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The motivation behind this research refers to the significant role of integration of production-distribution plans in effective performance of supply chain networks under fierce competition of today’s global marketplace. In this regard, this paper aims to deal with an integrated production-distribution planning problem in deterministic, multi-product and multi-echelon supply chain network. The bi-objective mixed-integer linear programming model is constructed to minimize not only the total transportation costs but also the total delivery time of supply chain, subject to satisfying retailer demands and capacity constraints where quantity discount on transportation costs, fixed cost associated with transportation vehicles usage and routing decisions have been included in the model.

As the proposed mathematical model is NP-hard and that finding an optimum solution in polynomial time is not reasonable, two multi-objective meta-heuristic algorithms, namely, non-dominated sorting genetic algorithm II (NSGAII) and multi-objective imperialist competitive algorithm (MOICA) are designed to obtain near optimal solutions for real-sized problems in reasonable computational times. The Taguchi method is then used to adjust the parameters of the developed algorithms. Finally, the applicability of the proposed model and the performance of the solution methodologies in comparison with each other are demonstrated for a set of randomly generated problem instances.

The practicality and applicability of the proposed model and the efficiency and efficacy of the developed solution methodologies were illustrated through a set of randomly generated real-sized problem instances. Result. In terms of two measures, the objective function value and the computational time were required to get solutions.

The main contribution of the present work was addressing an integrated production-distribution planning problem in a broader view, by proposing a closer to reality mathematical formulation which considers some real-world constraints simultaneously and accompanied by efficient multi-objective meta-heuristic algorithms to provide effective solutions for practical problem sizes.

Today’s, supply chain production and distribution of products to improve the customer satisfaction in the shortest possible time by paying the minimum cost, has become the most important challenge in global market. On the other hand, minimizing the total cost of the transportation and distribution is one of the critical items for companies. To handle this challenge, this paper aims to present a multi-objective multi-facility model of green closed-loop supply chain (GCLSC) under uncertain environment. In this model, the proposed GCLSC considers three classes in case of the leading chain and three classes in terms of the recursive chain. The objectives are to maximize the total profit of the GCLSC, satisfaction of demand, the satisfactions of the customers and getting to the proper cost of the consumers, distribution centers and recursive centers.

Then, this model is designed by considering several products under several periods regarding the recovery possibility of products. Finally, to evaluate the proposed model, several numerical examples are randomly designed and then solved using non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. Then, they are ranked by TOPSIS along with analytical hierarchy process so-called analytic hierarchy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS).

The results indicated that non-dominated ranked genetic algorithm (NRGA) algorithm outperforms non-dominated sorting genetic algorithm (NSGA-II) algorithm in terms of computation times. However, in other metrics, any significant difference was not seen. At the end, to rank the algorithms, a multi-criterion decision technique was used. The obtained results of this method indicated that NSGA-II had better performance than ones obtained by NRGA.

This study is motivated by the need of integrating the leading supply chain and retrogressive supply chain. In short, the highlights of the differences of this research with the mentioned studies are as follows: developing multi-objective multi-facility model of fuzzy GCLSC under uncertain environment and integrating the leading supply chain and retrogressive supply chain.

Poor health-related quality of life (HRQoL) is one of the important issues in the health sector. The purpose of this paper is to investigate the prevalence and socio-economic inequality in poor HRQoL in Tehran city, Iran.

In total, 562 adults were included in this cross-sectional study. The cluster sampling method was used for data collection from May to June, 2016 in Tehran city, Iran. Data on HRQoL, using EuroQol 5-dimensions questionnaire, and data on socio-economic and demographic variables were gathered. Convenience regression method was performed to measure the concentration index (CI). Decomposition analysis was performed to determine the contribution of variables on socio-economic inequality in poor HRQoL. All analyses were performed by Stata v.14.

The prevalence of poor HRQoL was 28.3 percent. The value of CI for “poor HRQoL” was −0.299 (95% confidence interval: −0.402 to −0.195). Socio-economic status (SES) was the largest contributor to socio-economic inequality in poor HRQoL (69.44 percent of inequality was explained by SES). Age, obesity and race had a positive contribution to socio-economic inequality in poor HRQoL among the participants. Nonetheless, sex and smoking intensity had a negative contribution to inequality in poor HRQoL.

