During the past few years, dynamic capability (DC) has been considered as an important issue in banking industry. This paper presents a survey on dynamic capability and its role on reaching sustainable competitive advantage (SCA) within Mellat bank of Iran (MBI). A valid research instrument is utilized to conduct a survey among 150 managers from MBI. The study utilizes structural equation modelling to examine different hypotheses based on an integrated model of DC and SCA. According to literature studies, expert opinions and exploratory factor analysis, DC is classified into sensing, learning, reconfiguration, and coordination. Furthermore, SCA of the banking industry is classified into three dimensions: market, customer, and financial performance. The results indicate that DC had the greatest effect on the market centered, while it had the least influence on the customer centered.

Flow of information in supply chain is as prominent as material and financial flows and among these three aspects of a supply chain, information flow could be of great importance since it provides a basis for a steady flow of goods and finance as well. To help managers control the flow of information in an effective way, first, the parameters which affect the flow of information should be determined. Then, if the parameters can be controlled carefully, the information will be shared correctly and in a timely manner among supply chain members. This paper identifies the influencing parameters on proper flow of information in supply chain and provides a list of parameters based on the literature as well as the industrial and academic experts’ opinions. Afterwards, in order to define the degree of importance for each parameter from the experts’ perspective, fuzzy TOPSIS method is employed and the parameters are ranked based on three criteria, namely “measurability”, “being illustrative” and “parameters relevancy” to the issue of information flow. The research findings show that “Supply Chain Hardware Capabilities”, “Supply Chain Network Infrastructure”, “Information Software Capabilities”, “Information Sharing Timeliness”, “Information Recency” and “Organizational Rewards” received the highest priorities, while “Power of Internal and Inter-personal Communications”, “Users’ Trust” and “Users’ Tendency” were standing at the bottom of this ranking. The results of this research could be employed as an input for strategy development process for supply chain information management activities. Thus, the awareness of each parameter’s importance in proper flow of information, helps us make appropriate strategies to improve information management in the supply chain.

Investigating the Customer Satisfaction Measurement (CSM) plays an important role in determining the range of customer needs and expectations resulting from delivered products or received services. In this research, a novel approach is proposed for measuring the customer’s satisfaction measurement. Due to ambiguity and lack of information related to evaluation criteria, in the proposed model, the customer feedbacks are considered as linguistic terms and due to the dominance of non –linear relations on behaviors and judgments of human, the result is obtained using a Fuzzy Neural Network. In continuation, roles of the fuzzy inference system for customer’s satisfaction are defined and determined for different conditions of customer’s judgments. Applicability of the proposed model has been successfully implemented through a case study for investigating the customer’s satisfaction on the basis of both qualitative and quantitative inputs.

This paper considers the capacity determination in a closed-loop supply chain network when a queueing system is established in the reverse flow. Since the queueing system imposes costs on the model, the decision maker faces the challenge of determining the capacity of facilities in such a way that a compromise between the queueing costs and the fixed costs of opening new facilities could be obtained. We develop a De Novo programming approach to determine the capacity of recovery facilities in the reverse flow. To this aim, a mixed integer nonlinear programming (MINLP) model is integrated with the De Novo programming and the robust counterpart of this model is proposed to cope with the uncertainty of the parameters. To solve the model, an interactive fuzzy programming approach is combined with the hard worst case robust programming. Numerical results show the performance of the developed model in determining the capacity of facilities.

In today’s dynamic business environment, the product mix and demand may vary from period to period. Therefore, the same configuration of cellular systems will not be optimal in different periods. In this paper, a new mixed integer non-linear mathematical programming model is presented to design dynamic cellular manufacturing systems and combines several design features including multi-period production planning, alternate routings, system reconfiguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material flow between machines. The required capacity for parts production is modeled based on flow shop perspective and the aim of mixed integer model is to find optimal independent cells, the quantity of machine types in each cell, and production quantity of the parts during each period of the time horizon. Since this problem belongs to NP-hard class, a three-phase approach is developed to solve the model for practical purposes. Phase 1 finds a feasible solution, phase 2 finds the neighbor solutions, and phase 3 improves a feasible solution. To analyze the computational efficiency, eight test problems with different sizes are considered and the optimal and near-optimal solutions are compared. The efficiency of the algorithm in terms of the objective function values and computational times is shown by the obtained results.

