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Spurred by the high turnover in the pharmaceutical industry, locating pharmacies inside urban areas along with the high product perishability in this industry, the pharmaceutical supply chain management has recently gained increasing attention. Accordingly, this paper unveils an inventory-routing problem for designing a pharmaceutical supply chain with perishable products and time-dependent travel time in an uncertain environment.

In this study, mathematical programming is employed to formulate a multi-graph network affected by the traffic volume in order to adapt to real-world situations. Likewise, by transforming the travel speed function to the travel time function using a step-by-step algorithm, the first-in-first-out property is warranted. Moreover, the Box–Jenkins forecasting method is employed to diminish the demand uncertainty.

An appealing result is that the delivery horizon constraint in the under-study multi-graph network may eventuate in selecting a longer path. Our analysis also indicates that the customers located in the busy places in the city are not predominantly visited in the initial and last delivery horizon, which are the rush times. Moreover, it is concluded that integrating disruption management, routing planning and inventory management in the studied network leads to a reduction of costs in the long term.

Applying the time-dependent travel time with a heterogeneous fleet of vehicles on the multi-graph network, considering perishability in the products for reducing inventory costs, considering multiple trips of transfer fleet, considering disruption impacts on supply chain components and utilizing the Box–Jenkins method to reduce uncertainty are the contributions of the present study.

The aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.

Two mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.

The results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.

Although integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.

Blasting is an economical method for rock breakage in open-pit mines. Backbreak is an undesirable phenomenon induced by blasting operations and has several unsuitable effects such as equipment instability and decreased performance of the blasting. Therefore, accurate estimation of backbreak is required for minimizing the environmental problems. The primary purpose of this paper is to propose a novel predictive model for estimating the backbreak at Shur River Dam region, Iran, using particle swarm optimization (PSO).

For this work, a total of 84 blasting events were considered and five effective factors on backbreak including spacing, burden, stemming, rock mass rating and specific charge were measured. To evaluate the accuracy of the proposed PSO model, multiple regression (MR) model was also developed, and the results of two predictive models were compared with actual field data.

Based on two statistical metrics [i.e. coefficient of determination (R²) and root mean square error (RMSE)], it was found that the proposed PSO model (with R² = 0.960 and RMSE = 0.08) can predict backbreak better than MR (with R² = 0.873 and RMSE = 0.14).

The analysis indicated that the specific charge is the most effective parameter on backbreak among all independent parameters used in this study.

This study aims to investigate the impact of outside financing (equity and debt financing) on the quality of financial reporting in Iran.

Sample includes the companies listed on the Tehran Stock Exchange – 152 companies in a period of four years during 2010-2013. Data were analyzed by using multiple linear regressions with the benefits of the combined data.

The results indicates that there is a positive relationship between the quality of financial reporting based on the qualitative characteristics of the theoretical principles of the Iranian Financial Accounting Standards Board and debt financing. Moreover, there is a negative relationship between the quality of financial reporting based on the Dechow and Dichev (2002) model and debt financing. Additionally, there is a negative relationship between the quality of financial reporting (based on the qualitative characteristics of the theoretical principles of the Iranian Financial Accounting Standards Board as well as the Dechow and Dichev models) and equity financing.

Financial statements as the output of the accounting system has always been considered by the investors, the creditors and the government; nonetheless, its dependability in making decisions has always been doubted because of using the accrual principle in the calculation of the reported figure in the statements and, consequently, the possibility of being manipulated by the managers as well as the likelihood of conflict of interest among the managers and the shareholders.

This study aims to examine the role of organisational democracy (OD) in facilitating the knowledge sharing (KS) process within companies, thus considering the effect of different OD principles.

The authors used data collected through a questionnaire on a sample of 254 employees at private universities and colleges to test the relationship between OD and KS. Data were analysed using the structural equation modelling technique.

Overall, OD has a direct and significant effect on facilitating KS in organisations. Also, the results showed that there are different degrees and intensities among the individual principles (sub-concepts) of OD and KS.

The findings highlight the important role of democracy in an organisation to enhance the organisational climate and employees’ behaviours, thus leading to higher KS outcomes. Also, results, provide an opportunity for managers to consider enhancing democracy in an organisation for improving internal collaboration effectiveness in KS.