There is little evidence about the prevalence of poor HRQoL in insured people. This study provided new evidence in this area through the investigation of socio-economic inequality in poor HRQoL and its determinants among people with health insurance in Iran using decomposition analysis.

Most educational institutions worldwide have shifted to online teaching and learning approaches to mitigate risks imposed by the COVID-19 pandemic. This causes several issues, particularly in delivering the construction management (CM) courses which require site visits, interpreting technical drawings and developing 3D building models. This paper aims to identify the key strategies for online learning and teaching adopted during the COVID-19 pandemic and to investigate the implications for construction management education.

The research approach is twofold. First, the study presents a systematic literature review (SLR) through a synthesis of the existing literature to identify the key strategies and lessons learned about online education during the COVID-19 pandemic in tertiary programs. It also discusses their implications in the context of the construction management (CM) sector in particular. Secondly, the authors shared their hands-on experience as construction management course facilitators – using the autoethnography approach – during the COVID-19 crisis.

In addition to identifying the key strategies such as online course delivery and assessments, the paper critically discusses the barriers to online learning and teaching, including (1) the technological and infrastructure barriers; (2) required online teaching skills and competencies; (3) issues surrounding mental health and wellbeing; (4) lack of consistency in the online delivery of various courses in a given program (5) difficulties around students' engagement and (6) the course characteristics and requirements.

The study offers some implications and recommendations not only for educational institutions and staff but also for vendors of online course delivery software. To prepare educational institutions for future online course delivery, the paper proposes several strategies. These include developing a set of guidelines for online course delivery, incorporating online teaching training modules into the recruitment process for academic staff, applying agile and resilience teaching and learning methods, wellbeing and mental health support and continuously improving course features to adapt to the online environment.

Neuromarketing is a science that can examine human subconscious decisions and activities. The study aimed to investigate the neuropsychological responses of consumers to promotion strategies and the decision to buy sports products, in order to determine the more effective strategy.

An experimental design clarifies whether there is a significant difference between the neuropsychological responses resulting from selective promotional strategies (charity, endorsement, advertising and discount). The authors conducted the research with 40 young adults (20 males and 20 females; age range 25–35 years). The Enobio 20-channel electroencephalograph (EEG) is used to record waves, a two-item questionnaire is used to measure purchasing decisions and self-assessment forms are used to measure arousal and pleasure. Data analysis performed by Friedman's statistical methods and logistic regression using SPSS 22 software.

Based on the alpha wave, the results showed the two charity and endorsement strategies had the highest effect on consumer attentions. The pleasure had the highest value in the discount, and the arousal had the highest value in the charity strategy. Neuropsychological responses also explain a significant percentage of the consumer decision to buy.

Access to research samples is difficult due to the ignorance of the samples as well as the fear of possible harm from imaging and brain scanning methods.

Neuromarketing is a science that can examine human subconscious decisions and activities.

The information obtained neural methods are more accurate than traditional research methods.

This study showed the alpha brainwave (attention), arousal and pleasure explain a significant part of the consumer decision to buy. More scientific data can be obtained through new scientific approaches such as neuromarketing, which has a great impact on understanding consumer behavior. Therefore, marketers and researchers can make their promotional activities more effective in terms of them.

Energy has a strategic role in the social and economic development of countries. Affordability, accessibility and availability of energy sources are the priorities of the governments in energy supply. Therefore, understanding the robustness of energy supply is an important subject of energy researchers and policymakers. This paper aims to analyze the robustness of the electricity system at the national level.

First, the strengths, weaknesses, opportunities and threats analysis is implemented for a selected case study. Then, the expert panel weighed the parameters’ effect on sustainable power generation, the survey is quantified using fuzzy logic. Finally, cross functional analysis is applied to evaluate the influence/dependence of the parameters.

The results show three determinant parameters which have the most influence on the system: fluctuations in oil prices, governmental acts and sanctions against the country. The most dependent parameters, as objectives variables, are the share of renewables and distributed generation (DG), system reliability, power generation diversity and transmission efficiency.

Using future studies methods in the energy level at the nation level has been done for the first time in the current work.