 2021. Such landscape is organised around classifications of types of measures of efficiency and their analyses, types of DEA analyses, types of DEA modelling frameworks and models, evaluation approaches and choice of variables for the specification of DEA models, and drivers of efficiency. To the best of our knowledge, no survey on the drivers of efficiency has ever been conducted. In this paper, we overcome this gap in the context of Islamic banking and back up previous findings with relevant theories. In addition, we unify the literature terminologywise. Finally, a critical analysis of the literature is provided along with methodological gaps, inconsistencies, and future research directions. [ABSTRACT FROM AUTHOR]

So far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)‐based multi‐period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration‐dependent frameworks outperform both dynamic models developed under duration‐independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models. [ABSTRACT FROM AUTHOR]

This paper presents a new heuristic to solve the problem of making sequencing, lot sizing and scheduling decisions for a number of products manufactured in a flow shop environment, so as to minimize the sum of setup and inventory holding costs while a given demand is fulfilled without backlogging. The proposed solution method first determines sequencing decisions then lot sizing and scheduling decisions are simultaneously determined. This heuristic, called the G-group method, divides the set of products into G groups and requires that products belonging to the same group have the same cycle time. Also, the cycle time of each group is restricted to be an integer multiple of a basic period. For each basic period of the global cycle, the products to be produced during this period and the production sequence to be used are chosen. Then, a non-linear program is solved to determine lot sizes and to construct a feasible production schedule. To evaluate its performance, the G-group method was compared to four other methods. Numerical results show that the proposed heuristic outperforms all these methods. [ABSTRACT FROM AUTHOR]