The purpose of this paper is to propose an integrated multiple criteria decision making (MCDM) approach to analyze interrelationships among sustainability criteria, weight them, and then evaluate suppliers according to these criteria. Data were collected through interviewing with seven experts who are involved in the procurement department of the case. Interpretive structural modelling (ISM), and pairwise comparison questionnaire are used to elicit the existence, strength of relationships among criteria and then weight them by using Fuzzy (Decision Making Trial and Evaluation Laboratory (FDEMATEL), Fuzzy Preference Programming (FPP), and Analytical Network Process (ANP). Then, Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is employed to evaluate suppliers based on identified and analyzed sustainability criteria. This study provides several implications for practitioners and scholars. First, the results show the efficiency of the proposed approach in practice. Second, respondents state that the proposed approach was very useful in decision making based on interrelationships among criteria, alternatives and policies. Third, findings validate the significant difference in rankings with or without considering interdependencies among criteria.

The theory of constraints is an approach for production planning and control, which emphasizes on the constraints in the system to increase throughput. The theory of constraints is often referred to as Drum-Buffer-Rope developed originally by Goldratt. Drum-Buffer-Rope uses the drum or constraint to create a schedule based on the finite capacity of the first bottleneck. Because of complexity of the job shop environment, Drum-Buffer-Rope material flow management has very little attention to job shop environment. The objective of this paper is to apply the Drum-Buffer-Rope technique in the job shop environment using a Markov chain analysis to compare traditional method with Drum-Buffer-Rope. Four measurement parameters were considered and the result showed the advantage of Drum-Buffer-Rope approach compared with traditional one.

Synthesis of pyrano-chromenes and pyrano-pyrans was developed by three-component reactions of kojic acid and aromatic aldehydes with dimethone and malononitrile, catalyzed with nano-Bi2O3-ZnO and nano-ZnO, respectively. Reactions proceeded smoothly and the corresponding heterocyclic products were obtained in good to high yields. Nano ZnO and nano Bi2O3-ZnO were prepared by sol-gel method and characterized by X-ray diffraction (XRD), energy-dispersive X-ray analysis (EDX), Fourier transform infrared (FT-IR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) techniques. Supporting Bi3+ on ZnO nanoparticles as Bi2O3, is the main novelty of this work. The simple reaction procedure, easy separation of products, low catalyst loading, reusability of the catalyst are some advantageous of this protocol.

An efficient route, convenient and environmentally friendly procedure for the synthesis chromenes derivatives have been developed via a three-component coupling and one-pot reactions of various aromatic aldehyde with malononitrile or ethyl cyanoacetate and phenols in the presence N,N-dimethylpyridin-4-amine (DMAP) in reflux conditions. In simple reaction conditions, the use of DMAP is explored as an easy workup and a green catalyst for the one-pot three-component synthesis ethyl 3-amino-1-aryl-1H-benzo[f]chromene-2-carboxylate derivatives.

The characterization of project critical success factors is a multidimensional construction that relies upon numerous components. This investigation analyzes the various criteria and components that impact the accomplishment of the tasks in the human resource region of the development project. This examination utilizes a comparative method, the analytic network process method is used to recognize the significant weight of models lastly, the positioning is finished by the Technique for Order of Preference by Similarity to Ideal Solution technique, to analyze 27 critical success factors in the human resource management in project, and thinks about a wide scope of progress rules and basic achievement factors. The outcomes and assessment of specialists illustrate, select increasing trust and certainty among employees and project team managers, and it has the most effect on the accomplishment of Human Resource Management in projects. Contributing to these factors will improve the effectiveness of the activities.

One of the most critical factors used to evaluate the efficiency of the portfolio selection process is the ability of the model to measure interdependencies among projects. Varieties of interactions among projects lead to several kinds of synergies in the whole portfolio, such as re-sources and knowledge interdependencies. There are few studies focused on project portfolio selection accompanied by modeling and estimating the impact of synergies between projects. Hence, this paper presents a model to select the best project portfolio applying a particular model to measure the effects of several types of interdependencies between paired projects. Then, the Promethee II method is used to prioritize projects. Then, the portfolio selection model, which is a non-linear integer model, is solved to find the best set of projects. Finally, numerical examples are addressed to illustrate the method results and validity.

Online social networks (OSNs) provide services targeting multifarious types of users in order to attract and retain them. For this purpose, developing new services according to user preferences has recently been under focused by various researchers. Most of present studies focus only on extracting the behavioral patterns of users, and neglect users’ interactions, which is the main part of the social activities in OSNs. To cope with this issue, this paper proposes a new methodology to bring both dimensions of data, the extracted behavioral patterns of users and their social interactions, in order to reach a better analysis. Moreover, the idea provides a basis for considering other dimensions efficiently. In order to evaluate the performance of the methodology, this paper performs a case study, and conducts a set of experiments on the computer-generated datasets. The results indicates the great performance of the methodology.

n this paper, armchair rectangular monolayer graphene nanoplates were analyzed based on the assumption of orthotropic properties with different boundary conditions. For this purpose, the nonlocal elasticity and Kirchhoff theories were applied to analyze the small-scale effects on the armchair monolayer graphene nanoplates and obtain the static equation of the graphene nanoplates, respectively. As there were no readily accurate answers to the analyses of all the problems associated with nanoplates of different boundary and geometrical conditions, numerical methods were employed for obtaining approximate responses. One of the capable approaches to the analyses of the differential equations governing these types of plates is the meshless method. In this research, the Discrete Least Squares Meshless (DLSM) method was employed for the static analysis of rectangular nanoplates with different boundary conditions besides assessing the effect of nonlocal coefficient amount on the nanoplate deflection. According to the results of this investigation, it could be concluded that the method utilized here was suitable for solving the problems of nano-dimensions compared to the analytical and Galerkin meshless methods.

This paper considers a portfolio selection problem with normally distributed returns and different rates for borrowing and lending. The primary concern is to determine the amount of investment in different planning horizons when the rate of borrowing is greater than the rate of lending. Chance constrained programming as an appropriate tool for addressing intrinsic uncertainty in portfolio selection problem is used. To solve this nonlinear programming, Genetic Algorithm is utilized. Numerical experiments are performed and the results are analyzed to present the performance of the proposed methodology.

Increasing in attentions to the environment, city legislative and social problems make companies change their prospects towards supply chain management and design sustainable transportation networks. In this paper, two-stage problem have been investigated in which the transportation stage is considered before Location-Routing Problem, so we call it Transportation-Location-Routing Problem (TLRP). It is an extension of the two-echelon Location-Routing Problem. In the first stage, there is a transportation problem with truck capacity limitation. Furthermore, customers’ time windows should be met in the second stage to make the mode more realistic. Minimization of distribution cost, fuel consumption, and carbon dioxide emission along with balancing the workloads for city drivers are considered as the objective functions of the mathematical model to design a sustainable distribution network. To tackle these conflicting objectives, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are applied to solve the problem. A new customized chromosome based on a priority based technique is presented for the problem. Due to the three comparison metrics for multi-objective problems, with tolerating a little more computational time, MOPSO has the better performance in this problem than NSGA-II.