Loading and unloading operations for Stock Keeping Units (SKUs) in warehouses are critical to logistics management systems. However, these operations also have a significant impact on the environment, particularly in terms of carbon dioxide (CO2) emissions. As such, warehouse management must consider not only operational efficiency but also environmental impact. The reduction of CO2 emissions in warehouses is becoming increasingly important, both for legal compliance and to meet sustainability targets. In this article, we will emphasize the environmental impact of warehouse operations, particularly on CO2 emissions, and explore ways to minimize them while still maximizing warehouse performance. We will review various optimization models proposed to address this issue and highlight the importance of considering environmental objectives when designing warehouse operations. We will also describe a simulation study conducted to determine the Pareto optimal frontier for a warehouse design, considering transportation, space utilization, and CO2 emissions. The outcomes of implementing this simulation's results include reduced CO2 emissions and increased space utilization, which demonstrate the potential benefits of considering environmental objectives in warehouse design and management.

In this research, three variance ratio tests: the standard variance ratio test, the wild bootstrap multiple variance ratio test, and the non-parametric rank scores test are adopted to test the random walk hypothesis (RWH) of stock markets in Middle East and North Africa (MENA) region using most recent data from January 2010 to September 2012. The empirical results obtained by all three econometric tests show that the RWH is strongly rejected for Kuwait, Tunisia, and Morocco. However, the standard variance ratio test and the wild bootstrap multiple variance ratio test reject the null hypothesis of random walk in Jordan and KSA, while non-parametric rank scores test do not. We may conclude that Jordan and KSA stock market are weak efficient. In sum, the empirical results suggest that return series in Kuwait, Tunisia, and Morocco are predictable. In other words, predictable patterns that can be exploited in these markets still exit. Therefore, investors may make profits in such less efficient markets.

Data Envelopment Analysis (DEA) is one of the most popular methods used for measuring the relative efficiency of similar units by considering various input/output parameters. This paper implements DEA models to estimate the relative efficiency of selected banks in the United States. The proposed study uses two inputs, total assets and number of employees, and one output, net revenue for measuring the relative efficiency of selected banks. The relative efficiencies of different banks are analyzed. The preliminary results indicate that Santander Bank is the most efficient banks operating in the United States followed by SunTrust Bank and HSBC. Other banks preserve lower efficiency compared with these three banks.

The purpose of this research is to study the role of the social media for knowledge sharing. The study presents a comprehensive review of the researches associated with the effect of knowledge management in social media. The study uses Scopus database as a primary search engine and covers 1858 of highly cited articles over the period 1994-2019. The records are statistically analyzed and categorized in terms of various criteria using an open source software package named R. The findings show that researches have grown exponentially during the recent years and the trend has continued at relatively stable rates. Based on the survey, knowledge management is the keyword which has carried the highest citations followed by social media and social networking. Among the most cited articles, papers published by researchers in United States have received the highest citations, followed by United Kingdom and China.

The aversion dynamics research agenda has incorporated within dispatching heuristics a number of real-world observations involving risk mitigation practices used by real schedulers. One such observation is that schedulers occasionally offload risky jobs from a primary machine to otherwise less desirable machine (older, slower) during periods of peak load to avoid the effects the risky job can have on subsequent jobs. This paper examines this situation within the proportional parallel machine environment. Safety time is used to adjust dispatching priorities of risky jobs to reflect the aversion. The effect of various safety time values on performance is studied. Robust safety time values and/or intervals are identified across a variety of experimental factors related to risk level, percent risky jobs in the job stream, and due date distribution.

The purpose of this research is to investigate the status and the evolution of the scientific studies for the effect of social networks on big data and usage of big data for modeling the social net-works users’ behavior. This paper presents a comprehensive review of the studies associated with big data in social media. The study uses Scopus database as a primary search engine and covers 2000 of highly cited articles over the period 2012-2019. The records are statistically analyzed and categorized in terms of different criteria. The findings show that researches have grown exponentially since 2014 and the trend has continued at relatively stable rates. Based on the survey, decision support systems is the keyword which has carried the highest densities followed by heuristics methods. Among the most cited articles, papers published by researchers in United States have received the highest citations (7548), followed by United Kingdom (588) and China with 543 citations. Thematic analysis shows that the subject nearly maintained an important and well-developed research field and for better results we can merge our research with “big data analytics” and “twitter” that are important topics in this field but not developed well.

