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The purpose of this paper is to propose a modified model in multi-stage processes when there are intermediate measures between the stages and in this sense, the new efficiency scores are more accurate. Conventional data envelopment analysis (DEA) models disregard the internal structures of peer decision-making units (DMUs) in evaluating their relative efficiency. Such an approach would cause managers to lose important DMU information. Therefore, in multistage processes, traditional DEA models encounter problems when intermediate measures are used for efficiency evaluation.

In this study, two-stage additive integer-valued DEA models were proposed. Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

The advantage of the proposed models for multi-stage systems is that they can accurately determine the stages with the greatest weaknesses/strengths. By introducing an applied case in the Iranian power industry, the paper demonstrated the applications and advantages of the proposed models.

Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO₂) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO₂ consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO₂ consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019.

This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model.

After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process.

The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

This study aims to propose a new procedure called innovation radar in hospitality (IRH), which was specifically designed to measure the innovations in hotels.

Based on a systematic review, a structured questionnaire was developed with 31 questions. The questions covered 12 dimensions related to hospitality: offer, platform, solutions, customer, customer experience, value capture, processes, organization, supply chain, presence, network and brand. The developed IRH instrument allowed to identify five ordered stages of innovation in the hospitality industry: basic operational, advanced operational, basic innovator, intermediate innovator and advanced innovator. The IRH was tested in real environment in Brazilian Hotels.

The procedure proved to be stable and able to rank hotels by innovation. The IRH allocated hotels consistently into one of the five stages. By analyzing each survey hotel individually, the procedure showed no discrepancies between the individual rates and the allocated stage by IRH.

The IRH can be an automated and structured instrument to measure innovation by consumers, platforms, agencies, research studies and governments.

To the best of authors’ knowledge, this is the first structured and quantitative procedure to measure innovation in hotels. The radar was able to detect specific actions aimed at innovation that serve as a good prediction mechanism for innovation in the hospitality sector. In this context, the radar emerges as an important tool for innovation metrics in the tourism sector, offering analysis mechanisms and a way to evaluate and monitor companies.

本论文旨在提出一种新流程, 名为酒店服务创新雷达（IRH）, 其设计用来评估酒店的创新服务

本论文通过系统文献综述的方法, 设计结构性问卷, 此问卷有31个问题, 其问题涉及酒店服务的12个维度：产品、平台、解决方案、顾客、顾客体验、价值获得、流程、组织、供应链、存在、网络、和品牌。本论文开发的IRH量表可以用来确认酒店服务业中五个创业阶段：基础运营、升级运营、基础创新者、中级创新者、和高级创新者。IRH在多个巴西酒店中得到了真实的测评

IRH流程证明其是稳定的且能够评定酒店的创新级别。IRH流程将酒店体系地分在五个阶段。通过分析每个酒店, IRH流程显示酒店价格与IRH创新阶段之间并无偏差联系。

IRH流程是自动结构性的量表, 用来衡量创新, 其中参考了消费者、平台、组织、研究人员、和政府等多重方面。

本论文是首个科研项目, 提出这个结构性定量的流程, 以评估酒店的创新。IRH流程能够检测每个创新项目, 以作为酒店业中的创新预测指标。旅游业中IRH也可以作为很好的创新机制, 提供分析机制和评估监督公司。

The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming that all of the data are non-negative. However, there are situations where all data are binary. As an example, the information on many diseases in health care is binary data. The existence of binary data in traditional DEA models may change the behavior of the production possibility set (PPS). This study defines a binary summation operator, expresses the modified principles and introduces the extracted PPS of axioms. Furthermore, this study proposes a binary integer programming of DEA (BIP-DEA) for assessing the efficiency scores to use as an alternate tool in prediction.

In this study, the extracted PPS of modified axioms and the BIP-DEA model for assessing the efficiency score is proposed.

The binary integer model was proposed to eliminate the challenges of the binary-value data in DEA.

The importance of the proposed model for many fields including the health-care industry is that it can predict the occurrence or non-occurrence of the events, using binary data. This model has been applied to evaluate the most important risk factors for stroke disease and mechanical disorders. The targets set by this model can help to diagnose earlier the disease and increase the patients’ chances of recovery.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

Companies are increasingly bringing personnel together into teams from different countries, physically and/or electronically, to develop products for multiple or worldwide markets. Called global new product teams (GNPTs), these groups face significant challenges, including cultural diversity. Differing cultural values can lead to conflict, misunderstanding, and inefficient work styles on the one hand, and strong idea generation and creative problem solving on the other. A study was conducted to identify team compositions that would optimize the effects of national culture so that product development outcomes are favorable. This began by developing a theoretical framework describing the impact of national culture on product development tasks. The framework was then translated into several mathematical models using analytical derivations and comparative statics. The models identify the levels and variances of culture values that maximize product development success by simultaneously considering four relevant dimensions of GNPT performance. Next, the utility of these models was tested by means of numerical simulations for a range of team scenarios. Concludes by drawing implications of the findings for managers and researchers.