This paper sheds light and adds new knowledge to embryonic studies that are directed towards the integration of democracy within the main concept of knowledge management (KM). This emphasises the need to use and stimulate OD and its principles for improving the effectiveness of KM practices with specific attention to KS.

The current study aims to identify and classify the financial resilience measurement indices using the intuitive fuzzy approach.

The present study aims to identify and classify firms' indices of financial resilience measurement using the Fuzzy–Delphi combined method and the intuitive fuzzy DEMATEL technique with interval values. For the study and the literature review, 29 financial resilience indices were identified, and 12 were finalised after screening and localisation. Next, the selected indices were classified into two groups of influencing and being influenced, and the significant range of each one was determined. Finally, the executive and research suggestions were presented based on the obtained results.

The study results indicate a higher significance level of redundancy and visibility in financial resilience.

The present study is the pioneer study to assess, identify and classify the contributing indices to financial resilience.

Despite the undoubted role of knowledge in the small family business (FB) in the restaurant and fast-food industry, there are some main challenges in the knowledge-sharing (KS) orientation for promoting their business. This study tries to recognise and explore the drivers influencing these enterprises' KS processes.

The authors applied combined exploratory and confirmatory analysis in two studies. In the first study, by the Delphi method, the 22 experts consisting of managers, FB owners and academic professors explore and forecast significant items and drivers. The second study is a confirmatory factor analysis (CFA) for validating the first study's indicators using the survey collected amongst 218 restaurants and fast foods employees.

The findings indicate 23 indicators in three main drivers of individual, organisational and technological, are critical for KS in small FBs in the investigated industry.

This research supports the understanding of knowledge management and the FB and contributes to recognising the influential factors for KS amongst small and medium-sized family businesses in the food-related industry. Also, by identifying and ranking the most significant factors, this research will help entrepreneurs facilitate FB entrepreneurship. Finally, the results provide practical implications for current and future KM and FB decision-makers.

Appropriate logistic planning for the pharmaceutical supply chain can significantly improve many financial and performance aspects. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of pharmaceuticals, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified requests.

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized requests for pharmaceuticals in the network. Besides, the total transportation activities of different types of vehicles and related costs are considered as other objectives. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize the second objective function while maintaining the optimality of the first objective function. The third objective function is optimized based on the optimality of other objective functions, as well. A non-dominated sorting genetic algorithm II-multi-objective particle swarm optimization heuristic method is designed for this aim.

The performances of the proposed model were analyzed in different cases and its results for different problems were shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

The proposed methodology can be applied to find the best logistic plan in real situations.

In this paper, the authors have tried to use a multi-objective optimization model to guide and correct the pharmaceutical supply chain to deal with the related requests. This is important because it can help managers to improve their plans.

This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (q_ult) of rock foundations, which is a considerable civil and geotechnical engineering problem.

The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of q_ult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.

A new and explicit formula generated by GEP is proposed for the estimation of the q_ult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.

The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

The purpose of this study is to analyze a new idea for external flow over a cylinder to increase the heat transfer and reduce pressure drop. Using wedge-shaped porous media in the front and wake regions of the cylinder can improve its hydrodynamic, and the rotating flow in the wake region can enhance the heat transfer with increased porous–liquid contact. Permeability plays a vital role, as a high-permeable medium improves heat transfer, whereas a low-permeable region improves the hydrodynamic.

Therefore, in the current research, external forced convection of nanofluid laminar flow over a bundle of cylinders is simulated using a two-phase mixture model. Four cases with different porous blocks around the cylinder are assessed: rectangular porous; wedge shape in trailing edge (TEP); wedge shape in leading and trailing edges (LTEP); and no porous block case. Also, three different lengths of wedge-shaped regions are considered for TEP and LTEP cases.

Results are presented in terms of Nusselt (Nu), Euler (Eu) and the performance evaluation criterion (PEC) numbers for various Reynolds (Re) and Darcy (Da) numbers.

It was found that in most situations, LTEP case provides the highest Nu and PEC values. Also, optimal Re and porous medium length exist to maximize PEC, depending on the values of Da and nanofluid volume fraction.