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Here, scalarization techniques for multi-objective optimization problems are addressed. A new scalarization approach, called unified Pascoletti-Serafini approach, is utilized and a new algorithm to construct the Pareto front of a given bi-objective optimization problem is formulated. It is shown that we can restrict the parameters of the scalarized problem. The computed efficient points provide a nearly equidistant approximation of the whole Pareto front. The performance of the proposed algorithm is illustrated by various test problems and its effectiveness with respect to some existing methods is shown.

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Efficiency plays a pivotal role in impacting costs and optimizing resource utilization for
businesses. This study aims to evaluate the technical and scale efficiency of 15 suppliers within
a production unit over a three-year period (2020-2022) using data envelopment analysis
(DEA). The analysis will involve assessing efficiency under two assumptions - constant returns
to scale and variable returns to scale. Variables were selected based on indicator availability,
representation principles, and expert input, with inputs including investment, nonoperating
expense costs, and operational expenses (comprising raw material costs, wages, and
overheads), while outputs encompass net sales and return on investment. Results from the study
indicated that supplier one, scoring 0.5716 assuming constant returns to scale and 0.6790
under variable returns to scale, emerged as the least efficient supplier. Interestingly, only two
suppliers (8 and 15) demonstrated higher efficiency levels. However, the net technical efficiency
of the supply chain showed an increasing concentration, which indicates the overall reduction
of the gap between suppliers and the improvement of the net technical efficiency in the supply
chain of the production unit. This study provides valuable insights into the differences between
suppliers from a macro perspective and offers guidance for manufacturing units looking to
expand their supply chain. 

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Evaluation of healthcare systems, as a key organization providing different health services, is essential. This issue becomes more crucial when occurring crises such as a pandemic. They need to keep track of their success in the face of the crisis to assess the effects of policy changes and their capability to respond to new challenges. The inverse data envelopment analysis (InvDEA) technique is an applicable method in order to estimate the input/output levels of decision-making units (DMUs) to preserve predetermined technical efficiency scores. In classic studies of InvDEA, decision-Making Units (DMUs) as black boxes, ignoring their internal structure. This paper estimates input levels and new intermediate products to achieve a predetermined efficiency score set by the decision maker. In traditional inverse data envelopment analysis models, precise data are required to determine the input and/or output levels of each decision-making unit. However, in many scenarios, such as system flexibility, social and cultural contexts information may be indeterminate. In these cases, experts’ opinions are used to model uncertainty. Uncertainty theory, a branch of mathematics, logically deals with degrees of belief. This paper aims to develop an inverse Network DEA model incorporating uncertainty theory. We assume that inputs and outputs of decision-making units are based on experts’ belief degrees. To demonstrate the model is performance, we explore efficiency of healthcare systems during COVID-19 pandemic.