The purpose of this research is to provide a conceptual overview on published studies and articles related to social media and information systems as two independent, continuous and interdepend-ent concepts. The research data is from the survey for two social media keywords and information systems from the Scopus site, which is one of the main scientific search engines. Due to a large number of findings, the study area was limited to 6 major areas of social sciences, business management, economics, arts and humanities, psychology and decision-making sciences, and 5185 articles were found. To investigate and analyze the findings, the Romethometrics and bibliometrics library of R Studio software were used. Biblimetrix was a method for studying and evaluating quantitative scientific texts using mathematical and statistical methods. The data obtained from this research is useful for scholars, researchers, decision-makers and those interested in social media and information networks in the political, economic, social and cultural spheres.

The purpose of this research is to investigate the status and the evolution of the scientific studies on the effect of social networks on e-commerce. The study seeks to address the status of a set of scientific productions of researchers in the world indexed in Scopus based on scientometrics indicators. In total, 1926 articles were found and the collected data were analyzed using quantitative and qualitative indicators of scientometrics with bibliometrix R software package. The findings show that researches have grown exponentially since 2009 and the trend has continued at relatively stable rates. Thematic analysis shows that the subject had a significant but not well-developed research field. There is a high rate of cooperation with a rich research network among institutions in United States, European and Asian countries. Studies also show that research interest in this area is prevalent in developed countries. In addition, the lack of funds and complex analytical tools may be due to lack of studies in developing countries, especially in Africa. The study of the global trend of research through scientometrics helps managers and researchers in identifying countries and institutions with the greatest potential for scientific production, which allows them to develop their professions.

Business trades among individuals, companies, firms, enterprises, and governments are rapidly expelled from their traditional mode, which were mainly based on paper-based exchange, and are geared towards trading through information-based systems. E-commerce has opened up a new field in terms of speed, efficiency, cost savings, and the exploitation of fleeting opportunities. This paper evaluates different authentication methods for online banking in Parsian Bank, which is one of the biggest private banks in Iran. The study designs a questionnaire and distributes it among some experts to find cause and effect factors based on a multi-criteria decision making technique named Decision Making Trial And Evaluation Laboratory (DEMATEL). The study evaluates 12 criteria and divides them into two groups of cause and effect. “Use of mobile systems in authenti-cation methods”, “Authentication with fingerprint and smart card”, “Authentication with bank card and password”, and “Authentication with digital signatures and passwords” are considered as the causative group, while “Authentication with a bank card”, “Biometric authentication and disposable codes” and “Authentication with digital signature” are among the effective group.

The trend toward sustainable city development is associated with intelligent transportation systems (ITS). Automation, efficiency, safety, security, and cost-effectiveness are critical factors in establishing each aspect of a smart city. Real-time data obtained from ITS play an essential role in improving the level of service of road segments, enhancing road safety, and supporting road users with road circumstances information. Travel time information is applicable in travel time maps, decision makings for traffic congestion, dynamic pricing of the network, emergency relief services, traffic flow monitoring, traffic jams management, and air quality analysis. Travel time on a road segment highly depends on geometrical specifications, environmental and weather conditions, traffic flow, and driving behavior. Due to specific driving behavior and road conditions, the above parameters are not essentially applicable in another region. The present research uses the data collected from loop detectors and License Plate Recognition (LPR) systems to develop a Bureau of Public Roads (BPR) model for Iran’s freeway network (Tehran-Qom Freeway). Because of the large amount of data, the SQL server program was used for creating and organizing the database and the BPR model was calibrated using SPSS statistical software. The results of the BPR model were evaluated with an ANOVA test, indicating that the derived model can estimate the travel time at freeway sections with a %5.2 error for the volume-to-capacity ratio (V/C) of less than 0.8.