In China, more than 80,000 people have been diagnosed with COVID-19, and more than 3,000 people have lost their lives. It seems that there will be more deaths since the epidemic is not over. All the Chinese provinces have reported the COVID-19 cases. This chapter aims to explore the trend of COVID-19 treatment efficiency in Chinese provinces using the data released daily by China Center for Disease Control and Prevention. Since China Center for Disease Control and Prevention began to release data daily from January 24 to March 12, we have more than 40 groups of daily data for 31 provinces in China mainland. In the calculation, we take the daily data of each province as a sample and then we have more than 1,200 samples in this study.

We use additive two-stage data envelopment analysis as an efficiency evaluation tool to calculate the COVID-19 treatment efficiency. In our framework, the first stage is to understand the infection rate and the second stage is to evaluate the treatment efficiency. In the first stage for the tth day, we use total population (p) and number of people infected in the previous day (inf _t−1) as the inputs and cumulative number of people infected in the current day (inf _t ) as the output. In the second stage for the tth day, we use cumulative number of people infected in the current day (inf _t ) as the input and cumulative death in the current day (death _t ) and cumulative recovery in the current day (recov _t ) as the outputs. Some techniques on how to deal with undesirable outputs such as inf _t and death _t are employed in this study.

After we have the infection rate and treatment efficiency for the samples more than 1,200, we analyze the COVID-19 treatment efficiency and its development trend from January 24 to March 12 in 34 regions of China from static and dynamic aspects. The results show that, on the whole, the overall efficiency and phased efficiency of COVID-19 treatment efficiency in all regions of China are relatively high, which reflects the key factor for the Chinese government to quickly control the epidemic in the short term. Relatively speaking, the average efficiency value in the infection stage (first stage) is lower than the average efficiency value in the healing stage (second stage), which shows that the focus of anti-epidemic in China should be early detection and prevention rather than treatment process. In terms of trend, the total efficiency of COVID-19 treatment in each region shows a trend of “increasing first and then decreasing.” Our analysis indicates that in the initial stage, the continuous increase of various resources leads to the rise of the total efficiency, while in the later stage, the rapid decline of the number of infected people leads to the decrease of the total efficiency. Based on the results of the efficiency analysis, this study provides corresponding management implications and policy suggestions, hoping to provide some enlightenment and suggestions for the anti-epidemic work of other countries in the severe environment where the epidemic is spreading rapidly.

Recent work has demonstrated the possibility of selectively sintering polymer powders with radio frequency (RF) radiation as a means of rapid, volumetric additive manufacturing. Although RF radiation can be used as a volumetric energy source, non-uniform heating resulting from the sample geometry and electrode configuration can lead to adverse effects in RF-treated samples. This paper aims to address these heating uniformity issues by implementing a computational design strategy for doped polymer powder beds to improve the RF heating uniformity.

Two approaches for improving the RF heating uniformity are presented with the goal of developing an RF-assisted additive manufacturing process. Both techniques use COMSOL Multiphysics® to predict the temperature rise during simulated RF exposure for different geometries. The effectiveness of each approach is evaluated by calculating the uniformity index, which provides an objective metric for comparing the heating uniformity between simulations. The first method implements an iterative heuristic tuning strategy to functionally grade the electrical conductivity within the sample. The second method involves reorienting the electrodes during the heating stage such that the electric field is applied in two directions.

Both approaches are shown to improve the heating uniformity and predicted part geometry for several test cases when applied independently. However, the greatest improvement in heating uniformity is demonstrated by combining the approaches and using multiple electrode orientations while functionally grading the samples.

This work presents an innovative approach for overcoming RF heating uniformity issues to improve the resulting part geometry in an RF-assisted, volumetric additive manufacturing method.

The purpose of this paper is to evaluate the efficiency of Algerian banks and examine the effects of explanatory factors on their performance.

In this paper, a methodology of two-stage network data envelopment analysis (DEA) is used to explore the efficiency of a sample of 13 Algerian banks during the 2013–2017 period. In the first stage, the network DEA is used to assess the overall and stages efficiencies. In the second stage, the partial least squares (PLS) regression is conducted to determine the potential effects of explanatory factors on stages efficiency.

The main empirical results indicate that Algerian banks need an efficiency improvement in both stages. The overall efficiency of the Algerian banking system improves over the study period. The deposit producing efficiency is positively affected by bank size and bank age. The revenue earning efficiency is negatively associated with bank size and bank age. The domestic banks are more efficient than foreign banks in the deposit producing stage and the foreign banks are more efficient than domestic banks in the revenue earning stage.

The results might be used as guidelines for both managers and policymakers in order to improve banks and banking system performance.

To the best of our knowledge, this study is the first that uses the DEA in investigating the efficiency of Algerian banks by dividing the overall efficiency into deposit producing and revenue earning efficiencies. Unlike most studies that have usually used OLS regression, Tobit regression and bootstrapped truncated regression, this study is the first in the bank efficiency literature that uses PLS regression to investigate the potential effect of explanatory variables on deposit producing and revenue earning efficiencies.

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.