Vertex and p-center problems are two well-known types of the center problem. In this paper, a p-center problem with uncertain demand-weighted distance will be introduced in which the demands are considered as fuzzy random variables (FRVs) and the objective of the problem is to minimize the maximum distance between a node and its nearest facility. Then, by introducing new methods, the proposed problem is converted to deterministic integer programming (IP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy random chance-constrained programming (FRCCP). Finally, the proposed methods are applied for locating bicycle stations in the city of Tabriz in Iran as a real case study. The computational results of our study show that these methods can be implemented for the center problem with uncertain frameworks.

Bidding price decision is a key issue for the contractors and construction companies. The success/failure of the contractors in competitive biddings is directly dependent on their bidding strategy. This paper aims to develop a hybrid statistical and mathematical modeling approach for determining the optimum bidding price in construction projects. By statistical analysis of historical data, some uncertain parameters like the number of competitors and the cost of the project are estimated. Then, a scenario-based mathematical model for bidding price decision is proposed. In order to present a model in more accordance with the real-world situations, factors like risk, minimum acceptable rate of return (MARR) and opportunistic behavior are taken into account. In order to achieve an insensitive solution to the change in the realization of the input data from the scenarios, a robust mathematical model is used. The performance of the model is evaluated through some numerical problems. Furthermore, sensitivity analysis of the key parameters and robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model's effectiveness in real-world situations, a case study is analyzed by the proposed approach. Numerical results show that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model in a real-world situation.

Modeling the simple assembly line balancing (SALB) problem has covered a wide range of real-world applications. The recent advances in optimization problems have created the opportunities to tackle more challenging problems. This paper presents a multi-objective decision making problem to consider two objectives, cost and cycle time, for simple assembly line balancing. The problem is formulated as a mixed integer nonlinear optimization and the proposed study of this paper uses two metaheuristics to solve the resulted problem on some benchmark problems. The preliminary results have indicated that multi objective particle swarm optimization (MOPSO) has provided better quality solutions while the hybrid method based on MOPSO and simulated annealing has yielded more non-dominated Pareto solutions.

The generalized exponential distribution could be a good option to analyse lifetime data, as an alternative for the use of standard existing lifetime distributions as exponential, Weibull or gamma distributions. Assuming different non-informative prior distributions for the parameters of the model, we introduce a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Some numerical illustrations considering simulated and real lifetime data are presented to illustrate the proposed methodology, especially the effects of different priors on the posterior summaries of interest.

The interphase is a region between fibers and a matrix, which has different properties from the matrix and the fibers, but is dependent on them. Considering the interphase region has a significant effect on the accuracy of obtained residual stresses. So far, in order to obtain the micromechanical residual stresses, the interphase properties are considered as an average. In this paper, the properties of the interphase are assumed variable by using a suitable UMAT code in the ABAQUS software. The equations of previous studies that have acquired interphase properties to be variable are used to write the UMAT code. A representative volume element (RVE) in polymer composites is modeled in three phases in the ABAQUS software and the interphase properties are considered as FGM by using the UMAT code. Temperature variation during curing to environment temperature is the only loading factor in the RVE. The matrix, fiber and interphase stresses are obtained in the ABAQUS software. The achieved stresses were compared with the results of previous studies that considered interphase properties as average. Finite element and energy methods were used in previous papers but in the present study just the finite element method with variable interphase properties was use. In addition, residual stress diagrams with the variable interphase properties are plotted to study the effect of the thermal expansion coefficient. The results of this study are similar to those in previous ones, and the curves are improved.